NAMIC Wiki - User contributions [en]

Special:Badtitle/NS100:Driving Biological Projects

2007-09-26T21:40:47Z

Shenton:

[[Image:Big-DBP-Logo.png|right]]

='''Driving Biological Projects'''=
The overall goal of NA-MIC is to create, to develop, to deploy, and to train others in the use of computational tools for the quantitative analysis and visualization of medical imaging data. Our ultimate goal is to develop tool elements that can be reused and linked into any flexible fabric that links operations important to refining, to analyzing, and to presenting biomedical image data. To achieve these goals, Driving Biological Projects (DBPs) were used to focus the technical development of such tools. Initially (2004-2007) the focus of the DBPs was on schizophrenia. From 2007 to 2010, we will expand our efforts into other diseases including lupus erythematodes, autism, velocardiofacial syndrome (VCSF), and prostate cancer.

==''' 2007-2010''' ==
[[Image:MRI Robot System Diagram.png|thumb|right|300px|System design for [http://wiki.na-mic.org/Wiki/index.php/Special_topic_breakout:_IGT_for_Prostate#JHU prostate procedures].]]
From the beginning of the NCBC project, NIH planned for a three year cycle for the DBPs. In accordance with this policy, starting with the 4th year of NA-MIC, the DBPs were shifted from schizophrenia to lupus, autism, velocardiofacial syndrome (VCSF), and prostate cancer.
# The Analysis of Brain Lesions in Neuropsychiatric Systemic Lupus Erythematosis: This DBP is led by Drs. Jeremy Bockholt and Charles Gasparovi at the MIND Institute and the University of New Mexico and details for this DBP are available [http://wiki.na-mic.org/Wiki/index.php/DBP2:MIND here].
# Longitudinal MRI study of early brain development in neuropsychiatric disorder: UNC Autism Study: This DBP is led by Drs. Heather Hazlett and Joseph Piven at University of North Carolina, Chapel Hill and details for this DBP are available [http://wiki.na-mic.org/Wiki/index.php/DBP2:UNC here].
# Velocardiofacial Syndrome (VCFS) as a genetic model for schizophrenia: This effort is led by Dr. Marek Kubicki at Harvard Medical School and details for this DBP are available [http://wiki.na-mic.org/Wiki/index.php/DBP2:Harvard here].
# Segmentation and Registration Tools for Robotic Prostate Interventions: This effort is led by Dr. Gabor Fichtinger at Queens University and details for this DBP are available [http://wiki.na-mic.org/Wiki/index.php/DBP2:JHU here].

==''' 2004-2007''' ==
[[Image:Significance testing.jpg|thumb|left|300px|Comparison of [[Special:PubDB_View?dspaceid=471|results of shape analysis]] of the Caudate]]
Schizophrenia was a particularly appropriate choice for an initial DBP because it is a disorder that has been the focus of many neuroimaging studies, including collaborations with computer scientists. All of these efforts have led to important advances in the field. New developments in neuroimaging promise to take our understanding even further, to a more complete understanding of the neural circuits that are disrupted in schizophrenia, as well as a more complete understanding of the relationship between brain abnormalities, cognitive functioning, and genetic influences. During the first three years of NA-MIC, Core 3 consisted of four DBPs which were grouped into two thrusts.
# Thrust 1 was directed by Drs. Shenton ([http://www.na-mic.org/Wiki/index.php/DBP:Harvard DBP1]) and Saykin ([http://www.na-mic.org/Wiki/index.php/DBP:Dartmouth DBP2]). The focus of this thrust was to utilize neuroimaging tools to evaluate fronto-temporal connectivity abnormalities in schizophrenia, as well as abnormalities in hemispheric connections (i.e., corpus callosum), and abnormalities in the anterior limb of the internal capsule. Improved segmentation techniques, coregistration of structural MRI, DTI-MR, and fMRI, as well as novel processing tools for evaluating white matter fiber tracts and interregional functional connectivity were needed to accomplish these goals, and they were developed in conjunction with Cores 1 and 2. Findings from this project, which involve both structural and functional information about brain abnormalities in schizophrenia, were correlated with neurocognitive, clinical, and behavioral data in order to understand further the relationship between brain abnormalities and cognition/behavior in schizophrenia. Common imaging, cognitive, and clinical measures were used across both sites (DBP1 and DBP2). (See Publication Pages on NA-MIC for specific details regarding findings.)
# Thrust 2 was directed by Drs. Steven Potkin ([http://www.na-mic.org/Wiki/index.php/DBP:Irvine DBP3]) and James Kennedy ([http://www.na-mic.org/Wiki/index.php/DBP:Toronto DBP4]). The main thrust of this core was to utilize improved segmentation, co-registration, statistics, and circuitry analysis tools to evaluate abnormal brain networks in schizophrenia. The dorsal prefrontal cortex and its associated local and distal connections are viewed as key to understanding schizophrenia. Abnormalities in dorsal prefontal cortex structure and function, either primary or secondary to its many connected regions, as revealed by MRI, are viewed as explaining various characteristics of the illness. It is well documented that schizophrenia is heritable; therefore, the genetic contribution was also considered by developing innovative methods in conjunction with Cores 1 and 2, to combine imaging data, genetic data, neurocognitive data, and clinical profiles.

2007 APR NIH Questions and Answers

2007-08-05T14:29:10Z

Shenton: /* A clinical project between Toronto and BWH still is recruitment phase in planning a DTI and genetic study of psychosis. What would be the genetic component? ('''Martha Shenton''') */

In a letter from Grace Peng, dated July 31 2007 the center team asks the following questions:

----

==== The weakest and probably most difficult parts of the NA-MIC effort are validation and comparison across algorithms. The validation that is being performed needs to be more systematic and coordinated like the tractography validation effort. Perhaps methods that engage the user community could be tried. ('''Ross Whitaker''')====

----

==== To what problems is DTI best applicable? Is it applicable across age ranges?('''Ross Whitaker''')====

(Comments by M. Shenton). The kind of problems that DTI are most applicable involve the evaluation of white matter fiber tracts. Disorders such as multiple sclerosis are particularly appropriate for using DTI to evaluate white matter lesions that are the sine quo non of this disorder. Other disorders such as stroke, Alzheimer's disease, and schizophrenia are also disorders where white matter is involved and thus where DTI may help to characterize further white matter pathology. With respect to whether or not DTI is applicable across age ranges, what we know now is that white matter in normal development is different across the ages and thus having a tool that makes possible the evaluation of white matter changes associated with normal development, across the life span, is important. As there is no gold standard for evaluating white matter using DTI post-processing tools, some trial and error is needed to determine which tools are optimal at which ages though of course the hope would be that tools can be developed that are sufficiently robust that they can be used to evaluate white matter changes across all age ranges.

==== Although the NA-MIC Wiki contains information on who is using the NA-MIC kit and what are they using it for, the next annual report should either summarize this information or provide a link to the information. ('''Tina Kapur''')====

----

==== The next annual report should include a link and reference to the User Manual for the NA-MIC Kit.('''Will Schroeder''')====

----

==== What is the rational for choosing a particular method (tool) for solving a particular problem (DBP)? Why was a particular method (tool) chosen for development? Is there a listing of which tool might be helpful for which family of problems? Please provide more specific details to these questions as they have been asked previously by the Center Team. ('''Ross Whitaker''')====

----

==== A clinical project between Toronto and BWH still is recruitment phase in planning a DTI and genetic study of psychosis. What would be the genetic component? ('''Martha Shenton''')====

Drs. Martha Shenton (BWH) and James Kennedy (University of Toronto) are beginning a collaboration based on mutual interests, although the specific goals have yet to be worked out. More specifically, Dr. Shenton is very much interested in developing further expertise in her laboratory in the area of genetics, particularly in the area of white matter genes and their association with white matter fiber tract abnormalities evaluated using DTI in schizophrenia. Dr. Shenton has an instructor in Psychiatry in her laboratory who will be visiting Dr. Kennedy’s laboratory for a one week period in August of 2007, to be followed by several later visits, in order to learn state-of-the-art techniques used for evaluating white matter genes and their role in schizophrenia.

In parallel, Dr. Kennedy is very much interested in developing further expertise in his laboratory in the area of neuroimaging, particularly in the area of MR morphometry and DTI measures of white matter in schizophrenia, which he would like to correlate with genetic data involving white matter genes. Following up on this interest, Dr. Kennedy has a 4th year resident in psychiatry at the University of Toronto School of Medicine who works in his laboratory and who is visiting Dr. Shenton’s laboratory from July 1, 2007 to December 31, 2007, in order to learn state-of-the-art neuroimaging techniques, including DTI and its application to understanding white matter pathology in schizophrenia.

The common thread with respect to the genetic component is thus a focus on white matter genes that are relevant to schizophrenia. At this point it is too early to determine where this collaborative effort will go, although it is clear that there is a tremendous amount of interest on both Dr. Shenton’s and Dr. Kennedy’s part, and the hope is that these early efforts will come to fruition in a more extensive collaboration as well as grant funding that supports this collaborative endeavor.

----

==== The visualization tool allows the overlay of spherical, vector and ellipsoid data onto surfaces via versatile color maps. Is this extensible to other data, such a genetic or molecular data? ('''Steve Pieper''')====

The NA-MIC Kit is a set of compatible tools including utilities, libraries, and applications. At the application level, there are many promising areas of genetic or molecular research to which 3D Slicer has not been applied. 3D Slicer is extensible though, with current active projects and pending collaboration grant proposals to adapt and enhance the application to process microscopy data. For example, Drs. Bryan Smith and Mark Ellisman of UCSD are [[Projects/Slicer3/2007_Project_Week_Support_for_electron_microscopy | working on this topic]] through a supplement via the NCBC program. In addition Drs. Machiraju, Pieper, Aylward, and Davis of Ohio State, Isomics, and Kitware have jointly applied for a NA-MIC collaboration grant with the goal of implementing advanced image analysis algorithms that are well adapted to detecting cellular structures (currently in review). Dr. Gouaillard of CalTech is also [[NA-MIC_NCBC_Collaboration:3D%2Bt_Cells_Lineage:GoFigure | collaborating with NA-MIC]] to adapt tools from the [http://www.cegs.caltech.edu/ Center of Excellence in Genomic Science (CEGS)] to work with their studies of the zebra fish embryogenesis. Beyond these specific examples, [http://www.na-mic.org/Wiki/index.php/Slicer:Feedback a wide range of research applications] from surgery planning to astronomy have been enabled by the software. As the slicer3 platform matures, an even larger range of applications is anticipated. At the library and utility levels an even greater diversity of applications is possible as demonstrated by [http://www.kitware.com/case/vtkinuse.html the range of applications using VTK] and [http://www.itk.org/HTML/Applications.htm the applications developed on ITK].

Our approach to extending our software into new fields, such as the wider ranges of genetic or molecular images mentioned in the question, is to identify collaborators who need new image computing solutions of the type NA-MIC is providing. These collaborations often start through technical points of contact; programmers often research open source tools and begin 'tinkering' to see what can be re-used in a new application. If there is sufficient interest, these experiments can grow into collaboration in new fields. For example [[NA-MIC_Collaborations#PAR-05-063:_Automated_FE_Mesh_Development | the collaboration with University of Iowa on Finite Element Meshing]] applies the software in a new direction that other NA-MIC developers had not been exploring.

2007 APR NIH Questions and Answers

2007-08-05T14:23:16Z

Shenton: /* To what problems is DTI best applicable? Is it applicable across age ranges?('''Ross Whitaker''') */

In a letter from Grace Peng, dated July 31 2007 the center team asks the following questions:

----

==== The weakest and probably most difficult parts of the NA-MIC effort are validation and comparison across algorithms. The validation that is being performed needs to be more systematic and coordinated like the tractography validation effort. Perhaps methods that engage the user community could be tried. ('''Ross Whitaker''')====

----

==== To what problems is DTI best applicable? Is it applicable across age ranges?('''Ross Whitaker''')====

(Comments by M. Shenton). The kind of problems that DTI are most applicable involve the evaluation of white matter fiber tracts. Disorders such as multiple sclerosis are particularly appropriate for using DTI to evaluate white matter lesions that are the sine quo non of this disorder. Other disorders such as stroke, Alzheimer's disease, and schizophrenia are also disorders where white matter is involved and thus where DTI may help to characterize further white matter pathology. With respect to whether or not DTI is applicable across age ranges, what we know now is that white matter in normal development is different across the ages and thus having a tool that makes possible the evaluation of white matter changes associated with normal development, across the life span, is important. As there is no gold standard for evaluating white matter using DTI post-processing tools, some trial and error is needed to determine which tools are optimal at which ages though of course the hope would be that tools can be developed that are sufficiently robust that they can be used to evaluate white matter changes across all age ranges.

==== Although the NA-MIC Wiki contains information on who is using the NA-MIC kit and what are they using it for, the next annual report should either summarize this information or provide a link to the information. ('''Tina Kapur''')====

----

==== The next annual report should include a link and reference to the User Manual for the NA-MIC Kit.('''Will Schroeder''')====

----

==== What is the rational for choosing a particular method (tool) for solving a particular problem (DBP)? Why was a particular method (tool) chosen for development? Is there a listing of which tool might be helpful for which family of problems? Please provide more specific details to these questions as they have been asked previously by the Center Team. ('''Ross Whitaker''')====

----

==== A clinical project between Toronto and BWH still is recruitment phase in planning a DTI and genetic study of psychosis. What would be the genetic component? ('''Martha Shenton''')====

Drs. Martha Shenton (BWH) and James Kennedy (University of Toronto) are beginning a collaboration based on mutual interests, although the specific goals have yet to be worked out. More specifically, Dr. Shenton is very much interested in developing further expertise in her laboratory in the area of genetics, particularly in the area of white matter genes and their association with white matter fiber tract abnormalities evaluated using DTI in schizophrenia. Dr. Shenton has an instructor in Psychiatry in her laboratory who will be visiting Dr. Kennedy’s laboratory for a one week period in August of 2007, to be followed by several later visits, in order to learn state-of-the-art techniques used for evaluating white matter genes and their role in schizophrenia.

In parallel, Dr. Kennedy is very much interested in developing further expertise in his laboratory in the area of neuroimaging, particularly in the area of MR morphometry and DTI measures of white matter in schizophrenia, which he would like to correlate with genetic data involving white matter genes. Following up on this interest, Dr. Kennedy has a 4th year resident in psychiatry at the University of Toronto School of Medicine who works in his laboratory and who is visiting Dr. Shenton’s laboratory from July 1, 2007 to December 31, 2007, in order to learn state-of-the-art neuroimaging techniques, including DTI and its application to understanding white matter pathology in schizophrenia. It is too early at this point to determine where this collaboration will go, although it is clear that there is a tremendous amount of interest on both Dr. Shenton’s and Dr. Kennedy’s part, and the hope is that these early efforts will come to fruition in a grant that supports this collaborative endeavor.

----

==== The visualization tool allows the overlay of spherical, vector and ellipsoid data onto surfaces via versatile color maps. Is this extensible to other data, such a genetic or molecular data? ('''Steve Pieper''')====

The NA-MIC Kit is a set of compatible tools including utilities, libraries, and applications. At the application level, there are many promising areas of genetic or molecular research to which 3D Slicer has not been applied. 3D Slicer is extensible though, with current active projects and pending collaboration grant proposals to adapt and enhance the application to process microscopy data. For example, Drs. Bryan Smith and Mark Ellisman of UCSD are [[Projects/Slicer3/2007_Project_Week_Support_for_electron_microscopy | working on this topic]] through a supplement via the NCBC program. In addition Drs. Machiraju, Pieper, Aylward, and Davis of Ohio State, Isomics, and Kitware have jointly applied for a NA-MIC collaboration grant with the goal of implementing advanced image analysis algorithms that are well adapted to detecting cellular structures (currently in review). Dr. Gouaillard of CalTech is also [[NA-MIC_NCBC_Collaboration:3D%2Bt_Cells_Lineage:GoFigure | collaborating with NA-MIC]] to adapt tools from the [http://www.cegs.caltech.edu/ Center of Excellence in Genomic Science (CEGS)] to work with their studies of the zebra fish embryogenesis. Beyond these specific examples, [http://www.na-mic.org/Wiki/index.php/Slicer:Feedback a wide range of research applications] from surgery planning to astronomy have been enabled by the software. As the slicer3 platform matures, an even larger range of applications is anticipated. At the library and utility levels an even greater diversity of applications is possible as demonstrated by [http://www.kitware.com/case/vtkinuse.html the range of applications using VTK] and [http://www.itk.org/HTML/Applications.htm the applications developed on ITK].

Our approach to extending our software into new fields, such as the wider ranges of genetic or molecular images mentioned in the question, is to identify collaborators who need new image computing solutions of the type NA-MIC is providing. These collaborations often start through technical points of contact; programmers often research open source tools and begin 'tinkering' to see what can be re-used in a new application. If there is sufficient interest, these experiments can grow into collaboration in new fields. For example [[NA-MIC_Collaborations#PAR-05-063:_Automated_FE_Mesh_Development | the collaboration with University of Iowa on Finite Element Meshing]] applies the software in a new direction that other NA-MIC developers had not been exploring.

2007 APR NIH Questions and Answers

2007-08-05T14:16:01Z

Shenton: /* A clinical project between Toronto and BWH still is recruitment phase in planning a DTI and genetic study of psychosis. What would be the genetic component? ('''Martha Shenton''') */

In a letter from Grace Peng, dated July 31 2007 the center team asks the following questions:

----

==== The weakest and probably most difficult parts of the NA-MIC effort are validation and comparison across algorithms. The validation that is being performed needs to be more systematic and coordinated like the tractography validation effort. Perhaps methods that engage the user community could be tried. ('''Ross Whitaker''')====

----

==== To what problems is DTI best applicable? Is it applicable across age ranges?('''Ross Whitaker''')====

----

==== Although the NA-MIC Wiki contains information on who is using the NA-MIC kit and what are they using it for, the next annual report should either summarize this information or provide a link to the information. ('''Tina Kapur''')====

----

==== The next annual report should include a link and reference to the User Manual for the NA-MIC Kit.('''Will Schroeder''')====

----

==== What is the rational for choosing a particular method (tool) for solving a particular problem (DBP)? Why was a particular method (tool) chosen for development? Is there a listing of which tool might be helpful for which family of problems? Please provide more specific details to these questions as they have been asked previously by the Center Team. ('''Ross Whitaker''')====

----

==== A clinical project between Toronto and BWH still is recruitment phase in planning a DTI and genetic study of psychosis. What would be the genetic component? ('''Martha Shenton''')====

Drs. Martha Shenton (BWH) and James Kennedy (University of Toronto) are beginning a collaboration based on mutual interests, although the specific goals have yet to be worked out. More specifically, Dr. Shenton is very much interested in developing further expertise in her laboratory in the area of genetics, particularly in the area of white matter genes and their association with white matter fiber tract abnormalities evaluated using DTI in schizophrenia. Dr. Shenton has an instructor in Psychiatry in her laboratory who will be visiting Dr. Kennedy’s laboratory for a one week period in August of 2007, to be followed by several later visits, in order to learn state-of-the-art techniques used for evaluating white matter genes and their role in schizophrenia.

In parallel, Dr. Kennedy is very much interested in developing further expertise in his laboratory in the area of neuroimaging, particularly in the area of MR morphometry and DTI measures of white matter in schizophrenia, which he would like to correlate with genetic data involving white matter genes. Following up on this interest, Dr. Kennedy has a 4th year resident in psychiatry at the University of Toronto School of Medicine who works in his laboratory and who is visiting Dr. Shenton’s laboratory from July 1, 2007 to December 31, 2007, in order to learn state-of-the-art neuroimaging techniques, including DTI and its application to understanding white matter pathology in schizophrenia. It is too early at this point to determine where this collaboration will go, although it is clear that there is a tremendous amount of interest on both Dr. Shenton’s and Dr. Kennedy’s part, and the hope is that these early efforts will come to fruition in a grant that supports this collaborative endeavor.

----

==== The visualization tool allows the overlay of spherical, vector and ellipsoid data onto surfaces via versatile color maps. Is this extensible to other data, such a genetic or molecular data? ('''Steve Pieper''')====

The NA-MIC Kit is a set of compatible tools including utilities, libraries, and applications. At the application level, there are many promising areas of genetic or molecular research to which 3D Slicer has not been applied. 3D Slicer is extensible though, with current active projects and pending collaboration grant proposals to adapt and enhance the application to process microscopy data. For example, Drs. Bryan Smith and Mark Ellisman of UCSD are [[Projects/Slicer3/2007_Project_Week_Support_for_electron_microscopy | working on this topic]] through a supplement via the NCBC program. In addition Drs. Machiraju, Pieper, Aylward, and Davis of Ohio State, Isomics, and Kitware have jointly applied for a NA-MIC collaboration grant with the goal of implementing advanced image analysis algorithms that are well adapted to detecting cellular structures (currently in review). Dr. Gouaillard of CalTech is also [[NA-MIC_NCBC_Collaboration:3D%2Bt_Cells_Lineage:GoFigure | collaborating with NA-MIC]] to adapt tools from the [http://www.cegs.caltech.edu/ Center of Excellence in Genomic Science (CEGS)] to work with their studies of the zebra fish embryogenesis. Beyond these specific examples, [http://www.na-mic.org/Wiki/index.php/Slicer:Feedback a wide range of research applications] from surgery planning to astronomy have been enabled by the software. As the slicer3 platform matures, an even larger range of applications is anticipated. At the library and utility levels an even greater diversity of applications is possible as demonstrated by [http://www.kitware.com/case/vtkinuse.html the range of applications using VTK] and [http://www.itk.org/HTML/Applications.htm the applications developed on ITK].

Our approach to extending our software into new fields, such as the wider ranges of genetic or molecular images mentioned in the question, is to identify collaborators who need new image computing solutions of the type NA-MIC is providing. These collaborations often start through technical points of contact; programmers often research open source tools and begin 'tinkering' to see what can be re-used in a new application. If there is sufficient interest, these experiments can grow into collaboration in new fields. For example [[NA-MIC_Collaborations#PAR-05-063:_Automated_FE_Mesh_Development | the collaboration with University of Iowa on Finite Element Meshing]] applies the software in a new direction that other NA-MIC developers had not been exploring.

2007 Materials for NCBC Program Review

2007-06-08T12:31:27Z

Shenton: /* Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing? */

==Materials requested for NCBC Program Review==

These are due to Gwen Jacobs by Friday, June 08, 2007.

=='''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''==

Summary: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#1._Introduction

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with respect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs include a focus on Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

Highlights: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#3._Highlights

The third year of the NA-MIC project saw continued development and dissemination of medical image analysis software. The current progress is clearly characterized by a significant increase in the application of Core-1 and Core-2 tools to image data provided by the Core-3 DBP groups. The main reasons for this progress is two-fold, first there are several new methods that are out of the prototype stage and ready to be applied to large sets of imaging datasets, and second there are new high-resolution imaging data from high-field scanners that are more appropriate for the new tools than historical data with often very coarse slice resolution.

With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NA-MIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NA-MIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

*Advanced Algorithms
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NA-MIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NA-MIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The NA-MIC toolkit includes a comprehensive set of modules for analysis of diffusion weighted images (DWI), including improved calculation of tensors, interpolation, nonlinear deformation and statistics on tensor fields, novel methods for tractography and for optimal path finding, and clustering of sets of streamlines.
* A quantitative tractography package for user-guided geometric parametrization and statistical analysis of fiber bundles (FiberViewer) has been contributed to the NAMIC toolbox. This tool used in several ongoing clinical DTI studies.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NA-MIC Sandbox in preparation for formal acceptance into the NA-MIC Kit.
*Technology Deployment Platform: Slicer3
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NA-MIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* Slicer3's execution model is also allowing researchers to access large computational resources. Two applications that support the use of such resources, BatchMake and GridWizard, have been recently integrated with Slicer3 for specific modules. The GUI for the modules that are to be run via these computational support applications allow for the creation of large population studies or parametric studies of an algorithm.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NA-MIC Kit, and are now available to all other modules built on the Slicer3 framework.

*Outreach and Technology Transfer
Cores 4-5-6 continue to support, train and disseminate to the NA-MIC community, and the broader biomedical computing community.
* NA-MIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NA-MIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair. The training and dissemination will continue with a DTI workshop at the forthcoming Human Brain Mapping meeting and activities at MICCAI 2007, among others.

==Q2==
'''A brief statement - (one page per question, max) addressing each of the questions listed below. These are the questions that we have been asked to address in our report. Our goal in asking for this information is to be able to produce a report that reviews the program as a whole. Your view, from the vantage point of the center you direct, is critical to our work. In addition, your answers will provide us with more information that we can use in our discussion with program staff on June 11th. We know that some of this information can be found on your websites, so in those cases a link to the information would be most helpful.'''

===Q2.1 To what extent does the vision and direction of the NCBC initiative promote biomedical computing?===

The RFA for the creation of the NCBC program laid out a very explicit vision.

The NCBCs are to be the core of the networked national effort to build the computational infrastructure for biomedical computing in the US. From the [http://www.bisti.nih.gov/ncbc/ NIH website] in 2004: Four new National Centers for Biomedical Computing (NCBC) will develop and implement the core of a universal computing infrastructure that is urgently needed to speed progress in biomedical research. The centers will create innovative software programs and other tools that enable the biomedical community to integrate, analyze, model, simulate, and share data on human health and disease. The original [http://grants1.nih.gov/grants/guide/rfa-files/RFA-RM-04-003.html RFA] stated: 1. The software should be freely available to biomedical researchers and educators in the non-profit sector and 2. The terms of software availability should permit the commercialization of enhanced or customized versions of the software.

As the amount of data produced by biomedical researchers is increasing at an ever accelerating pace, the relative importance of computing as an integral part of analysis is increasing as well. In addition both data and analyses are getting more and more complex. Increasingly, science is performed by interdisciplinary teams at multiple locations. Industrial strength research platforms are one of the emerging needs of biomedical research. This need was only been partially addressed before the creation of the NCBC program. The NCBC centers are in an outstanding position to develop stable, maintainable, and expandable software to address these needs.

In addition to building the infrastructure for biomedical computing, NCBCs play an important role in fostering a community of computational scientists dedicated to solving problems in the biomedical domain. In its short three-year life span, NA-MIC NCBC has contributed to this vision by holding numerous workshops and tutorials on open source software for biomedical image analysis, by supporting the Insight Journal, a peer-review venue for publications accompanied with open-source implementation, and by engaging a large number of graduate students in Computer Science programs in biomedical computing research. By creating high-profile bio-computation programs, the NCBC initiative brings the biomedical computing as a field of research to the attention of the computation community and actively promotes collaborations between computational and biological sciences.

The participants in our center signed on for this project because they believe in the vision of developing a universal computing infrastructure for medical image computing. NA-MIC is creating an open source platform that embodies this vision for the field of medical image computing. We have settled on a very liberal open source license for our software plattform. All the components of that plattform, called the NA-MIC kit, are distributed under a BSD style licence without restrictions on commercial use. Other centers have adopted different strategies to address the requirement for open source and to enable commercial use, as required in the original RFA; we believe the NA-MIC liberal license approach minimizes barriers to wide adoption and is will maximize return on the NIH investment. The availability of software platforms commoditizes infrastructure for research and allows individual researchers to spend more time on their core research. Over time, this will promote biomedical computing by lowering the hurdle for scientists to use the technology.

===Q2.2 In what ways has the NCBC initiative advanced biomedical computing?===
Ron:(Marty edits also)

It is too early to say how the NCBC initiative advanced biomedical computing, though the results thus far are promising. Four of the NCBC's have been funded since October 2004 (less than three years), and funding for the other three began in late 2005. Center efforts of this size need significant time and effort to get organized and to synchornize activities of all participants. This startup effort is where the primary focus of the centers has been until now. There are early signs that some of the centers are beginning to emerge from this phase of their evolution and turning toward activities aimed at the field at large. However, it will be several years before the full impact of this program will become visible.

Below is a more detailed discussion of the specifics of this evolution from the vantage point of NA-MIC

(Tina)

Answer:http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#4._Impact_and_Value_to_Biocomputing

In NA-MIC's third year, it is clear that NA-MIC is developing a culture, environment, and resources to foster collaborative research in medical image analysis that draws together mathematicians, computer scientists, software engineers, and clinical researchers. These artefacts of NA-MIC impact how NA-MIC operates, make NA-MIC a fulcrum for NIH funded research, and draws new collaborators from across the country and around the world to NA-MIC.

*Impact within the Center

**Within the center, the NA-MIC organization is nimble, forming ad hoc distributed teams within and between cores to address specific biocomputing tasks. Information is shared freely on the NA-MIC Wiki, on the weekly Engineering telephone conferences, and in the NA-MIC Subversion source code repository. The software engineering tools of CMake, Dart 2 and CTest, CPack, and KWWidgets facilitate a cross platform software environment for medical image analysis that can be easily built, tested, and distributed to end-users. Core 2 has provided a platform, Slicer 3, that allows Core 1 to easily integrate new technology and deliver this technology in an end user application to Core 3. Core 1 has developed a host of techniques to apply to structural and diffusion analysis which are under evaluation by Core 3. Additionally, Core 3 members are heavy users of the Slicer 3 software and they provide ongoing feedback to Core 2 with respect to features that are optimal and those that need to be refined. They also assist in refining the user interface of this software platform. Core 3 members are also actively using the software and new tools developed by applying them to address scientific questions of interest, as is evident from the new publications using these tools. Major NA-MIC events, such as the annual All Hands Meeting, the Summer Project Week, the Spring Algorithms meeting, and Engineering Teleconferences are avidly attended by NA-MIC researchers as opportunities to foster collaborations.

*Impact within NIH Funded Research

** Within NIH funded research, NA-MIC continues to forge relationships with other large NIH funded projects such as BIRN, caBIG, NAC, and IGT. Here, we are sharing the NA-MIC culture, engineering practices, and tools. The BIRN infrastructure, built on widely-accepted grid middleware, allows NA-MIC researchers to share data, access computational resources and provides a rich collaborative environment through a science portal. caBIG lists the 3D Slicer among the applications available on the National Cancer Imaging Archive. NAC and IGT use the NA-MIC infrastructure and are involved in the development of the 3D Slicer. BIRN recently held an event modeled after the NA-MIC Project Week. NA-MIC has become a resource on open source licensing to the medical image analysis community.

NA-MIC is also attracting NIH funded collaborations. Two grants have been funded under PAR-05-063 to collaborate with NA-MIC: Automated FE Mesh Development and Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI. Five additional applications to collaborate with NA-MIC via the NCBC collaborative grant mechanism are under consideration. Additional grant applications submitted under other calls are planning to use and extend the NA-MIC tools. The first collaborative R01 from a previous driving biological problem has also been submitted by Drs. Shenton and Saykin to follow up on important tool development ideas that will combine multi-modal imaging to address specific questions relevant to brain circuitry abnormalities in schizophrenia.

*National and International Impact

**NA-MIC events and tools garner national and international interest. There were nearly 100 participants at the NA-MIC All Hands Meeting in January 2007, with many of these participants from outside of NA-MIC. Several researchers from outside the NA-MIC community have attended the Summer Project Weeks and the Winter Project Half-Weeks to gain access to the NA-MIC tools and people. These external researchers are contributing ideas and technology back into NA-MIC.

**Components of the NA-MIC kit are used globally. The software engineering tools of CMake, Dart 2 and CTest are used by many open source projects and commercial applications. For example, the K Desktop Environment (KDE) for Linux and Unix workstations uses CMake and Dart. KDE is one of the largest open source projects in the world. Many open source projects and commercial products are benefiting from the NA-MIC related contributions to ITK and VTK. Finally, Slicer 3 is being used as an image analysis platform in several fields outside of medical image analysis, in particular, biological image analysis, astronomy, and industrial inspection.

**NA-MIC co-sponsored the Workshop on Open Science at the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2006 conference. The proceedings of the workshop are published on the electronic Insight Journal, another NIH-funded activity.

**Over 50 NA-MIC related publications have been produced since the inception of the center.

===Q2.3 Are the NCBCs interfacing appropriately?===
(MARTY EDITS - need to address NCBC's interfacing ONLY)

Outreach across NCBCs is an effective use of resources. Initial efforts are ongoing and include cross-center use of software tools such as SimBios/SimTk actively using NAMICs VTK toolkit, and the use of the promising Software and Data Integration Working Group (SDIWG). In addition, NA-MIC has interactions with CCB and Simbios. NA-MIC also interfaces with a number of large-scale efforts which are not NCBC but are comparable in scale: BIRN, several national resource centers, and to some extent, with CaBIG. The NCBC's are thus interfacing appropriately.

Within the next few years there will be a need to increase NCBCs interfacing significantly. This will require either reallocation of resources or the allocation of new, additional resources. Due to the large variety of approaches adopted by the different centers it will likely be necessary to allocate significant amounts of time by senior leadership at the centers for identifying objectives of such efforts and by the engineering cores to actually execute the plans. A serious effort in this direction will probably require the allocation of multiple FTE's.

===Q2.4. What new collaborations have been formed through the NCBC initiative?===
(MARTY NEW EDITS)
NA-MIC’s structure and organization has facilitated many new collaborations. NA-MIC is a distributed center, bringing together mathematicians, computer scientists, software engineers, and clinicians from multiple sites. This distributed structure provides two types of new collaborations within NA-MIC: new collaborations between cores and new collaborations within cores. For between core collaborations, many of the algorithm and engineering core researchers had not collaborated previously with the researchers in either the first or second round of Driving Biological Projects (DBPs). Thus, the NCBC provides a unique opportunity for the algorithm and engineering core researchers to gain clinical insight and to adapt and tune their algorithms and tools to new clinical contexts. Conversely, the DBPs have gained access to algorithms and tools that they previously did not have. Additionally, new collaborations among DBPs and Core 1 members is an important step in bringing together computer scientists and clinical researchers and we have been most successful here. Similarly, many of the algorithm core researchers and engineering core researchers had not previously collaborated. Thus, the NCBC exposed the researchers in the algorithm core to the tools and engineering practices of the engineering core and exposed the researchers in the engineering core to the computational techniques and data structures utilized by the algorithms core. For within core collaborations, many of the researchers within the algorithm core had not previously collaborated. Through NA-MIC, these researchers have been able to cooperate and also amicably compete to address the issues brought forth by the DBPs.

Below is a list of new collaborations within NA-MIC. This list was compiled from the complete list of [[NA-MIC_Collaborations | NA-MIC Collaborations]] and project lists from the 5 NA-MIC [[Engineering:Programming_Events |Project week events]]. Best effort was made to filter these lists down to just the new collaborations (groups of researchers) formed under NA-MIC.

[TINA a Question from MARTY: IS BWH CORE 3 I THINK SO?? I THINK SO, SO WHO IS HARVARD?? IS THIS CF?? HAVE A LOOK BELOW]

* Georgia Tech + UC Irvine – Rule based segmentation algorithm for DLPFC
* Georgia Tech + Kitware - Knowledge-based Bayesian classification and segmentation
* BWH + MIT + Kitware - Brain tissue classification and subparcellation of brain structures
* Georgia Tech + UNC + BWH - Multiscale shape segmentation techniques
* BWH + Dartmouth + UNC + Georgia Tech - Shape analysis of the caudate and corpus callosum
* Georgia Tech + UNC + BWH - Spherical wavelet based shape analysis for Caudate
* Georgia Tech + UNC + BWH - Multiscale shape analysis of the hippocampus
* Dartmouth + UNC + BWH - Shape analysis of tbe hippocampus
* Utah + UNC + BWH - Automated shape model construction
* Dartmouth + Isomics - Neural substrates of apathy in schizophrenia
* Georgia Tech + GE Research + Kitware - Spherical Wavelet Transforms
* Georgia Tech + UNC - Shape analysis with Spherical Wavelets
* Utah + UNC - Adaptive, particle-based sampling for shapes and complexes
* UNC + Utah + ???Harvard - Tensor estimation and Monte-Carlo simulation
* Harvard + MIT + UNC - Corpus Callosum Regional FA analysis in Schizophrenia
* Dartmouth + MGH + Isomics + BWH - Integrity of Fronto-Temporal Circuitry in Schizophrenia using Path of Interest Analysis
* MGH + Isomics - ITK implementation of POIStats, and Integration into Slicer3
* UC Irvine + MGH + UNC + MIT - DTI Validation
* Utah + UNC + GE Research - DTI Software and Algorithm Infrastructure
* Utah + BWH - Tensor based statistics
* Utah + BWH - Diffusion tensor image filtering
* MGH + Dartmouth + Kitware + GE Research - Non-rigid EPI registration
* Dartmouth + BWH - Neural Substrates of Working Memory in Schizophrenia: A Parametric 3-Back Study
* Dartmouth + BWH - Brain Activation during a Continuous Verbal Encoding and Recognition Task in Schizophrenia
* Dartmouth + BWH - Fronto-Temporal Connectivity in Schizophrenia during Semantic Memory
* UC Irvine + Toronto - Imaging Phenotypes in Schizophrenics and Controls
* MIT + Isomics + GE Research + Kitware - fMRI statistics software
* MIND + Isomics + MGH - Analysis of Brain Lesions in Neuropsychiatric Systemic Lupus Erythematosis
* JHU + Queen's + BWH + Georgia Tech - Segmentation and Registration Tools for Robotic Prostate Interventions
* UNC + GE Research - Longitudinal MRI study of early brain development
* BWH + Kitware + MIT - Velocardiofacial Syndrome (VCFS) as a genetic model for schizophrenia
* UNC + GE Research + BWH - DTI population analysis
* Georgia Tech + BWH - Geodesic tractography
* BWH + Queen's + GE Research - Display optimization
* UCSD + Isomics - Dendritic Spine Morphometrics
* <others to be filled in>

NA-MIC has also attracted researchers from the field who were not originally part of NA-MIC. Some of these new collaborations are formally organized using the NIH NCBC Collaborative R01 program. But other collaborations are being driven solely by the opportunity to share resources, techniques, capabilities, and ideas.

Below is a list of new collaborations between external researchers and NA-MIC. Again, best effort was made to only list the new collaborations with external parties.

* Mario Negri + GE Research – Integration of vmtk with Slicer 3
* Iowa + Isomics - Finger Bone Biomechanics
* CalTech + Kitware - Systems Biology and Genomic Science
* BWH + Wake Forest + Virginia Tech - Alcohol Stress in Primates
* BWH + MGH - Radiation Treatment Planning'
* Iowa + Kitware + BWH - Non Linear Registration Tools
* Northwestern + Isomics - Radiology Translation Station
* Harvard IIC + Isomics + GE Research - Astronomy Analysis and Visualization
* Virginia Tech + BWH - Applying EMSegmenter to nonhuman primate neuroimaging
* JHU + Queen's + BWH - Brachytherapy needle positioning robot integration
* Iowa + Kitware + BWH - Nonrigid registration
* Iowa + BWH - Developing electronic atlas
* Iowa + Kitware - GUI for nonridig image registration
* Canary Islands Technological Institute + Isomics + GE Research - DICOM Query/Retrieve
* Canary Islands Technological Institute + GE Research - Block matching registration
* UNC + Duke University Medical Center - DTI tractography analysis in depression study
* <others to be filled in>

The collaborative nature of NA-MIC is exemplified by the attendance at the NA-MIC All Hands Meeting and the NA-MIC Summer Project Week. Researchers from within and external to NA-MIC come together at these two events to forge collaborations. At the 2007 NA-MIC All Hands Meeting alone, there were 96 attendees: 56 NA-MIC researchers, 32 NA-MIC collaborators from 13 institutions, and 8 members of the External Advisory Board and NIH. At the Project Half-Week run in conjunction with the All Hands Meeting, there were 38 projects: 16 initiated from the algorithm core, 10 specific to the engineering core, and 11 from external collaborators.

More detailed information on collaborations as well as Project Week events can be found at:
* http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations
* http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events

===Q2.5. What new training opportunities have the centers provided?===

(MARTY: My questions is that generally training also refers to NEW investigators coming into the field, should there be some mention of that as well below?)

**Answer: http://www.na-mic.org/Wiki/index.php/Training:Main

* The requirement for all NCBC's to dedicate funds for training provides the opportunity to develop a targeted and deep portfolio of training resources. The unique perspective of providing these training opportunities and resources specifically targeted to a multi-disciplinary audience of basic and clinical biomedical scientists, computer scientists and medical imaging scientists is fostered by the NCBC Program. Traditional funding by NIH research grants results in allocation of all funds into the primary research; NIH training grants allocate funding for the support of trainees; the few education grants are restricted in budget and overhead thus severely limiting the quality and quantity of educational resources that can be offered. The NCBCs have created a new opportunity for our cadre of experienced clinician scientists, computer scientists and medical image analysis experts affiliated with our large centers to be supported to work on outreach activities.

* Within NA-MIC, this perspective has given rise to a thriving training program that supports the biomedical research community within NAMIC, across the NIH community and around the world. The strong demand for our training resources is evident from the large number of hits to our training web pages, from the rapid enrollment in all offered workshops, and the positive feedback from participants. We believe, and our belief is supported by the documented backgrounds of our workshop attendees, that a key aspect of our training materials that makes them useful to the community is that they are learner-centered, goal-oriented, and targeted to bridge the gaps in technical knowledge and language that exist between basic and clinical biomedical scientists, computer scientists and medical imaging scientists. For example, a tutorial that teaches how to use Slicer to register two images includes not only the necessary details of how to implement the algorithm, but also the conceptual framework for the registration approach, the mathematical underpinnings of the algorithm and a detailed anatomical approach for visually inspecting and refining the registration. This rich, but simple approach provides a consistently educational experience for every new user of the NAMIC toolkit.

* NAMIC supported new training opportunities are developed to maximize impact on the wider scientific community. The primary vehicle for this is "Slicer 101", our portfolio of Slicer training tutorials (http://www.na-mic.org/Wiki/index.php/Slicer:Workshops:User_Training_101). We have focused our efforts on making all our tutorial materials available via the NA-MIC Wiki as downloadable Powerpoint presentations and accompanying curated, anonymized datasets. The tutorials are all carefully tested on multiple computer platforms and by our team before being used in live Workshops (http://www.na-mic.org/Wiki/index.php/Training:Events_Timeline). Refinements are made based on the feedback of the audience and our experience during the teaching sessions. The final product of our work allows any new users, regardless of educational background, to not only use the NAMIC tools and algorithms, but to understand what they are doing and why. To date we have had over 7,880 hits to the Slicer 101 webpage.

* NAMIC supported Workshops are another unique venue for multi-disciplinary training. In addition to the all the points made regarding the content of the training materials, the 14 Workshops run by the NAMIC Training core over the past 3 years have each provided the opportunity for new connections to be made among basic and clinical biomedical scientists, computer scientists and medical imaging scientists. All our Workshops provide opportunities for formal and informal discussions among attendees of diverse backgrounds and strengths. These hands-on, interactive workshops allow participants to translate concepts of medical image processing into skills through instructor-led training. The simplicity of our approach, and the exceptional quality of the NAMIC toolkit, ensures a very high success rate for knowledge and skill acquisition. We estimate that 370 people from 52 different universities and companies attended our Workshops between 2005 and 2006.

* We are currently focusing our efforts on reaching a wider community by delivering a more didatic based Workshop in conjunction with the upcoming Organization for Human Brain Mapping meeting in Chicago next week. We held our enrollment to 50 so that we could offer the same hands-on interactive training experience to the attendees and our registration filled within a few weeks of the offering being posted. Tenatively, we anticipate that more than 12 countries and 14 states within the US will be represented at this upcoming Workshop.

* A final point is that this commitment and focus on training permeates all aspects of the NAMIC program. All large gatherings of NAMIC personnel including All Hands Meetings, Programming/Project Weeks, and Core meetings provide venues for our culture of training to be expressed. Each gathering creates an opportunity to build bridges between our participating disciplines and to improve the communication skills of each member. This culture includes implicit aspects such as a supportive and collegial environment that encourages questions and critical feedback, as well as explicit aspects such as encouraging junior level participants to make presentations and scheduling educational presentations from domain experts within and outside of the community. We believe that the positive attitude towards sharing knowledge and skills is fostered in all who are associated with our Project.

===Q2.6. What changes could make the program more effective in the future?===

* ''National Visibility''

The overall objective statement of the NCBC program calls for building computational infrastructure for the nation's biomedical research efforts -- this ambitious goal includes both computational and biomedical science, areas in which the existing centers have extensive experience, but also in nationwide "marketing" efforts where the scientific community is less adept. Several centers, including NA-MIC, have developed novel approaches to this problem through their training and dissemination cores, but as the output of the centers grows along with the number of potential users in the community and the potential impact, these critical resources will be increasingly strained. There are several approaches the overall program could take to address this issue including supplemental funding to host workshops or conferences, providing small travel grants for researchers or students to visit the centers, or actively encouraging a wider range of NIH funded researchers to adopt the tools generated by the NCBCs. In particular, collaboration PAR has been very effective as a mechanism for encouraging busy scientists to consider adopting the NCBC tools and is an excellent example of how the program can extend the impact of the basic investment in scientific infrastructure.

* ''Local Autonomy''

The program should avoid adding extra layers of uniformity to what are fundamentally unique centers. The NCBC program has successfully established a distributed network of centers drawing on the expertise of some of the nation's leading researchers drawn to the program for the opportunity to develop and apply their know-how to this ambitious effort. This rich environment, predictably, yields a diversity of approaches and organizational structures as each of the centers works to implement their particular vision of how to fulfill the overall mission of the NCBC program. Preserving the vitality of the effort depends on retaining this autonomy as each center strives to meet the individual objectives suited to their communities. The program needs well defined goals that each center must meet, but the overall program should facilitate the individual solutions of the center's leadership.

* ''Stability''

One critical mission of the NCBC program is the creation of infrastructure. These large-scale efforts require time-horizons that are incompatible with the current framework of many programs at NIH. It might take two or three iterations until a software package is sufficiently mature to be attractive to a larger community of scientists. Large software platforms require engineering staff of 10-20 in addition to the biomedical scientists developing functionality aimed at solving a particular problem. Many of the NCBCs are working on several packages. This results in underfunded projects which compromises the timeliness and performance of the resulting software.

It would be advisable to increase funding for the engineering and outreach activities of the centers and provide the funding in a reliable way. The continous stream of cuts from of the original budget has made this discrepancy even more pronounced. In 2007/2008 we will receive only 77.7% of the money that was budgeted in the application for that year. Following the RFA guidelines, the original budget did not contain adjustments for inflation. Furthermore, in deviation from the way that most NIH programs are funded, the budget was frozen in total dollars not direct dollars. Institutional overhead rates, and fringe and benefit rates have increased for several of the NA-MIC participants during the last three years and have resulted in further decreases in the amount of money available to actually do research.

===Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing?===

* ''Greater Start Up Time and Steeper Learning Curve Than Anticipated''

Bringing together computer scientists, engineers, and biomedical researchers, with diverse interests, training, and background, for the purpose of working on a set of biological problems, is no easy feat and, in retrospect requires a steeper learning curve than was anticipated. This steeper learning curve is understandable, since the main focus initially was on developing alliances among cores in order to increase awareness regarding the kinds of tools needed for the specific imaging problems posed by the biomedical researchers. The first year of the grant, as noted in our annual report, reflected a "core" emphasis. It was not until the second year that the focus shifted to a focus on "themes", which cut across "core" boundaries. While this shift was viewed as part of a natural evolution, this can help guide future NIH efforts by where we should begin new centers by suggesting that specific projects/clinical applications be highlighted in the first few months of the Center, based on meetings among core members, so as to facilitate a focus on clinical applications from the outset.

Such an early emphasis on clinical applications/problems would facilitate an early focus on the development and application of computational tools, which could be more closely aligned with specific clinical problems and applications. This would breakdown artificial barriers that a "core" focus involves, which, while seemingly an inherent part of the initial stages, could be curtailed. In this way, the needed applications of the driving biological problems could form natural groupings that involve members from all cores, and work groups could be set up that reflect a "theme"/"application" approach. This would also assist in more communication between cores, which would also facilitate ongoing communication among computer scientists, engineers, and biomedical researchers.

* ''Developing Robust Software for Advanced Applications is Difficult''

Creating industrial-strength software solutions to support scientific investigations is time consuming and requires skills not usually found in the academic environment. NA-MIC has been successful at bringing in commercial software development expertise to help accomplish the Center's goals (GE, Kitware, Isomics) but these resources are routinely stretched well beyond the allocated budgets due to the many research directions of the Center's scientists. When considering biomedical computing projects, NIH should keep in mind the time consuming nature of such efforts, otherwise the resulting systems run a greater risk of being difficult to maintain, difficult to scale up, and incompatible across systems.

An excellent example of the level of effort needed for a successful, extensible, cross-platform product is the National Library of Medicine's Insight Toolkit (ITK). The organizational meeting for ITK was held in October 1999. Between October 1999 and October 2002, fifty developers contributed code from six prime contractors (GE, Kitware, Insightful, UNC, Utah, and UPenn) and four sub-contractors (BWH, UPenn, Pitt, and Columbia) to produce ITK Version 1.0. To date, over $13.5 million has been awarded by the NIH for the development, use, and expansion of ITK. That total includes 20, one-year contracts that were given to early adopters of ITK. When assessing the level of effort expended on ITK, it is important to consider that ITK's funding did not cover algorithm, graphical user interface, or visualization developments. ITK costs only funded the integration of existing methods into a common library. Developing end applications, involving user interfaces and visualizations tailored for clinical users, requires significant additional effort.

* ''Algorithm Development Needs to be Interactive and Not Sequential''

In reviewing the last three years of the driving biological problem, schizophrenia, it is evident that tool development involved multiple interactions among members of Core 1 (Computer Scientists), 2 (Engineers), and 3 (Driving Biological Problem), at all stages of development, which led to the development of computational tools that were both more tailored to the specific applications needed by Core 3 members, as well as to the development of tools that were more optimized for general use. This interactive mode of tool development is in contrast to tool development that proceeds more sequentially, i.e., where one or several members of Core 1 and 3 met, and then Core 1 proceeded with what their understanding was of the problem and went off and developed a tool with very little further input from Core 3 until the tool was delivered. The latter approach often resulted in delays in receiving the tool, as there was less communication between Core 1 and Core 3 members in these instances, and often the tool did not really meet the specific needs of the application without further work. In the future, and based on this experience, NIH initiatives should emphasize the importance of encouraging a more "interactive" approach to tool development and to discouraging what is termed here as a more "sequential" approach to tool development. With a more interactive approach, progress can be more readily evaluated at each phase of tool development, and input and testing can be provided based on more communication among members of Core 1, 2, and 3. A more "interactive" model is also far more responsive to the needs of the driving biological problem, and also keeps the focus on the clinical application.

Focusing on interactions across core members will also likely facilitate breaking down the steep learning curve inherent in early interactions across cores members (see above).

* ''Funding''

As mentioned above, there is a mismatch between the objectives set by NIH and the resources available to address them. Moreover, additional cuts imposed over the last three years, combined with a "requirements creep", are problematic. In the future, increased interaction between Centers will require either additional resources or a change in the committment of existing activities. Of further note, the more successful Centers are, the more there will be an increase in the number of scientists who will want to interact with scientists at the Centers, which will also require additional resources. NIH will thus need to allocate significant additional resources to Centers in order to fulfill the vision of a network of Centers of biomedical computing, at the center of an ever increasing network of biomedical scientific programs benefiting from their software and services.

=='''Q3:A list of publications and/or software tools produced by the Center.''' If this information is provided in your progress report or is available on your website, a link will be sufficient. We are especially interested in your assessment of the maturity of your software tools and the impact they are having on the scientific community.===

A3:
*NA-MIC Publications are available here: http://www.na-mic.org/Wiki/index.php/Publications.
*NA-MIC Software Tools are available here: http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit
The Center has created and extended a number of software tools to handle some of the key problems in medical imaging analysis and to deliver these computational technologies via a suite of applications, toolkits, and infrastructure facilities. A summary description of these tools includes:
* Slicer3 (application) - a application platform for deploying imaging technologies, newly architected with an execution model facilitating integration, work flow, and large scale computing. While Slicer3 is the newest addition to the NAMIC Kit, it is built on pre-existing, mature toolkits so that the application is relatively mature, and is already in use. Because Slicer3 supports plug-in modules, active development is proceeding to create and package various modules for dissemination to the NAMIC community.
* ITK (toolkit) - a mature system for image analysis, registration and segmentation (initially created in 1999). ITK is in use worldwide for medical imaging research and development.
* VTK (toolkit) - a mature system for visualization, graphics, volume rendering, and interaction (initially created in 1993). VTK is used worldwide for research, teaching and commercial development.
* DART (computational infrastructure) - a key component of the NAMIC quality control process, DART is used to coordinate and report the software testing process. It was created in the first year of NAMIC and is in constant use, therefore a mature system.
* CMake/CTest/CPack (computational infrastructure) - CMake and CTest are relatively mature systems used to manage the building and testing of software across diverse computer platforms. CMake is used worldwide by some of the [http://lwn.net/Articles// world's largest open source systems such as KDE]. CPack, a recent addition to the NAMIC kit, is used to simplify the packaging and dissemination of software across platforms. Thus in NAMIC we can easily deploy our software across Windows, Linux, Unix, and Mac platforms.
* Other tools (computational infrastructure) - Many other software tools are used to support the development of advanced imaging applications, and to assist with large scale computing, including
** Teem - image processing tools (mature)
** KWWidgets - Open source, cross platform GUI toolkit (mature, but development continues to support workflow).
** BatchMake - Support large-scale computing, including grid computing, for performing large population studies and statistical analysis (under active development).
** GridWizard - an application scheduler aimed at allowing researchers to easily harness the power of large computational grids. It lets you run tens of thousands of commands simultaneously on multiple clusters of computers by typing a single command, without writing scripts. It can be used by itself, and is currently being integrated with Slicer3 and as part of a web-based portal environment. (http://www.na-mic.org/Wiki/index.php/Slicer3:Grid_Interface)

These tools address key problems in imaging including segmentation, registration, visualization, and shape analysis; or provide facilities supporting researchers and developers who wish to create advanced software applications. One of key characteristics of NAMIC is that we treat the development of advanced medical image analysis software holistically; that is, the complete cycle of algorithm design, efficient impmentations, quality control and dissemination are needed to effectively address challenges provided by the driving biological problems. Examples of how these tools are being used include the following:

(a) Segmentation: Here there are a variety of tools of varying degrees of maturity ranging from the EM Segmentor (a widely distributed, mature algorithm included in Slicer3 as an application plug-in) to more recent work on DTI segmentation based on directional flows which are Matlab and C++ based. Powerful mature tools such as Bayesian segmentation have been recently included in Slicer (and have been available in ITK for some time now) which can be combined with very recent work on the semi-automated segmentation of the DPFC done in collaboration with Core 3 researchers. Further, tools previously developed by NAMIC researchers which have had a wide distribution have been put into Slicer. For example, geometric based segmentation methods (some of which were included in packages marketed by GE) were tailored for cortical segmentation, and included in the Slicer, and in fact even improved with the inclusion of statistical based approaches.

(b) Registration: Similar remarks can be made for registration in which we have a spectrum ranging from very mature methods to very recent ones which are still being tested. In particular, mature widely distributed methodologies for rigid registration are now included in ITK, as well as spline-based registration methodologies. These are well-tested methods which have been made accessible to the general imaging community. Newer methodologies such as those based on optimal transport for elastic registration are being included in ITK. NAMIC has also pushed for fast implementations of its algorithms to be used on cheap widely available platforms. Taking a cue from the game industry, some algorithms have been ported to GPUs (graphics cards) which are being employed now as computing devices. This has led to a speed-up of almost two orders of magnitude on some of the registration algorithms being tested.

(c) Shape Analysis: Again a number of methodologies have been developed and implemented with varying levels of maturity. Shape methodologies based on spherical harmonics are quite mature, and are available in pipelines developed by NAMIC researchers and have been distributed to the general community. A newer spherical based wavelet shape analysis package has been put into ITK, which also drives a novel shape-based segmentation procedure. More globally based spherical harmonic ideas have been combined with the multi-resolution spherical wavelet approach as a statistical shape based package for schizophrenia. This general technique may be used for other purposes as well, and is presently being ported to some work being done on the prostate. Work has also been accomplished on particle based approaches to this important problem area with the code put into ITK. Many times we work with a Matlab/C++ initial version of our codes, then move to ITK, and finally to Slicer. However, even at the Matlab/C++ stage, algorithms have been distributed and used in a clinical setting (for example, rule-based brain segmentation approaches).

(d) Diffusion Weighted Image (DWI) Analysis: A number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical analysis of fiber bundles have been contributed to the NAMIC toolkit. Some of these tools have been already integrated into diffusion dedicated ITK package with GUI as part of the NAMIC toolkit (e.g., FiberViewer-UNC) and into the Slicer platform (BWH tractography, clustering). The impact of NAMIC DWI analysis activities is best characterized by most recent journal articles and journal articles in print. The application of the NAMIC FiberViewer tool (UNC) in large clinical studies at UNC and Duke are in print (Taylor et al, Gilmore et al., Cascio et al.). Clinical application of the Slicer DTI package by BWH/MIT is reported in O’Donnell et al. Kuroki N. et al. and Nakamura et al.. The research by MGH is found in two journal publications by Tuch et al.. The description of the methodologies also appeared or will appear in peer reviewed journals (Corouge et al., Fletcher et al., Corouge et al.). New methods in development (Finsler metric GT, volumetric PDE based path analysis Utah, stochastic tractography BWH), and path of interest MGH, are currently tested on Core-3 DBP data, with the goal to give recommendations on which type of solution is appropriate to solve specific clinical analysis questions.

(e) Visualization: Core 2 researchers involved with NAMIC now (e.g., the founders of Kitware) were at the forefront of developing VTK (and of course ITK). Thus here we are considering technologies which are at a commercial level of development, and used at thousands of sites. Algorithms developed at NAMIC have driven new directions for these packages. Newer visualization methods, for example, the conformal flattening procedure have been ported to an ITK filter and is in the NAMIC Sandbox. Quasi-isometric methods for brain flattening from the MGH Free Surfer have become part of the NAMIC enterprise as well. These flattening procedures are very easy to use, and may also be employed for registration. Code for the control of distortion of the area in flattening has been incorporated which give area-preservation with minimal distortion. The techniques may be also used for several other purposes included automatic fly-throughs in endoscopy (incorporated into Slicer), and for texture mappings for general visualization purposes.

==Logistics==
When completed, the information should be sent to:

Gwen Jacobs, PhD

Professor of Neuroscience

Asst. CIO and Director of Academic Computing

1 Lewis Hall

Montana State University

Bozeman, MT 59717

406-994-7334 - phone

406-994-7077 - FAX

gwen@cns.montana.edu <mailto:gwen@cns.montana.edu>

2007 Materials for NCBC Program Review

2007-06-08T12:03:48Z

Shenton: /* Q2.5. What new training opportunities have the centers provided? */

==Materials requested for NCBC Program Review==

These are due to Gwen Jacobs by Friday, June 08, 2007.

=='''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''==

Summary: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#1._Introduction

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with respect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs include a focus on Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

Highlights: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#3._Highlights

The third year of the NA-MIC project saw continued development and dissemination of medical image analysis software. The current progress is clearly characterized by a significant increase in the application of Core-1 and Core-2 tools to image data provided by the Core-3 DBP groups. The main reasons for this progress is two-fold, first there are several new methods that are out of the prototype stage and ready to be applied to large sets of imaging datasets, and second there are new high-resolution imaging data from high-field scanners that are more appropriate for the new tools than historical data with often very coarse slice resolution.

With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NA-MIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NA-MIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

*Advanced Algorithms
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NA-MIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NA-MIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The NA-MIC toolkit includes a comprehensive set of modules for analysis of diffusion weighted images (DWI), including improved calculation of tensors, interpolation, nonlinear deformation and statistics on tensor fields, novel methods for tractography and for optimal path finding, and clustering of sets of streamlines.
* A quantitative tractography package for user-guided geometric parametrization and statistical analysis of fiber bundles (FiberViewer) has been contributed to the NAMIC toolbox. This tool used in several ongoing clinical DTI studies.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NA-MIC Sandbox in preparation for formal acceptance into the NA-MIC Kit.
*Technology Deployment Platform: Slicer3
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NA-MIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* Slicer3's execution model is also allowing researchers to access large computational resources. Two applications that support the use of such resources, BatchMake and GridWizard, have been recently integrated with Slicer3 for specific modules. The GUI for the modules that are to be run via these computational support applications allow for the creation of large population studies or parametric studies of an algorithm.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NA-MIC Kit, and are now available to all other modules built on the Slicer3 framework.

*Outreach and Technology Transfer
Cores 4-5-6 continue to support, train and disseminate to the NA-MIC community, and the broader biomedical computing community.
* NA-MIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NA-MIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair. The training and dissemination will continue with a DTI workshop at the forthcoming Human Brain Mapping meeting and activities at MICCAI 2007, among others.

==Q2==
'''A brief statement - (one page per question, max) addressing each of the questions listed below. These are the questions that we have been asked to address in our report. Our goal in asking for this information is to be able to produce a report that reviews the program as a whole. Your view, from the vantage point of the center you direct, is critical to our work. In addition, your answers will provide us with more information that we can use in our discussion with program staff on June 11th. We know that some of this information can be found on your websites, so in those cases a link to the information would be most helpful.'''

===Q2.1 To what extent does the vision and direction of the NCBC initiative promote biomedical computing?===

The RFA for the creation of the NCBC program laid out a very explicit vision.

The NCBCs are to be the core of the networked national effort to build the computational infrastructure for biomedical computing in the US. From the [http://www.bisti.nih.gov/ncbc/ NIH website] in 2004: Four new National Centers for Biomedical Computing (NCBC) will develop and implement the core of a universal computing infrastructure that is urgently needed to speed progress in biomedical research. The centers will create innovative software programs and other tools that enable the biomedical community to integrate, analyze, model, simulate, and share data on human health and disease. The original [http://grants1.nih.gov/grants/guide/rfa-files/RFA-RM-04-003.html RFA] stated: 1. The software should be freely available to biomedical researchers and educators in the non-profit sector and 2. The terms of software availability should permit the commercialization of enhanced or customized versions of the software.

As the amount of data produced by biomedical researchers is increasing at an ever accelerating pace, the relative importance of computing as an integral part of analysis is increasing as well. In addition both data and analyses are getting more and more complex. Increasingly, science is performed by interdisciplinary teams at multiple locations. Industrial strength research platforms are one of the emerging needs of biomedical research. This need was only been partially addressed before the creation of the NCBC program. The NCBC centers are in an outstanding position to develop stable, maintainable, and expandable software to address these needs.

In addition to building the infrastructure for biomedical computing, NCBCs play an important role in fostering a community of computational scientists dedicated to solving problems in the biomedical domain. In its short three-year life span, NA-MIC NCBC has contributed to this vision by holding numerous workshops and tutorials on open source software for biomedical image analysis, by supporting the Insight Journal, a peer-review venue for publications accompanied with open-source implementation, and by engaging a large number of graduate students in Computer Science programs in biomedical computing research. By creating high-profile bio-computation programs, the NCBC initiative brings the biomedical computing as a field of research to the attention of the computation community and actively promotes collaborations between computational and biological sciences.

The participants in our center signed on for this project because they believe in the vision of developing a universal computing infrastructure for medical image computing. NA-MIC is creating an open source platform that embodies this vision for the field of medical image computing. We have settled on a very liberal open source license for our software plattform. All the components of that plattform, called the NA-MIC kit, are distributed under a BSD style licence without restrictions on commercial use. Other centers have adopted different strategies to address the requirement for open source and to enable commercial use, as required in the original RFA; we believe the NA-MIC liberal license approach minimizes barriers to wide adoption and is will maximize return on the NIH investment. The availability of software platforms commoditizes infrastructure for research and allows individual researchers to spend more time on their core research. Over time, this will promote biomedical computing by lowering the hurdle for scientists to use the technology.

===Q2.2 In what ways has the NCBC initiative advanced biomedical computing?===
Ron:(Marty edits also)

It is too early to say how the NCBC initiative advanced biomedical computing, though the results thus far are promising. Four of the NCBC's have been funded since October 2004 (less than three years), and funding for the other three began in late 2005. Center efforts of this size need significant time and effort to get organized and to synchornize activities of all participants. This startup effort is where the primary focus of the centers has been until now. There are early signs that some of the centers are beginning to emerge from this phase of their evolution and turning toward activities aimed at the field at large. However, it will be several years before the full impact of this program will become visible.

Below is a more detailed discussion of the specifics of this evolution from the vantage point of NA-MIC

(Tina)

Answer:http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#4._Impact_and_Value_to_Biocomputing

In NA-MIC's third year, it is clear that NA-MIC is developing a culture, environment, and resources to foster collaborative research in medical image analysis that draws together mathematicians, computer scientists, software engineers, and clinical researchers. These artefacts of NA-MIC impact how NA-MIC operates, make NA-MIC a fulcrum for NIH funded research, and draws new collaborators from across the country and around the world to NA-MIC.

*Impact within the Center

**Within the center, the NA-MIC organization is nimble, forming ad hoc distributed teams within and between cores to address specific biocomputing tasks. Information is shared freely on the NA-MIC Wiki, on the weekly Engineering telephone conferences, and in the NA-MIC Subversion source code repository. The software engineering tools of CMake, Dart 2 and CTest, CPack, and KWWidgets facilitate a cross platform software environment for medical image analysis that can be easily built, tested, and distributed to end-users. Core 2 has provided a platform, Slicer 3, that allows Core 1 to easily integrate new technology and deliver this technology in an end user application to Core 3. Core 1 has developed a host of techniques to apply to structural and diffusion analysis which are under evaluation by Core 3. Additionally, Core 3 members are heavy users of the Slicer 3 software and they provide ongoing feedback to Core 2 with respect to features that are optimal and those that need to be refined. They also assist in refining the user interface of this software platform. Core 3 members are also actively using the software and new tools developed by applying them to address scientific questions of interest, as is evident from the new publications using these tools. Major NA-MIC events, such as the annual All Hands Meeting, the Summer Project Week, the Spring Algorithms meeting, and Engineering Teleconferences are avidly attended by NA-MIC researchers as opportunities to foster collaborations.

*Impact within NIH Funded Research

** Within NIH funded research, NA-MIC continues to forge relationships with other large NIH funded projects such as BIRN, caBIG, NAC, and IGT. Here, we are sharing the NA-MIC culture, engineering practices, and tools. The BIRN infrastructure, built on widely-accepted grid middleware, allows NA-MIC researchers to share data, access computational resources and provides a rich collaborative environment through a science portal. caBIG lists the 3D Slicer among the applications available on the National Cancer Imaging Archive. NAC and IGT use the NA-MIC infrastructure and are involved in the development of the 3D Slicer. BIRN recently held an event modeled after the NA-MIC Project Week. NA-MIC has become a resource on open source licensing to the medical image analysis community.

NA-MIC is also attracting NIH funded collaborations. Two grants have been funded under PAR-05-063 to collaborate with NA-MIC: Automated FE Mesh Development and Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI. Five additional applications to collaborate with NA-MIC via the NCBC collaborative grant mechanism are under consideration. Additional grant applications submitted under other calls are planning to use and extend the NA-MIC tools. The first collaborative R01 from a previous driving biological problem has also been submitted by Drs. Shenton and Saykin to follow up on important tool development ideas that will combine multi-modal imaging to address specific questions relevant to brain circuitry abnormalities in schizophrenia.

*National and International Impact

**NA-MIC events and tools garner national and international interest. There were nearly 100 participants at the NA-MIC All Hands Meeting in January 2007, with many of these participants from outside of NA-MIC. Several researchers from outside the NA-MIC community have attended the Summer Project Weeks and the Winter Project Half-Weeks to gain access to the NA-MIC tools and people. These external researchers are contributing ideas and technology back into NA-MIC.

**Components of the NA-MIC kit are used globally. The software engineering tools of CMake, Dart 2 and CTest are used by many open source projects and commercial applications. For example, the K Desktop Environment (KDE) for Linux and Unix workstations uses CMake and Dart. KDE is one of the largest open source projects in the world. Many open source projects and commercial products are benefiting from the NA-MIC related contributions to ITK and VTK. Finally, Slicer 3 is being used as an image analysis platform in several fields outside of medical image analysis, in particular, biological image analysis, astronomy, and industrial inspection.

**NA-MIC co-sponsored the Workshop on Open Science at the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2006 conference. The proceedings of the workshop are published on the electronic Insight Journal, another NIH-funded activity.

**Over 50 NA-MIC related publications have been produced since the inception of the center.

===Q2.3 Are the NCBCs interfacing appropriately?===
(MARTY EDITS - need to address NCBC's interfacing ONLY)

Outreach across NCBCs is an effective use of resources. Initial efforts are ongoing and include cross-center use of software tools such as SimBios/SimTk actively using NAMICs VTK toolkit, and the use of the promising Software and Data Integration Working Group (SDIWG). In addition, NA-MIC has interactions with CCB and Simbios. NA-MIC also interfaces with a number of large-scale efforts which are not NCBC but are comparable in scale: BIRN, several national resource centers, and to some extent, with CaBIG. The NCBC's are thus interfacing appropriately.

Within the next few years there will be a need to increase NCBCs interfacing significantly. This will require either reallocation of resources or the allocation of new, additional resources. Due to the large variety of approaches adopted by the different centers it will likely be necessary to allocate significant amounts of time by senior leadership at the centers for identifying objectives of such efforts and by the engineering cores to actually execute the plans. A serious effort in this direction will probably require the allocation of multiple FTE's.

===Q2.4. What new collaborations have been formed through the NCBC initiative?===
(MARTY NEW EDITS)
NA-MIC’s structure and organization has facilitated many new collaborations. NA-MIC is a distributed center, bringing together mathematicians, computer scientists, software engineers, and clinicians from multiple sites. This distributed structure provides two types of new collaborations within NA-MIC: new collaborations between cores and new collaborations within cores. For between core collaborations, many of the algorithm and engineering core researchers had not collaborated previously with the researchers in either the first or second round of Driving Biological Projects (DBPs). Thus, the NCBC provides a unique opportunity for the algorithm and engineering core researchers to gain clinical insight and to adapt and tune their algorithms and tools to new clinical contexts. Conversely, the DBPs have gained access to algorithms and tools that they previously did not have. Additionally, new collaborations among DBPs and Core 1 members is an important step in bringing together computer scientists and clinical researchers and we have been most successful here. Similarly, many of the algorithm core researchers and engineering core researchers had not previously collaborated. Thus, the NCBC exposed the researchers in the algorithm core to the tools and engineering practices of the engineering core and exposed the researchers in the engineering core to the computational techniques and data structures utilized by the algorithms core. For within core collaborations, many of the researchers within the algorithm core had not previously collaborated. Through NA-MIC, these researchers have been able to cooperate and also amicably compete to address the issues brought forth by the DBPs.

Below is a list of new collaborations within NA-MIC. This list was compiled from the complete list of [[NA-MIC_Collaborations | NA-MIC Collaborations]] and project lists from the 5 NA-MIC [[Engineering:Programming_Events |Project week events]]. Best effort was made to filter these lists down to just the new collaborations (groups of researchers) formed under NA-MIC.

[TINA a Question from MARTY: IS BWH CORE 3 I THINK SO?? I THINK SO, SO WHO IS HARVARD?? IS THIS CF?? HAVE A LOOK BELOW]

* Georgia Tech + UC Irvine – Rule based segmentation algorithm for DLPFC
* Georgia Tech + Kitware - Knowledge-based Bayesian classification and segmentation
* BWH + MIT + Kitware - Brain tissue classification and subparcellation of brain structures
* Georgia Tech + UNC + BWH - Multiscale shape segmentation techniques
* BWH + Dartmouth + UNC + Georgia Tech - Shape analysis of the caudate and corpus callosum
* Georgia Tech + UNC + BWH - Spherical wavelet based shape analysis for Caudate
* Georgia Tech + UNC + BWH - Multiscale shape analysis of the hippocampus
* Dartmouth + UNC + BWH - Shape analysis of tbe hippocampus
* Utah + UNC + BWH - Automated shape model construction
* Dartmouth + Isomics - Neural substrates of apathy in schizophrenia
* Georgia Tech + GE Research + Kitware - Spherical Wavelet Transforms
* Georgia Tech + UNC - Shape analysis with Spherical Wavelets
* Utah + UNC - Adaptive, particle-based sampling for shapes and complexes
* UNC + Utah + ???Harvard - Tensor estimation and Monte-Carlo simulation
* Harvard + MIT + UNC - Corpus Callosum Regional FA analysis in Schizophrenia
* Dartmouth + MGH + Isomics + BWH - Integrity of Fronto-Temporal Circuitry in Schizophrenia using Path of Interest Analysis
* MGH + Isomics - ITK implementation of POIStats, and Integration into Slicer3
* UC Irvine + MGH + UNC + MIT - DTI Validation
* Utah + UNC + GE Research - DTI Software and Algorithm Infrastructure
* Utah + BWH - Tensor based statistics
* Utah + BWH - Diffusion tensor image filtering
* MGH + Dartmouth + Kitware + GE Research - Non-rigid EPI registration
* Dartmouth + BWH - Neural Substrates of Working Memory in Schizophrenia: A Parametric 3-Back Study
* Dartmouth + BWH - Brain Activation during a Continuous Verbal Encoding and Recognition Task in Schizophrenia
* Dartmouth + BWH - Fronto-Temporal Connectivity in Schizophrenia during Semantic Memory
* UC Irvine + Toronto - Imaging Phenotypes in Schizophrenics and Controls
* MIT + Isomics + GE Research + Kitware - fMRI statistics software
* MIND + Isomics + MGH - Analysis of Brain Lesions in Neuropsychiatric Systemic Lupus Erythematosis
* JHU + Queen's + BWH + Georgia Tech - Segmentation and Registration Tools for Robotic Prostate Interventions
* UNC + GE Research - Longitudinal MRI study of early brain development
* BWH + Kitware + MIT - Velocardiofacial Syndrome (VCFS) as a genetic model for schizophrenia
* UNC + GE Research + BWH - DTI population analysis
* Georgia Tech + BWH - Geodesic tractography
* BWH + Queen's + GE Research - Display optimization
* UCSD + Isomics - Dendritic Spine Morphometrics
* <others to be filled in>

NA-MIC has also attracted researchers from the field who were not originally part of NA-MIC. Some of these new collaborations are formally organized using the NIH NCBC Collaborative R01 program. But other collaborations are being driven solely by the opportunity to share resources, techniques, capabilities, and ideas.

Below is a list of new collaborations between external researchers and NA-MIC. Again, best effort was made to only list the new collaborations with external parties.

* Mario Negri + GE Research – Integration of vmtk with Slicer 3
* Iowa + Isomics - Finger Bone Biomechanics
* CalTech + Kitware - Systems Biology and Genomic Science
* BWH + Wake Forest + Virginia Tech - Alcohol Stress in Primates
* BWH + MGH - Radiation Treatment Planning'
* Iowa + Kitware + BWH - Non Linear Registration Tools
* Northwestern + Isomics - Radiology Translation Station
* Harvard IIC + Isomics + GE Research - Astronomy Analysis and Visualization
* Virginia Tech + BWH - Applying EMSegmenter to nonhuman primate neuroimaging
* JHU + Queen's + BWH - Brachytherapy needle positioning robot integration
* Iowa + Kitware + BWH - Nonrigid registration
* Iowa + BWH - Developing electronic atlas
* Iowa + Kitware - GUI for nonridig image registration
* Canary Islands Technological Institute + Isomics + GE Research - DICOM Query/Retrieve
* Canary Islands Technological Institute + GE Research - Block matching registration
* UNC + Duke University Medical Center - DTI tractography analysis in depression study
* <others to be filled in>

The collaborative nature of NA-MIC is exemplified by the attendance at the NA-MIC All Hands Meeting and the NA-MIC Summer Project Week. Researchers from within and external to NA-MIC come together at these two events to forge collaborations. At the 2007 NA-MIC All Hands Meeting alone, there were 96 attendees: 56 NA-MIC researchers, 32 NA-MIC collaborators from 13 institutions, and 8 members of the External Advisory Board and NIH. At the Project Half-Week run in conjunction with the All Hands Meeting, there were 38 projects: 16 initiated from the algorithm core, 10 specific to the engineering core, and 11 from external collaborators.

More detailed information on collaborations as well as Project Week events can be found at:
* http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations
* http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events

===Q2.5. What new training opportunities have the centers provided?===

(MARTY: My questions is that generally training also refers to NEW investigators coming into the field, should there be some mention of that as well below?)

**Answer: http://www.na-mic.org/Wiki/index.php/Training:Main

* The requirement for all NCBC's to dedicate funds for training provides the opportunity to develop a targeted and deep portfolio of training resources. The unique perspective of providing these training opportunities and resources specifically targeted to a multi-disciplinary audience of basic and clinical biomedical scientists, computer scientists and medical imaging scientists is fostered by the NCBC Program. Traditional funding by NIH research grants results in allocation of all funds into the primary research; NIH training grants allocate funding for the support of trainees; the few education grants are restricted in budget and overhead thus severely limiting the quality and quantity of educational resources that can be offered. The NCBCs have created a new opportunity for our cadre of experienced clinician scientists, computer scientists and medical image analysis experts affiliated with our large centers to be supported to work on outreach activities.

* Within NA-MIC, this perspective has given rise to a thriving training program that supports the biomedical research community within NAMIC, across the NIH community and around the world. The strong demand for our training resources is evident from the large number of hits to our training web pages, from the rapid enrollment in all offered workshops, and the positive feedback from participants. We believe, and our belief is supported by the documented backgrounds of our workshop attendees, that a key aspect of our training materials that makes them useful to the community is that they are learner-centered, goal-oriented, and targeted to bridge the gaps in technical knowledge and language that exist between basic and clinical biomedical scientists, computer scientists and medical imaging scientists. For example, a tutorial that teaches how to use Slicer to register two images includes not only the necessary details of how to implement the algorithm, but also the conceptual framework for the registration approach, the mathematical underpinnings of the algorithm and a detailed anatomical approach for visually inspecting and refining the registration. This rich, but simple approach provides a consistently educational experience for every new user of the NAMIC toolkit.

* NAMIC supported new training opportunities are developed to maximize impact on the wider scientific community. The primary vehicle for this is "Slicer 101", our portfolio of Slicer training tutorials (http://www.na-mic.org/Wiki/index.php/Slicer:Workshops:User_Training_101). We have focused our efforts on making all our tutorial materials available via the NA-MIC Wiki as downloadable Powerpoint presentations and accompanying curated, anonymized datasets. The tutorials are all carefully tested on multiple computer platforms and by our team before being used in live Workshops (http://www.na-mic.org/Wiki/index.php/Training:Events_Timeline). Refinements are made based on the feedback of the audience and our experience during the teaching sessions. The final product of our work allows any new users, regardless of educational background, to not only use the NAMIC tools and algorithms, but to understand what they are doing and why. To date we have had over 7,880 hits to the Slicer 101 webpage.

* NAMIC supported Workshops are another unique venue for multi-disciplinary training. In addition to the all the points made regarding the content of the training materials, the 14 Workshops run by the NAMIC Training core over the past 3 years have each provided the opportunity for new connections to be made among basic and clinical biomedical scientists, computer scientists and medical imaging scientists. All our Workshops provide opportunities for formal and informal discussions among attendees of diverse backgrounds and strengths. These hands-on, interactive workshops allow participants to translate concepts of medical image processing into skills through instructor-led training. The simplicity of our approach, and the exceptional quality of the NAMIC toolkit, ensures a very high success rate for knowledge and skill acquisition. We estimate that 370 people from 52 different universities and companies attended our Workshops between 2005 and 2006.

* We are currently focusing our efforts on reaching a wider community by delivering a more didatic based Workshop in conjunction with the upcoming Organization for Human Brain Mapping meeting in Chicago next week. We held our enrollment to 50 so that we could offer the same hands-on interactive training experience to the attendees and our registration filled within a few weeks of the offering being posted. Tenatively, we anticipate that more than 12 countries and 14 states within the US will be represented at this upcoming Workshop.

* A final point is that this commitment and focus on training permeates all aspects of the NAMIC program. All large gatherings of NAMIC personnel including All Hands Meetings, Programming/Project Weeks, and Core meetings provide venues for our culture of training to be expressed. Each gathering creates an opportunity to build bridges between our participating disciplines and to improve the communication skills of each member. This culture includes implicit aspects such as a supportive and collegial environment that encourages questions and critical feedback, as well as explicit aspects such as encouraging junior level participants to make presentations and scheduling educational presentations from domain experts within and outside of the community. We believe that the positive attitude towards sharing knowledge and skills is fostered in all who are associated with our Project.

===Q2.6. What changes could make the program more effective in the future?===

* ''National Visibility''

The overall objective statement of the NCBC program calls for building computational infrastructure for the nation's biomedical research efforts -- this ambitious goal includes both computational and biomedical science, areas in which the existing centers have extensive experience, but also in nationwide "marketing" efforts where the scientific community is less adept. Several centers, including NA-MIC, have developed novel approaches to this problem through their training and dissemination cores, but as the output of the centers grows along with the number of potential users in the community and the potential impact, these critical resources will be increasingly strained. There are several approaches the overall program could take to address this issue including supplemental funding to host workshops or conferences, providing small travel grants for researchers or students to visit the centers, or actively encouraging a wider range of NIH funded researchers to adopt the tools generated by the NCBCs. In particular, collaboration PAR has been very effective as a mechanism for encouraging busy scientists to consider adopting the NCBC tools and is an excellent example of how the program can extend the impact of the basic investment in scientific infrastructure.

* ''Local Autonomy''

The program should avoid adding extra layers of uniformity to what are fundamentally unique centers. The NCBC program has successfully established a distributed network of centers drawing on the expertise of some of the nation's leading researchers drawn to the program for the opportunity to develop and apply their know-how to this ambitious effort. This rich environment, predictably, yields a diversity of approaches and organizational structures as each of the centers works to implement their particular vision of how to fulfill the overall mission of the NCBC program. Preserving the vitality of the effort depends on retaining this autonomy as each center strives to meet the individual objectives suited to their communities. The program needs well defined goals that each center must meet, but the overall program should facilitate the individual solutions of the center's leadership.

* ''Stability''

One critical mission of the NCBC program is the creation of infrastructure. These large-scale efforts require time-horizons that are incompatible with the current framework of many programs at NIH. It might take two or three iterations until a software package is sufficiently mature to be attractive to a larger community of scientists. Large software platforms require engineering staff of 10-20 in addition to the biomedical scientists developing functionality aimed at solving a particular problem. Many of the NCBCs are working on several packages. This results in underfunded projects which compromises the timeliness and performance of the resulting software.

It would be advisable to increase funding for the engineering and outreach activities of the centers and provide the funding in a reliable way. The continous stream of cuts from of the original budget has made this discrepancy even more pronounced. In 2007/2008 we will receive only 77.7% of the money that was budgeted in the application for that year. Following the RFA guidelines, the original budget did not contain adjustments for inflation. Furthermore, in deviation from the way that most NIH programs are funded, the budget was frozen in total dollars not direct dollars. Institutional overhead rates, and fringe and benefit rates have increased for several of the NA-MIC participants during the last three years and have resulted in further decreases in the amount of money available to actually do research.

===Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing?===

* ''Greater Start Up Time and Steeper Learning Curve Than Anticipated''

Bringing together computer scientists, engineers, and biomedical researchers, with diverse interests, training, and background, for the purpose of working on a set of biological problems, is no easy feat and, in retrospect required a steeper learning curve than was anticipated. This steeper learning curve is understandable, since the main focus initially was on developing alliances among the cores in order to increase awareness about the kinds of tools needed for the specific imaging problems posed by the biomedical researchers who were driving the biological problems. The first year of the grant, as noted in our annual report, thus reflected a "core" emphasis, as an interdisciplinary team was brought together, many members for the first time. It was not until the second year of the grant that the focus on a "core" emphasis shifted to a focus on "themes", which cut across "core" boundaries. While this shift was viewed as part of a natural evolution, now that we know this, we can help guide future NIH efforts by suggesting that specific projects/clinical applications should be highlighted in the first few months of the grant, based on meetings among core members, so as to facilitate a focus on clinical applications from the outset.

Such an early emphasis on clinical applications/problems would also facilitate an early focus on the development and application of computational tools, which could be more closely aligned with specific clinical problems and applications. This would breakdown artificial barriers that a "core" focus involves, which, while seemingly an inherent part of the initial stages, could be curtailed by highlighting early the need to focus on specific needed applications. In this way, the needed applications of the driving biological problems could form natural groupings that involve members from all cores, and work groups could be set up from the beginning that reflect a "theme"/"application" approach. This would also assist in more communication between core members, which would also likely facilitate ongoing communication among computer scientists, engineers, and biomedical researchers.

* ''Developing Robust Software for Advanced Applications is Difficult''

Creating industrial-strength software solutions to support scientific investigations is time consuming and requires skill not usually found in the academic environment. NA-MIC has been successful at bringing in commercial software development expertise to help accomplish the center's goals (GE, Kitware, Isomics) but these resources are routinely stretched well beyond the allocated budgets due to the many research directions of the center's scientists. When considering biomedical computing projects, the NIH must not attempt to short change the development process or the resulting systems run a greater risk of being difficult to maintain, difficult to scale up, and incompatible across systems.

An excellent example of the level of effort needed for a successful, extensible, cross-platform product is the National Library of Medicine's Insight Toolkit (ITK). The organizational meeting for ITK was held in October 1999. Between October 1999 and October 2002, fifty developers contributed code from six prime contractors (GE, Kitware, Insightful, UNC, Utah, and UPenn) and four sub-contractors (BWH, UPenn, Pitt, and Columbia) to produce ITK Version 1.0. To date, over $13.5 million has been awarded by the NIH for the development, use, and expansion of ITK. That total includes 20, one-year contracts that were given to early adopters of ITK. When assessing the level of effort expended on ITK, it is important to consider that ITK's funding did not cover algorithm, graphical user interface, or visualization developments. ITK costs only funded the integration of existing methods into a common library. Developing end applications, involving user interfaces and visualizations tailored for clinical users, requires significant additional effort.

* ''Algorithm Development Needs to be Interactive and Not Sequential''

In reviewing the last three years of the driving biological problem, schizophrenia, it is evident that tool development that involved multiple interactions among members of Core 1 (Computer Scientists), 2 (Engineers), and 3 (Driving Biological Problem), at all stages of development, led to the development of computational tools that were both more tailored to the specific applications needed by Core 3 members, as well as to the development of tools that were more optimized for general use. This interactive mode of tool development is in contrast to tool development that proceeded more sequentially, where one or several members of Core 1 and 3 met, and then Core 1 proceeded with what their understanding was of the problem and went off and developed a tool with very little further input from Core 3 until the tool was delivered. The latter approach often resulted in delays in receiving the tool, as there was less communication between Core 1 and Core 3 members in these instances, and often the tool did not really meet the specific needs of the application without further work. In the future, and based on this experience, NIH initiatives should emphasize the importance of encouraging a more "interactive" approach to tool development and to discouraging what is termed here as a more "sequential" approach to tool development. With a more interactive approach, progress can be more readily evaluated at each phase of tool development, and input and testing can be provided based on more communication among members of Core 1, 2, and 3. A more "interactive" model is also far more responsive to the needs of the driving biological problem, and also keeps the focus on the clinical application.

Focusing on interactions across core members will also likely facilitate breaking down the steep learning curve inherent in early interactions across cores members (see above).

* ''Funding''

As mentioned above, there is a serious mismatch between the objectives set by NIH and the resources available to address them. A further aggravation were the additional cuts imposed over the last three years, combined with a "requirements creep". The future increased interaction between the centers will require either additional resources or decommittment for existing activities. If the centers are as successful as envisioned, an increasing number of scientists will want to interact with the scientists at the centers which will also require additional resources.

NIH will need to allocate significant additional resources to the centers in order to fulfill the vision of a network of centers of biomedical computing at the center of an ever increasing network of biomedical scientific programs benefiting form their software and services.

=='''Q3:A list of publications and/or software tools produced by the Center.''' If this information is provided in your progress report or is available on your website, a link will be sufficient. We are especially interested in your assessment of the maturity of your software tools and the impact they are having on the scientific community.===

A3:
*NA-MIC Publications are available here: http://www.na-mic.org/Wiki/index.php/Publications.
*NA-MIC Software Tools are available here: http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit
The Center has created and extended a number of software tools to handle some of the key problems in medical imaging analysis and to deliver these computational technologies via a suite of applications, toolkits, and infrastructure facilities. A summary description of these tools includes:
* Slicer3 (application) - a application platform for deploying imaging technologies, newly architected with an execution model facilitating integration, work flow, and large scale computing. While Slicer3 is the newest addition to the NAMIC Kit, it is built on pre-existing, mature toolkits so that the application is relatively mature, and is already in use. Because Slicer3 supports plug-in modules, active development is proceeding to create and package various modules for dissemination to the NAMIC community.
* ITK (toolkit) - a mature system for image analysis, registration and segmentation (initially created in 1999). ITK is in use worldwide for medical imaging research and development.
* VTK (toolkit) - a mature system for visualization, graphics, volume rendering, and interaction (initially created in 1993). VTK is used worldwide for research, teaching and commercial development.
* DART (computational infrastructure) - a key component of the NAMIC quality control process, DART is used to coordinate and report the software testing process. It was created in the first year of NAMIC and is in constant use, therefore a mature system.
* CMake/CTest/CPack (computational infrastructure) - CMake and CTest are relatively mature systems used to manage the building and testing of software across diverse computer platforms. CMake is used worldwide by some of the [http://lwn.net/Articles// world's largest open source systems such as KDE]. CPack, a recent addition to the NAMIC kit, is used to simplify the packaging and dissemination of software across platforms. Thus in NAMIC we can easily deploy our software across Windows, Linux, Unix, and Mac platforms.
* Other tools (computational infrastructure) - Many other software tools are used to support the development of advanced imaging applications, and to assist with large scale computing, including
** Teem - image processing tools (mature)
** KWWidgets - Open source, cross platform GUI toolkit (mature, but development continues to support workflow).
** BatchMake - Support large-scale computing, including grid computing, for performing large population studies and statistical analysis (under active development).
** GridWizard - an application scheduler aimed at allowing researchers to easily harness the power of large computational grids. It lets you run tens of thousands of commands simultaneously on multiple clusters of computers by typing a single command, without writing scripts. It can be used by itself, and is currently being integrated with Slicer3 and as part of a web-based portal environment. (http://www.na-mic.org/Wiki/index.php/Slicer3:Grid_Interface)

These tools address key problems in imaging including segmentation, registration, visualization, and shape analysis; or provide facilities supporting researchers and developers who wish to create advanced software applications. One of key characteristics of NAMIC is that we treat the development of advanced medical image analysis software holistically; that is, the complete cycle of algorithm design, efficient impmentations, quality control and dissemination are needed to effectively address challenges provided by the driving biological problems. Examples of how these tools are being used include the following:

(a) Segmentation: Here there are a variety of tools of varying degrees of maturity ranging from the EM Segmentor (a widely distributed, mature algorithm included in Slicer3 as an application plug-in) to more recent work on DTI segmentation based on directional flows which are Matlab and C++ based. Powerful mature tools such as Bayesian segmentation have been recently included in Slicer (and have been available in ITK for some time now) which can be combined with very recent work on the semi-automated segmentation of the DPFC done in collaboration with Core 3 researchers. Further, tools previously developed by NAMIC researchers which have had a wide distribution have been put into Slicer. For example, geometric based segmentation methods (some of which were included in packages marketed by GE) were tailored for cortical segmentation, and included in the Slicer, and in fact even improved with the inclusion of statistical based approaches.

(b) Registration: Similar remarks can be made for registration in which we have a spectrum ranging from very mature methods to very recent ones which are still being tested. In particular, mature widely distributed methodologies for rigid registration are now included in ITK, as well as spline-based registration methodologies. These are well-tested methods which have been made accessible to the general imaging community. Newer methodologies such as those based on optimal transport for elastic registration are being included in ITK. NAMIC has also pushed for fast implementations of its algorithms to be used on cheap widely available platforms. Taking a cue from the game industry, some algorithms have been ported to GPUs (graphics cards) which are being employed now as computing devices. This has led to a speed-up of almost two orders of magnitude on some of the registration algorithms being tested.

(c) Shape Analysis: Again a number of methodologies have been developed and implemented with varying levels of maturity. Shape methodologies based on spherical harmonics are quite mature, and are available in pipelines developed by NAMIC researchers and have been distributed to the general community. A newer spherical based wavelet shape analysis package has been put into ITK, which also drives a novel shape-based segmentation procedure. More globally based spherical harmonic ideas have been combined with the multi-resolution spherical wavelet approach as a statistical shape based package for schizophrenia. This general technique may be used for other purposes as well, and is presently being ported to some work being done on the prostate. Work has also been accomplished on particle based approaches to this important problem area with the code put into ITK. Many times we work with a Matlab/C++ initial version of our codes, then move to ITK, and finally to Slicer. However, even at the Matlab/C++ stage, algorithms have been distributed and used in a clinical setting (for example, rule-based brain segmentation approaches).

(d) Diffusion Weighted Image (DWI) Analysis: A number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical analysis of fiber bundles have been contributed to the NAMIC toolkit. Some of these tools have been already integrated into diffusion dedicated ITK package with GUI as part of the NAMIC toolkit (e.g., FiberViewer-UNC) and into the Slicer platform (BWH tractography, clustering). The impact of NAMIC DWI analysis activities is best characterized by most recent journal articles and journal articles in print. The application of the NAMIC FiberViewer tool (UNC) in large clinical studies at UNC and Duke are in print (Taylor et al, Gilmore et al., Cascio et al.). Clinical application of the Slicer DTI package by BWH/MIT is reported in O’Donnell et al. Kuroki N. et al. and Nakamura et al.. The research by MGH is found in two journal publications by Tuch et al.. The description of the methodologies also appeared or will appear in peer reviewed journals (Corouge et al., Fletcher et al., Corouge et al.). New methods in development (Finsler metric GT, volumetric PDE based path analysis Utah, stochastic tractography BWH), and path of interest MGH, are currently tested on Core-3 DBP data, with the goal to give recommendations on which type of solution is appropriate to solve specific clinical analysis questions.

(e) Visualization: Core 2 researchers involved with NAMIC now (e.g., the founders of Kitware) were at the forefront of developing VTK (and of course ITK). Thus here we are considering technologies which are at a commercial level of development, and used at thousands of sites. Algorithms developed at NAMIC have driven new directions for these packages. Newer visualization methods, for example, the conformal flattening procedure have been ported to an ITK filter and is in the NAMIC Sandbox. Quasi-isometric methods for brain flattening from the MGH Free Surfer have become part of the NAMIC enterprise as well. These flattening procedures are very easy to use, and may also be employed for registration. Code for the control of distortion of the area in flattening has been incorporated which give area-preservation with minimal distortion. The techniques may be also used for several other purposes included automatic fly-throughs in endoscopy (incorporated into Slicer), and for texture mappings for general visualization purposes.

==Logistics==
When completed, the information should be sent to:

Gwen Jacobs, PhD

Professor of Neuroscience

Asst. CIO and Director of Academic Computing

1 Lewis Hall

Montana State University

Bozeman, MT 59717

406-994-7334 - phone

406-994-7077 - FAX

gwen@cns.montana.edu <mailto:gwen@cns.montana.edu>

2007 Materials for NCBC Program Review

2007-06-08T12:02:03Z

Shenton: /* Q2.4. What new collaborations have been formed through the NCBC initiative? */

==Materials requested for NCBC Program Review==

These are due to Gwen Jacobs by Friday, June 08, 2007.

=='''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''==

Summary: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#1._Introduction

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with respect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs include a focus on Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

Highlights: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#3._Highlights

The third year of the NA-MIC project saw continued development and dissemination of medical image analysis software. The current progress is clearly characterized by a significant increase in the application of Core-1 and Core-2 tools to image data provided by the Core-3 DBP groups. The main reasons for this progress is two-fold, first there are several new methods that are out of the prototype stage and ready to be applied to large sets of imaging datasets, and second there are new high-resolution imaging data from high-field scanners that are more appropriate for the new tools than historical data with often very coarse slice resolution.

With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NA-MIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NA-MIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

*Advanced Algorithms
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NA-MIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NA-MIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The NA-MIC toolkit includes a comprehensive set of modules for analysis of diffusion weighted images (DWI), including improved calculation of tensors, interpolation, nonlinear deformation and statistics on tensor fields, novel methods for tractography and for optimal path finding, and clustering of sets of streamlines.
* A quantitative tractography package for user-guided geometric parametrization and statistical analysis of fiber bundles (FiberViewer) has been contributed to the NAMIC toolbox. This tool used in several ongoing clinical DTI studies.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NA-MIC Sandbox in preparation for formal acceptance into the NA-MIC Kit.
*Technology Deployment Platform: Slicer3
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NA-MIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* Slicer3's execution model is also allowing researchers to access large computational resources. Two applications that support the use of such resources, BatchMake and GridWizard, have been recently integrated with Slicer3 for specific modules. The GUI for the modules that are to be run via these computational support applications allow for the creation of large population studies or parametric studies of an algorithm.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NA-MIC Kit, and are now available to all other modules built on the Slicer3 framework.

*Outreach and Technology Transfer
Cores 4-5-6 continue to support, train and disseminate to the NA-MIC community, and the broader biomedical computing community.
* NA-MIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NA-MIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair. The training and dissemination will continue with a DTI workshop at the forthcoming Human Brain Mapping meeting and activities at MICCAI 2007, among others.

==Q2==
'''A brief statement - (one page per question, max) addressing each of the questions listed below. These are the questions that we have been asked to address in our report. Our goal in asking for this information is to be able to produce a report that reviews the program as a whole. Your view, from the vantage point of the center you direct, is critical to our work. In addition, your answers will provide us with more information that we can use in our discussion with program staff on June 11th. We know that some of this information can be found on your websites, so in those cases a link to the information would be most helpful.'''

===Q2.1 To what extent does the vision and direction of the NCBC initiative promote biomedical computing?===

The RFA for the creation of the NCBC program laid out a very explicit vision.

The NCBCs are to be the core of the networked national effort to build the computational infrastructure for biomedical computing in the US. From the [http://www.bisti.nih.gov/ncbc/ NIH website] in 2004: Four new National Centers for Biomedical Computing (NCBC) will develop and implement the core of a universal computing infrastructure that is urgently needed to speed progress in biomedical research. The centers will create innovative software programs and other tools that enable the biomedical community to integrate, analyze, model, simulate, and share data on human health and disease. The original [http://grants1.nih.gov/grants/guide/rfa-files/RFA-RM-04-003.html RFA] stated: 1. The software should be freely available to biomedical researchers and educators in the non-profit sector and 2. The terms of software availability should permit the commercialization of enhanced or customized versions of the software.

As the amount of data produced by biomedical researchers is increasing at an ever accelerating pace, the relative importance of computing as an integral part of analysis is increasing as well. In addition both data and analyses are getting more and more complex. Increasingly, science is performed by interdisciplinary teams at multiple locations. Industrial strength research platforms are one of the emerging needs of biomedical research. This need was only been partially addressed before the creation of the NCBC program. The NCBC centers are in an outstanding position to develop stable, maintainable, and expandable software to address these needs.

In addition to building the infrastructure for biomedical computing, NCBCs play an important role in fostering a community of computational scientists dedicated to solving problems in the biomedical domain. In its short three-year life span, NA-MIC NCBC has contributed to this vision by holding numerous workshops and tutorials on open source software for biomedical image analysis, by supporting the Insight Journal, a peer-review venue for publications accompanied with open-source implementation, and by engaging a large number of graduate students in Computer Science programs in biomedical computing research. By creating high-profile bio-computation programs, the NCBC initiative brings the biomedical computing as a field of research to the attention of the computation community and actively promotes collaborations between computational and biological sciences.

The participants in our center signed on for this project because they believe in the vision of developing a universal computing infrastructure for medical image computing. NA-MIC is creating an open source platform that embodies this vision for the field of medical image computing. We have settled on a very liberal open source license for our software plattform. All the components of that plattform, called the NA-MIC kit, are distributed under a BSD style licence without restrictions on commercial use. Other centers have adopted different strategies to address the requirement for open source and to enable commercial use, as required in the original RFA; we believe the NA-MIC liberal license approach minimizes barriers to wide adoption and is will maximize return on the NIH investment. The availability of software platforms commoditizes infrastructure for research and allows individual researchers to spend more time on their core research. Over time, this will promote biomedical computing by lowering the hurdle for scientists to use the technology.

===Q2.2 In what ways has the NCBC initiative advanced biomedical computing?===
Ron:(Marty edits also)

It is too early to say how the NCBC initiative advanced biomedical computing, though the results thus far are promising. Four of the NCBC's have been funded since October 2004 (less than three years), and funding for the other three began in late 2005. Center efforts of this size need significant time and effort to get organized and to synchornize activities of all participants. This startup effort is where the primary focus of the centers has been until now. There are early signs that some of the centers are beginning to emerge from this phase of their evolution and turning toward activities aimed at the field at large. However, it will be several years before the full impact of this program will become visible.

Below is a more detailed discussion of the specifics of this evolution from the vantage point of NA-MIC

(Tina)

Answer:http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#4._Impact_and_Value_to_Biocomputing

In NA-MIC's third year, it is clear that NA-MIC is developing a culture, environment, and resources to foster collaborative research in medical image analysis that draws together mathematicians, computer scientists, software engineers, and clinical researchers. These artefacts of NA-MIC impact how NA-MIC operates, make NA-MIC a fulcrum for NIH funded research, and draws new collaborators from across the country and around the world to NA-MIC.

*Impact within the Center

**Within the center, the NA-MIC organization is nimble, forming ad hoc distributed teams within and between cores to address specific biocomputing tasks. Information is shared freely on the NA-MIC Wiki, on the weekly Engineering telephone conferences, and in the NA-MIC Subversion source code repository. The software engineering tools of CMake, Dart 2 and CTest, CPack, and KWWidgets facilitate a cross platform software environment for medical image analysis that can be easily built, tested, and distributed to end-users. Core 2 has provided a platform, Slicer 3, that allows Core 1 to easily integrate new technology and deliver this technology in an end user application to Core 3. Core 1 has developed a host of techniques to apply to structural and diffusion analysis which are under evaluation by Core 3. Additionally, Core 3 members are heavy users of the Slicer 3 software and they provide ongoing feedback to Core 2 with respect to features that are optimal and those that need to be refined. They also assist in refining the user interface of this software platform. Core 3 members are also actively using the software and new tools developed by applying them to address scientific questions of interest, as is evident from the new publications using these tools. Major NA-MIC events, such as the annual All Hands Meeting, the Summer Project Week, the Spring Algorithms meeting, and Engineering Teleconferences are avidly attended by NA-MIC researchers as opportunities to foster collaborations.

*Impact within NIH Funded Research

** Within NIH funded research, NA-MIC continues to forge relationships with other large NIH funded projects such as BIRN, caBIG, NAC, and IGT. Here, we are sharing the NA-MIC culture, engineering practices, and tools. The BIRN infrastructure, built on widely-accepted grid middleware, allows NA-MIC researchers to share data, access computational resources and provides a rich collaborative environment through a science portal. caBIG lists the 3D Slicer among the applications available on the National Cancer Imaging Archive. NAC and IGT use the NA-MIC infrastructure and are involved in the development of the 3D Slicer. BIRN recently held an event modeled after the NA-MIC Project Week. NA-MIC has become a resource on open source licensing to the medical image analysis community.

NA-MIC is also attracting NIH funded collaborations. Two grants have been funded under PAR-05-063 to collaborate with NA-MIC: Automated FE Mesh Development and Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI. Five additional applications to collaborate with NA-MIC via the NCBC collaborative grant mechanism are under consideration. Additional grant applications submitted under other calls are planning to use and extend the NA-MIC tools. The first collaborative R01 from a previous driving biological problem has also been submitted by Drs. Shenton and Saykin to follow up on important tool development ideas that will combine multi-modal imaging to address specific questions relevant to brain circuitry abnormalities in schizophrenia.

*National and International Impact

**NA-MIC events and tools garner national and international interest. There were nearly 100 participants at the NA-MIC All Hands Meeting in January 2007, with many of these participants from outside of NA-MIC. Several researchers from outside the NA-MIC community have attended the Summer Project Weeks and the Winter Project Half-Weeks to gain access to the NA-MIC tools and people. These external researchers are contributing ideas and technology back into NA-MIC.

**Components of the NA-MIC kit are used globally. The software engineering tools of CMake, Dart 2 and CTest are used by many open source projects and commercial applications. For example, the K Desktop Environment (KDE) for Linux and Unix workstations uses CMake and Dart. KDE is one of the largest open source projects in the world. Many open source projects and commercial products are benefiting from the NA-MIC related contributions to ITK and VTK. Finally, Slicer 3 is being used as an image analysis platform in several fields outside of medical image analysis, in particular, biological image analysis, astronomy, and industrial inspection.

**NA-MIC co-sponsored the Workshop on Open Science at the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2006 conference. The proceedings of the workshop are published on the electronic Insight Journal, another NIH-funded activity.

**Over 50 NA-MIC related publications have been produced since the inception of the center.

===Q2.3 Are the NCBCs interfacing appropriately?===
(MARTY EDITS - need to address NCBC's interfacing ONLY)

Outreach across NCBCs is an effective use of resources. Initial efforts are ongoing and include cross-center use of software tools such as SimBios/SimTk actively using NAMICs VTK toolkit, and the use of the promising Software and Data Integration Working Group (SDIWG). In addition, NA-MIC has interactions with CCB and Simbios. NA-MIC also interfaces with a number of large-scale efforts which are not NCBC but are comparable in scale: BIRN, several national resource centers, and to some extent, with CaBIG. The NCBC's are thus interfacing appropriately.

Within the next few years there will be a need to increase NCBCs interfacing significantly. This will require either reallocation of resources or the allocation of new, additional resources. Due to the large variety of approaches adopted by the different centers it will likely be necessary to allocate significant amounts of time by senior leadership at the centers for identifying objectives of such efforts and by the engineering cores to actually execute the plans. A serious effort in this direction will probably require the allocation of multiple FTE's.

===Q2.4. What new collaborations have been formed through the NCBC initiative?===
(MARTY NEW EDITS)
NA-MIC’s structure and organization has facilitated many new collaborations. NA-MIC is a distributed center, bringing together mathematicians, computer scientists, software engineers, and clinicians from multiple sites. This distributed structure provides two types of new collaborations within NA-MIC: new collaborations between cores and new collaborations within cores. For between core collaborations, many of the algorithm and engineering core researchers had not collaborated previously with the researchers in either the first or second round of Driving Biological Projects (DBPs). Thus, the NCBC provides a unique opportunity for the algorithm and engineering core researchers to gain clinical insight and to adapt and tune their algorithms and tools to new clinical contexts. Conversely, the DBPs have gained access to algorithms and tools that they previously did not have. Additionally, new collaborations among DBPs and Core 1 members is an important step in bringing together computer scientists and clinical researchers and we have been most successful here. Similarly, many of the algorithm core researchers and engineering core researchers had not previously collaborated. Thus, the NCBC exposed the researchers in the algorithm core to the tools and engineering practices of the engineering core and exposed the researchers in the engineering core to the computational techniques and data structures utilized by the algorithms core. For within core collaborations, many of the researchers within the algorithm core had not previously collaborated. Through NA-MIC, these researchers have been able to cooperate and also amicably compete to address the issues brought forth by the DBPs.

Below is a list of new collaborations within NA-MIC. This list was compiled from the complete list of [[NA-MIC_Collaborations | NA-MIC Collaborations]] and project lists from the 5 NA-MIC [[Engineering:Programming_Events |Project week events]]. Best effort was made to filter these lists down to just the new collaborations (groups of researchers) formed under NA-MIC.

[TINA a Question from MARTY: IS BWH CORE 3 I THINK SO?? I THINK SO, SO WHO IS HARVARD?? IS THIS CF?? HAVE A LOOK BELOW]

* Georgia Tech + UC Irvine – Rule based segmentation algorithm for DLPFC
* Georgia Tech + Kitware - Knowledge-based Bayesian classification and segmentation
* BWH + MIT + Kitware - Brain tissue classification and subparcellation of brain structures
* Georgia Tech + UNC + BWH - Multiscale shape segmentation techniques
* BWH + Dartmouth + UNC + Georgia Tech - Shape analysis of the caudate and corpus callosum
* Georgia Tech + UNC + BWH - Spherical wavelet based shape analysis for Caudate
* Georgia Tech + UNC + BWH - Multiscale shape analysis of the hippocampus
* Dartmouth + UNC + BWH - Shape analysis of tbe hippocampus
* Utah + UNC + BWH - Automated shape model construction
* Dartmouth + Isomics - Neural substrates of apathy in schizophrenia
* Georgia Tech + GE Research + Kitware - Spherical Wavelet Transforms
* Georgia Tech + UNC - Shape analysis with Spherical Wavelets
* Utah + UNC - Adaptive, particle-based sampling for shapes and complexes
* UNC + Utah + ???Harvard - Tensor estimation and Monte-Carlo simulation
* Harvard + MIT + UNC - Corpus Callosum Regional FA analysis in Schizophrenia
* Dartmouth + MGH + Isomics + BWH - Integrity of Fronto-Temporal Circuitry in Schizophrenia using Path of Interest Analysis
* MGH + Isomics - ITK implementation of POIStats, and Integration into Slicer3
* UC Irvine + MGH + UNC + MIT - DTI Validation
* Utah + UNC + GE Research - DTI Software and Algorithm Infrastructure
* Utah + BWH - Tensor based statistics
* Utah + BWH - Diffusion tensor image filtering
* MGH + Dartmouth + Kitware + GE Research - Non-rigid EPI registration
* Dartmouth + BWH - Neural Substrates of Working Memory in Schizophrenia: A Parametric 3-Back Study
* Dartmouth + BWH - Brain Activation during a Continuous Verbal Encoding and Recognition Task in Schizophrenia
* Dartmouth + BWH - Fronto-Temporal Connectivity in Schizophrenia during Semantic Memory
* UC Irvine + Toronto - Imaging Phenotypes in Schizophrenics and Controls
* MIT + Isomics + GE Research + Kitware - fMRI statistics software
* MIND + Isomics + MGH - Analysis of Brain Lesions in Neuropsychiatric Systemic Lupus Erythematosis
* JHU + Queen's + BWH + Georgia Tech - Segmentation and Registration Tools for Robotic Prostate Interventions
* UNC + GE Research - Longitudinal MRI study of early brain development
* BWH + Kitware + MIT - Velocardiofacial Syndrome (VCFS) as a genetic model for schizophrenia
* UNC + GE Research + BWH - DTI population analysis
* Georgia Tech + BWH - Geodesic tractography
* BWH + Queen's + GE Research - Display optimization
* UCSD + Isomics - Dendritic Spine Morphometrics
* <others to be filled in>

NA-MIC has also attracted researchers from the field who were not originally part of NA-MIC. Some of these new collaborations are formally organized using the NIH NCBC Collaborative R01 program. But other collaborations are being driven solely by the opportunity to share resources, techniques, capabilities, and ideas.

Below is a list of new collaborations between external researchers and NA-MIC. Again, best effort was made to only list the new collaborations with external parties.

* Mario Negri + GE Research – Integration of vmtk with Slicer 3
* Iowa + Isomics - Finger Bone Biomechanics
* CalTech + Kitware - Systems Biology and Genomic Science
* BWH + Wake Forest + Virginia Tech - Alcohol Stress in Primates
* BWH + MGH - Radiation Treatment Planning'
* Iowa + Kitware + BWH - Non Linear Registration Tools
* Northwestern + Isomics - Radiology Translation Station
* Harvard IIC + Isomics + GE Research - Astronomy Analysis and Visualization
* Virginia Tech + BWH - Applying EMSegmenter to nonhuman primate neuroimaging
* JHU + Queen's + BWH - Brachytherapy needle positioning robot integration
* Iowa + Kitware + BWH - Nonrigid registration
* Iowa + BWH - Developing electronic atlas
* Iowa + Kitware - GUI for nonridig image registration
* Canary Islands Technological Institute + Isomics + GE Research - DICOM Query/Retrieve
* Canary Islands Technological Institute + GE Research - Block matching registration
* UNC + Duke University Medical Center - DTI tractography analysis in depression study
* <others to be filled in>

The collaborative nature of NA-MIC is exemplified by the attendance at the NA-MIC All Hands Meeting and the NA-MIC Summer Project Week. Researchers from within and external to NA-MIC come together at these two events to forge collaborations. At the 2007 NA-MIC All Hands Meeting alone, there were 96 attendees: 56 NA-MIC researchers, 32 NA-MIC collaborators from 13 institutions, and 8 members of the External Advisory Board and NIH. At the Project Half-Week run in conjunction with the All Hands Meeting, there were 38 projects: 16 initiated from the algorithm core, 10 specific to the engineering core, and 11 from external collaborators.

More detailed information on collaborations as well as Project Week events can be found at:
* http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations
* http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events

===Q2.5. What new training opportunities have the centers provided?===

**Answer: http://www.na-mic.org/Wiki/index.php/Training:Main

* The requirement for all NCBC's to dedicate funds for training provides the opportunity to develop a targeted and deep portfolio of training resources. The unique perspective of providing these training opportunities and resources specifically targeted to a multi-disciplinary audience of basic and clinical biomedical scientists, computer scientists and medical imaging scientists is fostered by the NCBC Program. Traditional funding by NIH research grants results in allocation of all funds into the primary research; NIH training grants allocate funding for the support of trainees; the few education grants are restricted in budget and overhead thus severely limiting the quality and quantity of educational resources that can be offered. The NCBCs have created a new opportunity for our cadre of experienced clinician scientists, computer scientists and medical image analysis experts affiliated with our large centers to be supported to work on outreach activities.

* Within NA-MIC, this perspective has given rise to a thriving training program that supports the biomedical research community within NAMIC, across the NIH community and around the world. The strong demand for our training resources is evident from the large number of hits to our training web pages, from the rapid enrollment in all offered workshops, and the positive feedback from participants. We believe, and our belief is supported by the documented backgrounds of our workshop attendees, that a key aspect of our training materials that makes them useful to the community is that they are learner-centered, goal-oriented, and targeted to bridge the gaps in technical knowledge and language that exist between basic and clinical biomedical scientists, computer scientists and medical imaging scientists. For example, a tutorial that teaches how to use Slicer to register two images includes not only the necessary details of how to implement the algorithm, but also the conceptual framework for the registration approach, the mathematical underpinnings of the algorithm and a detailed anatomical approach for visually inspecting and refining the registration. This rich, but simple approach provides a consistently educational experience for every new user of the NAMIC toolkit.

* NAMIC supported new training opportunities are developed to maximize impact on the wider scientific community. The primary vehicle for this is "Slicer 101", our portfolio of Slicer training tutorials (http://www.na-mic.org/Wiki/index.php/Slicer:Workshops:User_Training_101). We have focused our efforts on making all our tutorial materials available via the NA-MIC Wiki as downloadable Powerpoint presentations and accompanying curated, anonymized datasets. The tutorials are all carefully tested on multiple computer platforms and by our team before being used in live Workshops (http://www.na-mic.org/Wiki/index.php/Training:Events_Timeline). Refinements are made based on the feedback of the audience and our experience during the teaching sessions. The final product of our work allows any new users, regardless of educational background, to not only use the NAMIC tools and algorithms, but to understand what they are doing and why. To date we have had over 7,880 hits to the Slicer 101 webpage.

* NAMIC supported Workshops are another unique venue for multi-disciplinary training. In addition to the all the points made regarding the content of the training materials, the 14 Workshops run by the NAMIC Training core over the past 3 years have each provided the opportunity for new connections to be made among basic and clinical biomedical scientists, computer scientists and medical imaging scientists. All our Workshops provide opportunities for formal and informal discussions among attendees of diverse backgrounds and strengths. These hands-on, interactive workshops allow participants to translate concepts of medical image processing into skills through instructor-led training. The simplicity of our approach, and the exceptional quality of the NAMIC toolkit, ensures a very high success rate for knowledge and skill acquisition. We estimate that 370 people from 52 different universities and companies attended our Workshops between 2005 and 2006.

* We are currently focusing our efforts on reaching a wider community by delivering a more didatic based Workshop in conjunction with the upcoming Organization for Human Brain Mapping meeting in Chicago next week. We held our enrollment to 50 so that we could offer the same hands-on interactive training experience to the attendees and our registration filled within a few weeks of the offering being posted. Tenatively, we anticipate that more than 12 countries and 14 states within the US will be represented at this upcoming Workshop.

* A final point is that this commitment and focus on training permeates all aspects of the NAMIC program. All large gatherings of NAMIC personnel including All Hands Meetings, Programming/Project Weeks, and Core meetings provide venues for our culture of training to be expressed. Each gathering creates an opportunity to build bridges between our participating disciplines and to improve the communication skills of each member. This culture includes implicit aspects such as a supportive and collegial environment that encourages questions and critical feedback, as well as explicit aspects such as encouraging junior level participants to make presentations and scheduling educational presentations from domain experts within and outside of the community. We believe that the positive attitude towards sharing knowledge and skills is fostered in all who are associated with our Project.

===Q2.6. What changes could make the program more effective in the future?===

* ''National Visibility''

The overall objective statement of the NCBC program calls for building computational infrastructure for the nation's biomedical research efforts -- this ambitious goal includes both computational and biomedical science, areas in which the existing centers have extensive experience, but also in nationwide "marketing" efforts where the scientific community is less adept. Several centers, including NA-MIC, have developed novel approaches to this problem through their training and dissemination cores, but as the output of the centers grows along with the number of potential users in the community and the potential impact, these critical resources will be increasingly strained. There are several approaches the overall program could take to address this issue including supplemental funding to host workshops or conferences, providing small travel grants for researchers or students to visit the centers, or actively encouraging a wider range of NIH funded researchers to adopt the tools generated by the NCBCs. In particular, collaboration PAR has been very effective as a mechanism for encouraging busy scientists to consider adopting the NCBC tools and is an excellent example of how the program can extend the impact of the basic investment in scientific infrastructure.

* ''Local Autonomy''

The program should avoid adding extra layers of uniformity to what are fundamentally unique centers. The NCBC program has successfully established a distributed network of centers drawing on the expertise of some of the nation's leading researchers drawn to the program for the opportunity to develop and apply their know-how to this ambitious effort. This rich environment, predictably, yields a diversity of approaches and organizational structures as each of the centers works to implement their particular vision of how to fulfill the overall mission of the NCBC program. Preserving the vitality of the effort depends on retaining this autonomy as each center strives to meet the individual objectives suited to their communities. The program needs well defined goals that each center must meet, but the overall program should facilitate the individual solutions of the center's leadership.

* ''Stability''

One critical mission of the NCBC program is the creation of infrastructure. These large-scale efforts require time-horizons that are incompatible with the current framework of many programs at NIH. It might take two or three iterations until a software package is sufficiently mature to be attractive to a larger community of scientists. Large software platforms require engineering staff of 10-20 in addition to the biomedical scientists developing functionality aimed at solving a particular problem. Many of the NCBCs are working on several packages. This results in underfunded projects which compromises the timeliness and performance of the resulting software.

It would be advisable to increase funding for the engineering and outreach activities of the centers and provide the funding in a reliable way. The continous stream of cuts from of the original budget has made this discrepancy even more pronounced. In 2007/2008 we will receive only 77.7% of the money that was budgeted in the application for that year. Following the RFA guidelines, the original budget did not contain adjustments for inflation. Furthermore, in deviation from the way that most NIH programs are funded, the budget was frozen in total dollars not direct dollars. Institutional overhead rates, and fringe and benefit rates have increased for several of the NA-MIC participants during the last three years and have resulted in further decreases in the amount of money available to actually do research.

===Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing?===

* ''Greater Start Up Time and Steeper Learning Curve Than Anticipated''

Bringing together computer scientists, engineers, and biomedical researchers, with diverse interests, training, and background, for the purpose of working on a set of biological problems, is no easy feat and, in retrospect required a steeper learning curve than was anticipated. This steeper learning curve is understandable, since the main focus initially was on developing alliances among the cores in order to increase awareness about the kinds of tools needed for the specific imaging problems posed by the biomedical researchers who were driving the biological problems. The first year of the grant, as noted in our annual report, thus reflected a "core" emphasis, as an interdisciplinary team was brought together, many members for the first time. It was not until the second year of the grant that the focus on a "core" emphasis shifted to a focus on "themes", which cut across "core" boundaries. While this shift was viewed as part of a natural evolution, now that we know this, we can help guide future NIH efforts by suggesting that specific projects/clinical applications should be highlighted in the first few months of the grant, based on meetings among core members, so as to facilitate a focus on clinical applications from the outset.

Such an early emphasis on clinical applications/problems would also facilitate an early focus on the development and application of computational tools, which could be more closely aligned with specific clinical problems and applications. This would breakdown artificial barriers that a "core" focus involves, which, while seemingly an inherent part of the initial stages, could be curtailed by highlighting early the need to focus on specific needed applications. In this way, the needed applications of the driving biological problems could form natural groupings that involve members from all cores, and work groups could be set up from the beginning that reflect a "theme"/"application" approach. This would also assist in more communication between core members, which would also likely facilitate ongoing communication among computer scientists, engineers, and biomedical researchers.

* ''Developing Robust Software for Advanced Applications is Difficult''

Creating industrial-strength software solutions to support scientific investigations is time consuming and requires skill not usually found in the academic environment. NA-MIC has been successful at bringing in commercial software development expertise to help accomplish the center's goals (GE, Kitware, Isomics) but these resources are routinely stretched well beyond the allocated budgets due to the many research directions of the center's scientists. When considering biomedical computing projects, the NIH must not attempt to short change the development process or the resulting systems run a greater risk of being difficult to maintain, difficult to scale up, and incompatible across systems.

An excellent example of the level of effort needed for a successful, extensible, cross-platform product is the National Library of Medicine's Insight Toolkit (ITK). The organizational meeting for ITK was held in October 1999. Between October 1999 and October 2002, fifty developers contributed code from six prime contractors (GE, Kitware, Insightful, UNC, Utah, and UPenn) and four sub-contractors (BWH, UPenn, Pitt, and Columbia) to produce ITK Version 1.0. To date, over $13.5 million has been awarded by the NIH for the development, use, and expansion of ITK. That total includes 20, one-year contracts that were given to early adopters of ITK. When assessing the level of effort expended on ITK, it is important to consider that ITK's funding did not cover algorithm, graphical user interface, or visualization developments. ITK costs only funded the integration of existing methods into a common library. Developing end applications, involving user interfaces and visualizations tailored for clinical users, requires significant additional effort.

* ''Algorithm Development Needs to be Interactive and Not Sequential''

In reviewing the last three years of the driving biological problem, schizophrenia, it is evident that tool development that involved multiple interactions among members of Core 1 (Computer Scientists), 2 (Engineers), and 3 (Driving Biological Problem), at all stages of development, led to the development of computational tools that were both more tailored to the specific applications needed by Core 3 members, as well as to the development of tools that were more optimized for general use. This interactive mode of tool development is in contrast to tool development that proceeded more sequentially, where one or several members of Core 1 and 3 met, and then Core 1 proceeded with what their understanding was of the problem and went off and developed a tool with very little further input from Core 3 until the tool was delivered. The latter approach often resulted in delays in receiving the tool, as there was less communication between Core 1 and Core 3 members in these instances, and often the tool did not really meet the specific needs of the application without further work. In the future, and based on this experience, NIH initiatives should emphasize the importance of encouraging a more "interactive" approach to tool development and to discouraging what is termed here as a more "sequential" approach to tool development. With a more interactive approach, progress can be more readily evaluated at each phase of tool development, and input and testing can be provided based on more communication among members of Core 1, 2, and 3. A more "interactive" model is also far more responsive to the needs of the driving biological problem, and also keeps the focus on the clinical application.

Focusing on interactions across core members will also likely facilitate breaking down the steep learning curve inherent in early interactions across cores members (see above).

* ''Funding''

As mentioned above, there is a serious mismatch between the objectives set by NIH and the resources available to address them. A further aggravation were the additional cuts imposed over the last three years, combined with a "requirements creep". The future increased interaction between the centers will require either additional resources or decommittment for existing activities. If the centers are as successful as envisioned, an increasing number of scientists will want to interact with the scientists at the centers which will also require additional resources.

NIH will need to allocate significant additional resources to the centers in order to fulfill the vision of a network of centers of biomedical computing at the center of an ever increasing network of biomedical scientific programs benefiting form their software and services.

=='''Q3:A list of publications and/or software tools produced by the Center.''' If this information is provided in your progress report or is available on your website, a link will be sufficient. We are especially interested in your assessment of the maturity of your software tools and the impact they are having on the scientific community.===

A3:
*NA-MIC Publications are available here: http://www.na-mic.org/Wiki/index.php/Publications.
*NA-MIC Software Tools are available here: http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit
The Center has created and extended a number of software tools to handle some of the key problems in medical imaging analysis and to deliver these computational technologies via a suite of applications, toolkits, and infrastructure facilities. A summary description of these tools includes:
* Slicer3 (application) - a application platform for deploying imaging technologies, newly architected with an execution model facilitating integration, work flow, and large scale computing. While Slicer3 is the newest addition to the NAMIC Kit, it is built on pre-existing, mature toolkits so that the application is relatively mature, and is already in use. Because Slicer3 supports plug-in modules, active development is proceeding to create and package various modules for dissemination to the NAMIC community.
* ITK (toolkit) - a mature system for image analysis, registration and segmentation (initially created in 1999). ITK is in use worldwide for medical imaging research and development.
* VTK (toolkit) - a mature system for visualization, graphics, volume rendering, and interaction (initially created in 1993). VTK is used worldwide for research, teaching and commercial development.
* DART (computational infrastructure) - a key component of the NAMIC quality control process, DART is used to coordinate and report the software testing process. It was created in the first year of NAMIC and is in constant use, therefore a mature system.
* CMake/CTest/CPack (computational infrastructure) - CMake and CTest are relatively mature systems used to manage the building and testing of software across diverse computer platforms. CMake is used worldwide by some of the [http://lwn.net/Articles// world's largest open source systems such as KDE]. CPack, a recent addition to the NAMIC kit, is used to simplify the packaging and dissemination of software across platforms. Thus in NAMIC we can easily deploy our software across Windows, Linux, Unix, and Mac platforms.
* Other tools (computational infrastructure) - Many other software tools are used to support the development of advanced imaging applications, and to assist with large scale computing, including
** Teem - image processing tools (mature)
** KWWidgets - Open source, cross platform GUI toolkit (mature, but development continues to support workflow).
** BatchMake - Support large-scale computing, including grid computing, for performing large population studies and statistical analysis (under active development).
** GridWizard - an application scheduler aimed at allowing researchers to easily harness the power of large computational grids. It lets you run tens of thousands of commands simultaneously on multiple clusters of computers by typing a single command, without writing scripts. It can be used by itself, and is currently being integrated with Slicer3 and as part of a web-based portal environment. (http://www.na-mic.org/Wiki/index.php/Slicer3:Grid_Interface)

These tools address key problems in imaging including segmentation, registration, visualization, and shape analysis; or provide facilities supporting researchers and developers who wish to create advanced software applications. One of key characteristics of NAMIC is that we treat the development of advanced medical image analysis software holistically; that is, the complete cycle of algorithm design, efficient impmentations, quality control and dissemination are needed to effectively address challenges provided by the driving biological problems. Examples of how these tools are being used include the following:

(a) Segmentation: Here there are a variety of tools of varying degrees of maturity ranging from the EM Segmentor (a widely distributed, mature algorithm included in Slicer3 as an application plug-in) to more recent work on DTI segmentation based on directional flows which are Matlab and C++ based. Powerful mature tools such as Bayesian segmentation have been recently included in Slicer (and have been available in ITK for some time now) which can be combined with very recent work on the semi-automated segmentation of the DPFC done in collaboration with Core 3 researchers. Further, tools previously developed by NAMIC researchers which have had a wide distribution have been put into Slicer. For example, geometric based segmentation methods (some of which were included in packages marketed by GE) were tailored for cortical segmentation, and included in the Slicer, and in fact even improved with the inclusion of statistical based approaches.

(b) Registration: Similar remarks can be made for registration in which we have a spectrum ranging from very mature methods to very recent ones which are still being tested. In particular, mature widely distributed methodologies for rigid registration are now included in ITK, as well as spline-based registration methodologies. These are well-tested methods which have been made accessible to the general imaging community. Newer methodologies such as those based on optimal transport for elastic registration are being included in ITK. NAMIC has also pushed for fast implementations of its algorithms to be used on cheap widely available platforms. Taking a cue from the game industry, some algorithms have been ported to GPUs (graphics cards) which are being employed now as computing devices. This has led to a speed-up of almost two orders of magnitude on some of the registration algorithms being tested.

(c) Shape Analysis: Again a number of methodologies have been developed and implemented with varying levels of maturity. Shape methodologies based on spherical harmonics are quite mature, and are available in pipelines developed by NAMIC researchers and have been distributed to the general community. A newer spherical based wavelet shape analysis package has been put into ITK, which also drives a novel shape-based segmentation procedure. More globally based spherical harmonic ideas have been combined with the multi-resolution spherical wavelet approach as a statistical shape based package for schizophrenia. This general technique may be used for other purposes as well, and is presently being ported to some work being done on the prostate. Work has also been accomplished on particle based approaches to this important problem area with the code put into ITK. Many times we work with a Matlab/C++ initial version of our codes, then move to ITK, and finally to Slicer. However, even at the Matlab/C++ stage, algorithms have been distributed and used in a clinical setting (for example, rule-based brain segmentation approaches).

(d) Diffusion Weighted Image (DWI) Analysis: A number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical analysis of fiber bundles have been contributed to the NAMIC toolkit. Some of these tools have been already integrated into diffusion dedicated ITK package with GUI as part of the NAMIC toolkit (e.g., FiberViewer-UNC) and into the Slicer platform (BWH tractography, clustering). The impact of NAMIC DWI analysis activities is best characterized by most recent journal articles and journal articles in print. The application of the NAMIC FiberViewer tool (UNC) in large clinical studies at UNC and Duke are in print (Taylor et al, Gilmore et al., Cascio et al.). Clinical application of the Slicer DTI package by BWH/MIT is reported in O’Donnell et al. Kuroki N. et al. and Nakamura et al.. The research by MGH is found in two journal publications by Tuch et al.. The description of the methodologies also appeared or will appear in peer reviewed journals (Corouge et al., Fletcher et al., Corouge et al.). New methods in development (Finsler metric GT, volumetric PDE based path analysis Utah, stochastic tractography BWH), and path of interest MGH, are currently tested on Core-3 DBP data, with the goal to give recommendations on which type of solution is appropriate to solve specific clinical analysis questions.

(e) Visualization: Core 2 researchers involved with NAMIC now (e.g., the founders of Kitware) were at the forefront of developing VTK (and of course ITK). Thus here we are considering technologies which are at a commercial level of development, and used at thousands of sites. Algorithms developed at NAMIC have driven new directions for these packages. Newer visualization methods, for example, the conformal flattening procedure have been ported to an ITK filter and is in the NAMIC Sandbox. Quasi-isometric methods for brain flattening from the MGH Free Surfer have become part of the NAMIC enterprise as well. These flattening procedures are very easy to use, and may also be employed for registration. Code for the control of distortion of the area in flattening has been incorporated which give area-preservation with minimal distortion. The techniques may be also used for several other purposes included automatic fly-throughs in endoscopy (incorporated into Slicer), and for texture mappings for general visualization purposes.

==Logistics==
When completed, the information should be sent to:

Gwen Jacobs, PhD

Professor of Neuroscience

Asst. CIO and Director of Academic Computing

1 Lewis Hall

Montana State University

Bozeman, MT 59717

406-994-7334 - phone

406-994-7077 - FAX

gwen@cns.montana.edu <mailto:gwen@cns.montana.edu>

2007 Materials for NCBC Program Review

2007-06-08T11:56:24Z

Shenton: /* Q2.3 Are the NCBCs interfacing appropriately? */

==Materials requested for NCBC Program Review==

These are due to Gwen Jacobs by Friday, June 08, 2007.

=='''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''==

Summary: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#1._Introduction

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with respect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs include a focus on Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

Highlights: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#3._Highlights

The third year of the NA-MIC project saw continued development and dissemination of medical image analysis software. The current progress is clearly characterized by a significant increase in the application of Core-1 and Core-2 tools to image data provided by the Core-3 DBP groups. The main reasons for this progress is two-fold, first there are several new methods that are out of the prototype stage and ready to be applied to large sets of imaging datasets, and second there are new high-resolution imaging data from high-field scanners that are more appropriate for the new tools than historical data with often very coarse slice resolution.

With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NA-MIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NA-MIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

*Advanced Algorithms
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NA-MIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NA-MIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The NA-MIC toolkit includes a comprehensive set of modules for analysis of diffusion weighted images (DWI), including improved calculation of tensors, interpolation, nonlinear deformation and statistics on tensor fields, novel methods for tractography and for optimal path finding, and clustering of sets of streamlines.
* A quantitative tractography package for user-guided geometric parametrization and statistical analysis of fiber bundles (FiberViewer) has been contributed to the NAMIC toolbox. This tool used in several ongoing clinical DTI studies.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NA-MIC Sandbox in preparation for formal acceptance into the NA-MIC Kit.
*Technology Deployment Platform: Slicer3
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NA-MIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* Slicer3's execution model is also allowing researchers to access large computational resources. Two applications that support the use of such resources, BatchMake and GridWizard, have been recently integrated with Slicer3 for specific modules. The GUI for the modules that are to be run via these computational support applications allow for the creation of large population studies or parametric studies of an algorithm.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NA-MIC Kit, and are now available to all other modules built on the Slicer3 framework.

*Outreach and Technology Transfer
Cores 4-5-6 continue to support, train and disseminate to the NA-MIC community, and the broader biomedical computing community.
* NA-MIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NA-MIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair. The training and dissemination will continue with a DTI workshop at the forthcoming Human Brain Mapping meeting and activities at MICCAI 2007, among others.

==Q2==
'''A brief statement - (one page per question, max) addressing each of the questions listed below. These are the questions that we have been asked to address in our report. Our goal in asking for this information is to be able to produce a report that reviews the program as a whole. Your view, from the vantage point of the center you direct, is critical to our work. In addition, your answers will provide us with more information that we can use in our discussion with program staff on June 11th. We know that some of this information can be found on your websites, so in those cases a link to the information would be most helpful.'''

===Q2.1 To what extent does the vision and direction of the NCBC initiative promote biomedical computing?===

The RFA for the creation of the NCBC program laid out a very explicit vision.

The NCBCs are to be the core of the networked national effort to build the computational infrastructure for biomedical computing in the US. From the [http://www.bisti.nih.gov/ncbc/ NIH website] in 2004: Four new National Centers for Biomedical Computing (NCBC) will develop and implement the core of a universal computing infrastructure that is urgently needed to speed progress in biomedical research. The centers will create innovative software programs and other tools that enable the biomedical community to integrate, analyze, model, simulate, and share data on human health and disease. The original [http://grants1.nih.gov/grants/guide/rfa-files/RFA-RM-04-003.html RFA] stated: 1. The software should be freely available to biomedical researchers and educators in the non-profit sector and 2. The terms of software availability should permit the commercialization of enhanced or customized versions of the software.

As the amount of data produced by biomedical researchers is increasing at an ever accelerating pace, the relative importance of computing as an integral part of analysis is increasing as well. In addition both data and analyses are getting more and more complex. Increasingly, science is performed by interdisciplinary teams at multiple locations. Industrial strength research platforms are one of the emerging needs of biomedical research. This need was only been partially addressed before the creation of the NCBC program. The NCBC centers are in an outstanding position to develop stable, maintainable, and expandable software to address these needs.

In addition to building the infrastructure for biomedical computing, NCBCs play an important role in fostering a community of computational scientists dedicated to solving problems in the biomedical domain. In its short three-year life span, NA-MIC NCBC has contributed to this vision by holding numerous workshops and tutorials on open source software for biomedical image analysis, by supporting the Insight Journal, a peer-review venue for publications accompanied with open-source implementation, and by engaging a large number of graduate students in Computer Science programs in biomedical computing research. By creating high-profile bio-computation programs, the NCBC initiative brings the biomedical computing as a field of research to the attention of the computation community and actively promotes collaborations between computational and biological sciences.

The participants in our center signed on for this project because they believe in the vision of developing a universal computing infrastructure for medical image computing. NA-MIC is creating an open source platform that embodies this vision for the field of medical image computing. We have settled on a very liberal open source license for our software plattform. All the components of that plattform, called the NA-MIC kit, are distributed under a BSD style licence without restrictions on commercial use. Other centers have adopted different strategies to address the requirement for open source and to enable commercial use, as required in the original RFA; we believe the NA-MIC liberal license approach minimizes barriers to wide adoption and is will maximize return on the NIH investment. The availability of software platforms commoditizes infrastructure for research and allows individual researchers to spend more time on their core research. Over time, this will promote biomedical computing by lowering the hurdle for scientists to use the technology.

===Q2.2 In what ways has the NCBC initiative advanced biomedical computing?===
Ron:(Marty edits also)

It is too early to say how the NCBC initiative advanced biomedical computing, though the results thus far are promising. Four of the NCBC's have been funded since October 2004 (less than three years), and funding for the other three began in late 2005. Center efforts of this size need significant time and effort to get organized and to synchornize activities of all participants. This startup effort is where the primary focus of the centers has been until now. There are early signs that some of the centers are beginning to emerge from this phase of their evolution and turning toward activities aimed at the field at large. However, it will be several years before the full impact of this program will become visible.

Below is a more detailed discussion of the specifics of this evolution from the vantage point of NA-MIC

(Tina)

Answer:http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#4._Impact_and_Value_to_Biocomputing

In NA-MIC's third year, it is clear that NA-MIC is developing a culture, environment, and resources to foster collaborative research in medical image analysis that draws together mathematicians, computer scientists, software engineers, and clinical researchers. These artefacts of NA-MIC impact how NA-MIC operates, make NA-MIC a fulcrum for NIH funded research, and draws new collaborators from across the country and around the world to NA-MIC.

*Impact within the Center

**Within the center, the NA-MIC organization is nimble, forming ad hoc distributed teams within and between cores to address specific biocomputing tasks. Information is shared freely on the NA-MIC Wiki, on the weekly Engineering telephone conferences, and in the NA-MIC Subversion source code repository. The software engineering tools of CMake, Dart 2 and CTest, CPack, and KWWidgets facilitate a cross platform software environment for medical image analysis that can be easily built, tested, and distributed to end-users. Core 2 has provided a platform, Slicer 3, that allows Core 1 to easily integrate new technology and deliver this technology in an end user application to Core 3. Core 1 has developed a host of techniques to apply to structural and diffusion analysis which are under evaluation by Core 3. Additionally, Core 3 members are heavy users of the Slicer 3 software and they provide ongoing feedback to Core 2 with respect to features that are optimal and those that need to be refined. They also assist in refining the user interface of this software platform. Core 3 members are also actively using the software and new tools developed by applying them to address scientific questions of interest, as is evident from the new publications using these tools. Major NA-MIC events, such as the annual All Hands Meeting, the Summer Project Week, the Spring Algorithms meeting, and Engineering Teleconferences are avidly attended by NA-MIC researchers as opportunities to foster collaborations.

*Impact within NIH Funded Research

** Within NIH funded research, NA-MIC continues to forge relationships with other large NIH funded projects such as BIRN, caBIG, NAC, and IGT. Here, we are sharing the NA-MIC culture, engineering practices, and tools. The BIRN infrastructure, built on widely-accepted grid middleware, allows NA-MIC researchers to share data, access computational resources and provides a rich collaborative environment through a science portal. caBIG lists the 3D Slicer among the applications available on the National Cancer Imaging Archive. NAC and IGT use the NA-MIC infrastructure and are involved in the development of the 3D Slicer. BIRN recently held an event modeled after the NA-MIC Project Week. NA-MIC has become a resource on open source licensing to the medical image analysis community.

NA-MIC is also attracting NIH funded collaborations. Two grants have been funded under PAR-05-063 to collaborate with NA-MIC: Automated FE Mesh Development and Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI. Five additional applications to collaborate with NA-MIC via the NCBC collaborative grant mechanism are under consideration. Additional grant applications submitted under other calls are planning to use and extend the NA-MIC tools. The first collaborative R01 from a previous driving biological problem has also been submitted by Drs. Shenton and Saykin to follow up on important tool development ideas that will combine multi-modal imaging to address specific questions relevant to brain circuitry abnormalities in schizophrenia.

*National and International Impact

**NA-MIC events and tools garner national and international interest. There were nearly 100 participants at the NA-MIC All Hands Meeting in January 2007, with many of these participants from outside of NA-MIC. Several researchers from outside the NA-MIC community have attended the Summer Project Weeks and the Winter Project Half-Weeks to gain access to the NA-MIC tools and people. These external researchers are contributing ideas and technology back into NA-MIC.

**Components of the NA-MIC kit are used globally. The software engineering tools of CMake, Dart 2 and CTest are used by many open source projects and commercial applications. For example, the K Desktop Environment (KDE) for Linux and Unix workstations uses CMake and Dart. KDE is one of the largest open source projects in the world. Many open source projects and commercial products are benefiting from the NA-MIC related contributions to ITK and VTK. Finally, Slicer 3 is being used as an image analysis platform in several fields outside of medical image analysis, in particular, biological image analysis, astronomy, and industrial inspection.

**NA-MIC co-sponsored the Workshop on Open Science at the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2006 conference. The proceedings of the workshop are published on the electronic Insight Journal, another NIH-funded activity.

**Over 50 NA-MIC related publications have been produced since the inception of the center.

===Q2.3 Are the NCBCs interfacing appropriately?===
(MARTY EDITS - need to address NCBC's interfacing ONLY)

Outreach across NCBCs is an effective use of resources. Initial efforts are ongoing and include cross-center use of software tools such as SimBios/SimTk actively using NAMICs VTK toolkit, and the use of the promising Software and Data Integration Working Group (SDIWG). In addition, NA-MIC has interactions with CCB and Simbios. NA-MIC also interfaces with a number of large-scale efforts which are not NCBC but are comparable in scale: BIRN, several national resource centers, and to some extent, with CaBIG. The NCBC's are thus interfacing appropriately.

Within the next few years there will be a need to increase NCBCs interfacing significantly. This will require either reallocation of resources or the allocation of new, additional resources. Due to the large variety of approaches adopted by the different centers it will likely be necessary to allocate significant amounts of time by senior leadership at the centers for identifying objectives of such efforts and by the engineering cores to actually execute the plans. A serious effort in this direction will probably require the allocation of multiple FTE's.

===Q2.4. What new collaborations have been formed through the NCBC initiative?===

NA-MIC’s structure and organization has facilitated many new collaborations. NA-MIC is a distributed center, bringing together mathematicians, computer scientists, software engineers, and clinicians from multiple sites. This distributed structure provided two types of new collaborations within NA-MIC: new collaborations between cores and new collaborations within cores. For between core collaborations, many of the algorithm and engineering core researchers had not collaborated previously with the researchers in either the first or second round of Driving Biological Projects (DBPs). Thus, the NCBC provided a unique opportunity for the algorithm and engineering core researchers to gain clinical insight and to adapt and tune their algorithms and tools to new clinical contexts. Conversely, the DBPs gained access to algorithms and tools that they previously had not utilized. Similarly, many of the algorithm core researchers and engineering core researchers had not previously collaborated. Thus, the NCBC exposed the researchers in the algorithm core to the tools and engineering practices of the engineering core and exposed the researchers in the engineering core to the computational techniques and data structures utilized by the algorithms core. For within core collaborations, many of the researchers within the algorithm core had not previously collaborated. Through NA-MIC, these researchers have been able to cooperate and also amicably compete to address the issues brought forth by the DBPs.

Below is a list of new collaborations within NA-MIC. This list was compiled from the complete list of [[NA-MIC_Collaborations | NA-MIC Collaborations]] and project lists from the 5 NA-MIC [[Engineering:Programming_Events |Project week events]]. Best effort was made to filter these lists down to just the new collaborations (groups of researchers) formed under NA-MIC.

* Georgia Tech + UC Irvine – Rule based segmentation algorithm for DLPFC
* Georgia Tech + Kitware - Knowledge-based Bayesian classification and segmentation
* BWH + MIT + Kitware - Brain tissue classification and subparcellation of brain structures
* Georgia Tech + UNC + BWH - Multiscale shape segmentation techniques
* BWH + Dartmouth + UNC + Georgia Tech - Shape analysis of the caudate and corpus callosum
* Georgia Tech + UNC + BWH - Spherical wavelet based shape analysis for Caudate
* Georgia Tech + UNC + BWH - Multiscale shape analysis of the hippocampus
* Dartmouth + UNC + BWH - Shape analysis of tbe hippocampus
* Utah + UNC + BWH - Automated shape model construction
* Dartmouth + Isomics - Neural substrates of apathy in schizophrenia
* Georgia Tech + GE Research + Kitware - Spherical Wavelet Transforms
* Georgia Tech + UNC - Shape analysis with Spherical Wavelets
* Utah + UNC - Adaptive, particle-based sampling for shapes and complexes
* UNC + Utah + Harvard - Tensor estimation and Monte-Carlo simulation
* Harvard + MIT + UNC - Corpus Callosum Regional FA analysis in Schizophrenia
* Dartmouth + MGH + Isomics + BWH - Integrity of Fronto-Temporal Circuitry in Schizophrenia using Path of Interest Analysis
* MGH + Isomics - ITK implementation of POIStats, and Integration into Slicer3
* UC Irvine + MGH + UNC + MIT - DTI Validation
* Utah + UNC + GE Research - DTI Software and Algorithm Infrastructure
* Utah + BWH - Tensor based statistics
* Utah + BWH - Diffusion tensor image filtering
* MGH + Dartmouth + Kitware + GE Research - Non-rigid EPI registration
* Dartmouth + BWH - Neural Substrates of Working Memory in Schizophrenia: A Parametric 3-Back Study
* Dartmouth + BWH - Brain Activation during a Continuous Verbal Encoding and Recognition Task in Schizophrenia
* Dartmouth + BWH - Fronto-Temporal Connectivity in Schizophrenia during Semantic Memory
* UC Irvine + Toronto - Imaging Phenotypes in Schizophrenics and Controls
* MIT + Isomics + GE Research + Kitware - fMRI statistics software
* MIND + Isomics + MGH - Analysis of Brain Lesions in Neuropsychiatric Systemic Lupus Erythematosis
* JHU + Queen's + BWH + Georgia Tech - Segmentation and Registration Tools for Robotic Prostate Interventions
* UNC + GE Research - Longitudinal MRI study of early brain development
* BWH + Kitware + MIT - Velocardiofacial Syndrome (VCFS) as a genetic model for schizophrenia
* UNC + GE Research + BWH - DTI population analysis
* Georgia Tech + BWH - Geodesic tractography
* BWH + Queen's + GE Research - Display optimization
* UCSD + Isomics - Dendritic Spine Morphometrics
* <others to be filled in>

NA-MIC has also attracted researchers from the field who were not originally part of NA-MIC. Some of these new collaborations are formally organized using the NIH NCBC Collaborative R01 program. But other collaborations are being driven solely by the opportunity to share resources, techniques, capabilities, and ideas.

Below is a list of new collaborations between external researchers and NA-MIC. Again, best effort was made to only list the new collaborations with external parties.

* Mario Negri + GE Research – Integration of vmtk with Slicer 3
* Iowa + Isomics - Finger Bone Biomechanics
* CalTech + Kitware - Systems Biology and Genomic Science
* BWH + Wake Forest + Virginia Tech - Alcohol Stress in Primates
* BWH + MGH - Radiation Treatment Planning'
* Iowa + Kitware + BWH - Non Linear Registration Tools
* Northwestern + Isomics - Radiology Translation Station
* Harvard IIC + Isomics + GE Research - Astronomy Analysis and Visualization
* Virginia Tech + BWH - Applying EMSegmenter to nonhuman primate neuroimaging
* JHU + Queen's + BWH - Brachytherapy needle positioning robot integration
* Iowa + Kitware + BWH - Nonrigid registration
* Iowa + BWH - Developing electronic atlas
* Iowa + Kitware - GUI for nonridig image registration
* Canary Islands Technological Institute + Isomics + GE Research - DICOM Query/Retrieve
* Canary Islands Technological Institute + GE Research - Block matching registration
* UNC + Duke University Medical Center - DTI tractography analysis in depression study
* <others to be filled in>

The collaborative nature of NA-MIC is exemplified by the attendance at the NA-MIC All Hands Meeting and the NA-MIC Summer Project Week. Researchers from within and external to NA-MIC come together at these two events to forge collaborations. At the 2007 NA-MIC All Hands Meeting alone, there were 96 attendees: 56 NA-MIC researchers, 32 NA-MIC collaborators from 13 institutions, and 8 members of the External Advisory Board and NIH. At the Project Half-Week run in conjunction with the All Hands Meeting, there were 38 projects: 16 initiated from the algorithm core, 10 specific to the engineering core, and 11 from external collaborators.

More detailed information on collaborations as well as Project Week events can be found at:
* http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations
* http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events

===Q2.5. What new training opportunities have the centers provided?===

**Answer: http://www.na-mic.org/Wiki/index.php/Training:Main

* The requirement for all NCBC's to dedicate funds for training provides the opportunity to develop a targeted and deep portfolio of training resources. The unique perspective of providing these training opportunities and resources specifically targeted to a multi-disciplinary audience of basic and clinical biomedical scientists, computer scientists and medical imaging scientists is fostered by the NCBC Program. Traditional funding by NIH research grants results in allocation of all funds into the primary research; NIH training grants allocate funding for the support of trainees; the few education grants are restricted in budget and overhead thus severely limiting the quality and quantity of educational resources that can be offered. The NCBCs have created a new opportunity for our cadre of experienced clinician scientists, computer scientists and medical image analysis experts affiliated with our large centers to be supported to work on outreach activities.

* Within NA-MIC, this perspective has given rise to a thriving training program that supports the biomedical research community within NAMIC, across the NIH community and around the world. The strong demand for our training resources is evident from the large number of hits to our training web pages, from the rapid enrollment in all offered workshops, and the positive feedback from participants. We believe, and our belief is supported by the documented backgrounds of our workshop attendees, that a key aspect of our training materials that makes them useful to the community is that they are learner-centered, goal-oriented, and targeted to bridge the gaps in technical knowledge and language that exist between basic and clinical biomedical scientists, computer scientists and medical imaging scientists. For example, a tutorial that teaches how to use Slicer to register two images includes not only the necessary details of how to implement the algorithm, but also the conceptual framework for the registration approach, the mathematical underpinnings of the algorithm and a detailed anatomical approach for visually inspecting and refining the registration. This rich, but simple approach provides a consistently educational experience for every new user of the NAMIC toolkit.

* NAMIC supported new training opportunities are developed to maximize impact on the wider scientific community. The primary vehicle for this is "Slicer 101", our portfolio of Slicer training tutorials (http://www.na-mic.org/Wiki/index.php/Slicer:Workshops:User_Training_101). We have focused our efforts on making all our tutorial materials available via the NA-MIC Wiki as downloadable Powerpoint presentations and accompanying curated, anonymized datasets. The tutorials are all carefully tested on multiple computer platforms and by our team before being used in live Workshops (http://www.na-mic.org/Wiki/index.php/Training:Events_Timeline). Refinements are made based on the feedback of the audience and our experience during the teaching sessions. The final product of our work allows any new users, regardless of educational background, to not only use the NAMIC tools and algorithms, but to understand what they are doing and why. To date we have had over 7,880 hits to the Slicer 101 webpage.

* NAMIC supported Workshops are another unique venue for multi-disciplinary training. In addition to the all the points made regarding the content of the training materials, the 14 Workshops run by the NAMIC Training core over the past 3 years have each provided the opportunity for new connections to be made among basic and clinical biomedical scientists, computer scientists and medical imaging scientists. All our Workshops provide opportunities for formal and informal discussions among attendees of diverse backgrounds and strengths. These hands-on, interactive workshops allow participants to translate concepts of medical image processing into skills through instructor-led training. The simplicity of our approach, and the exceptional quality of the NAMIC toolkit, ensures a very high success rate for knowledge and skill acquisition. We estimate that 370 people from 52 different universities and companies attended our Workshops between 2005 and 2006.

* We are currently focusing our efforts on reaching a wider community by delivering a more didatic based Workshop in conjunction with the upcoming Organization for Human Brain Mapping meeting in Chicago next week. We held our enrollment to 50 so that we could offer the same hands-on interactive training experience to the attendees and our registration filled within a few weeks of the offering being posted. Tenatively, we anticipate that more than 12 countries and 14 states within the US will be represented at this upcoming Workshop.

* A final point is that this commitment and focus on training permeates all aspects of the NAMIC program. All large gatherings of NAMIC personnel including All Hands Meetings, Programming/Project Weeks, and Core meetings provide venues for our culture of training to be expressed. Each gathering creates an opportunity to build bridges between our participating disciplines and to improve the communication skills of each member. This culture includes implicit aspects such as a supportive and collegial environment that encourages questions and critical feedback, as well as explicit aspects such as encouraging junior level participants to make presentations and scheduling educational presentations from domain experts within and outside of the community. We believe that the positive attitude towards sharing knowledge and skills is fostered in all who are associated with our Project.

===Q2.6. What changes could make the program more effective in the future?===

* ''National Visibility''

The overall objective statement of the NCBC program calls for building computational infrastructure for the nation's biomedical research efforts -- this ambitious goal includes both computational and biomedical science, areas in which the existing centers have extensive experience, but also in nationwide "marketing" efforts where the scientific community is less adept. Several centers, including NA-MIC, have developed novel approaches to this problem through their training and dissemination cores, but as the output of the centers grows along with the number of potential users in the community and the potential impact, these critical resources will be increasingly strained. There are several approaches the overall program could take to address this issue including supplemental funding to host workshops or conferences, providing small travel grants for researchers or students to visit the centers, or actively encouraging a wider range of NIH funded researchers to adopt the tools generated by the NCBCs. In particular, collaboration PAR has been very effective as a mechanism for encouraging busy scientists to consider adopting the NCBC tools and is an excellent example of how the program can extend the impact of the basic investment in scientific infrastructure.

* ''Local Autonomy''

The program should avoid adding extra layers of uniformity to what are fundamentally unique centers. The NCBC program has successfully established a distributed network of centers drawing on the expertise of some of the nation's leading researchers drawn to the program for the opportunity to develop and apply their know-how to this ambitious effort. This rich environment, predictably, yields a diversity of approaches and organizational structures as each of the centers works to implement their particular vision of how to fulfill the overall mission of the NCBC program. Preserving the vitality of the effort depends on retaining this autonomy as each center strives to meet the individual objectives suited to their communities. The program needs well defined goals that each center must meet, but the overall program should facilitate the individual solutions of the center's leadership.

* ''Stability''

One critical mission of the NCBC program is the creation of infrastructure. These large-scale efforts require time-horizons that are incompatible with the current framework of many programs at NIH. It might take two or three iterations until a software package is sufficiently mature to be attractive to a larger community of scientists. Large software platforms require engineering staff of 10-20 in addition to the biomedical scientists developing functionality aimed at solving a particular problem. Many of the NCBCs are working on several packages. This results in underfunded projects which compromises the timeliness and performance of the resulting software.

It would be advisable to increase funding for the engineering and outreach activities of the centers and provide the funding in a reliable way. The continous stream of cuts from of the original budget has made this discrepancy even more pronounced. In 2007/2008 we will receive only 77.7% of the money that was budgeted in the application for that year. Following the RFA guidelines, the original budget did not contain adjustments for inflation. Furthermore, in deviation from the way that most NIH programs are funded, the budget was frozen in total dollars not direct dollars. Institutional overhead rates, and fringe and benefit rates have increased for several of the NA-MIC participants during the last three years and have resulted in further decreases in the amount of money available to actually do research.

===Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing?===

* ''Greater Start Up Time and Steeper Learning Curve Than Anticipated''

Bringing together computer scientists, engineers, and biomedical researchers, with diverse interests, training, and background, for the purpose of working on a set of biological problems, is no easy feat and, in retrospect required a steeper learning curve than was anticipated. This steeper learning curve is understandable, since the main focus initially was on developing alliances among the cores in order to increase awareness about the kinds of tools needed for the specific imaging problems posed by the biomedical researchers who were driving the biological problems. The first year of the grant, as noted in our annual report, thus reflected a "core" emphasis, as an interdisciplinary team was brought together, many members for the first time. It was not until the second year of the grant that the focus on a "core" emphasis shifted to a focus on "themes", which cut across "core" boundaries. While this shift was viewed as part of a natural evolution, now that we know this, we can help guide future NIH efforts by suggesting that specific projects/clinical applications should be highlighted in the first few months of the grant, based on meetings among core members, so as to facilitate a focus on clinical applications from the outset.

Such an early emphasis on clinical applications/problems would also facilitate an early focus on the development and application of computational tools, which could be more closely aligned with specific clinical problems and applications. This would breakdown artificial barriers that a "core" focus involves, which, while seemingly an inherent part of the initial stages, could be curtailed by highlighting early the need to focus on specific needed applications. In this way, the needed applications of the driving biological problems could form natural groupings that involve members from all cores, and work groups could be set up from the beginning that reflect a "theme"/"application" approach. This would also assist in more communication between core members, which would also likely facilitate ongoing communication among computer scientists, engineers, and biomedical researchers.

* ''Developing Robust Software for Advanced Applications is Difficult''

Creating industrial-strength software solutions to support scientific investigations is time consuming and requires skill not usually found in the academic environment. NA-MIC has been successful at bringing in commercial software development expertise to help accomplish the center's goals (GE, Kitware, Isomics) but these resources are routinely stretched well beyond the allocated budgets due to the many research directions of the center's scientists. When considering biomedical computing projects, the NIH must not attempt to short change the development process or the resulting systems run a greater risk of being difficult to maintain, difficult to scale up, and incompatible across systems.

An excellent example of the level of effort needed for a successful, extensible, cross-platform product is the National Library of Medicine's Insight Toolkit (ITK). The organizational meeting for ITK was held in October 1999. Between October 1999 and October 2002, fifty developers contributed code from six prime contractors (GE, Kitware, Insightful, UNC, Utah, and UPenn) and four sub-contractors (BWH, UPenn, Pitt, and Columbia) to produce ITK Version 1.0. To date, over $13.5 million has been awarded by the NIH for the development, use, and expansion of ITK. That total includes 20, one-year contracts that were given to early adopters of ITK. When assessing the level of effort expended on ITK, it is important to consider that ITK's funding did not cover algorithm, graphical user interface, or visualization developments. ITK costs only funded the integration of existing methods into a common library. Developing end applications, involving user interfaces and visualizations tailored for clinical users, requires significant additional effort.

* ''Algorithm Development Needs to be Interactive and Not Sequential''

In reviewing the last three years of the driving biological problem, schizophrenia, it is evident that tool development that involved multiple interactions among members of Core 1 (Computer Scientists), 2 (Engineers), and 3 (Driving Biological Problem), at all stages of development, led to the development of computational tools that were both more tailored to the specific applications needed by Core 3 members, as well as to the development of tools that were more optimized for general use. This interactive mode of tool development is in contrast to tool development that proceeded more sequentially, where one or several members of Core 1 and 3 met, and then Core 1 proceeded with what their understanding was of the problem and went off and developed a tool with very little further input from Core 3 until the tool was delivered. The latter approach often resulted in delays in receiving the tool, as there was less communication between Core 1 and Core 3 members in these instances, and often the tool did not really meet the specific needs of the application without further work. In the future, and based on this experience, NIH initiatives should emphasize the importance of encouraging a more "interactive" approach to tool development and to discouraging what is termed here as a more "sequential" approach to tool development. With a more interactive approach, progress can be more readily evaluated at each phase of tool development, and input and testing can be provided based on more communication among members of Core 1, 2, and 3. A more "interactive" model is also far more responsive to the needs of the driving biological problem, and also keeps the focus on the clinical application.

Focusing on interactions across core members will also likely facilitate breaking down the steep learning curve inherent in early interactions across cores members (see above).

* ''Funding''

As mentioned above, there is a serious mismatch between the objectives set by NIH and the resources available to address them. A further aggravation were the additional cuts imposed over the last three years, combined with a "requirements creep". The future increased interaction between the centers will require either additional resources or decommittment for existing activities. If the centers are as successful as envisioned, an increasing number of scientists will want to interact with the scientists at the centers which will also require additional resources.

NIH will need to allocate significant additional resources to the centers in order to fulfill the vision of a network of centers of biomedical computing at the center of an ever increasing network of biomedical scientific programs benefiting form their software and services.

=='''Q3:A list of publications and/or software tools produced by the Center.''' If this information is provided in your progress report or is available on your website, a link will be sufficient. We are especially interested in your assessment of the maturity of your software tools and the impact they are having on the scientific community.===

A3:
*NA-MIC Publications are available here: http://www.na-mic.org/Wiki/index.php/Publications.
*NA-MIC Software Tools are available here: http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit
The Center has created and extended a number of software tools to handle some of the key problems in medical imaging analysis and to deliver these computational technologies via a suite of applications, toolkits, and infrastructure facilities. A summary description of these tools includes:
* Slicer3 (application) - a application platform for deploying imaging technologies, newly architected with an execution model facilitating integration, work flow, and large scale computing. While Slicer3 is the newest addition to the NAMIC Kit, it is built on pre-existing, mature toolkits so that the application is relatively mature, and is already in use. Because Slicer3 supports plug-in modules, active development is proceeding to create and package various modules for dissemination to the NAMIC community.
* ITK (toolkit) - a mature system for image analysis, registration and segmentation (initially created in 1999). ITK is in use worldwide for medical imaging research and development.
* VTK (toolkit) - a mature system for visualization, graphics, volume rendering, and interaction (initially created in 1993). VTK is used worldwide for research, teaching and commercial development.
* DART (computational infrastructure) - a key component of the NAMIC quality control process, DART is used to coordinate and report the software testing process. It was created in the first year of NAMIC and is in constant use, therefore a mature system.
* CMake/CTest/CPack (computational infrastructure) - CMake and CTest are relatively mature systems used to manage the building and testing of software across diverse computer platforms. CMake is used worldwide by some of the [http://lwn.net/Articles// world's largest open source systems such as KDE]. CPack, a recent addition to the NAMIC kit, is used to simplify the packaging and dissemination of software across platforms. Thus in NAMIC we can easily deploy our software across Windows, Linux, Unix, and Mac platforms.
* Other tools (computational infrastructure) - Many other software tools are used to support the development of advanced imaging applications, and to assist with large scale computing, including
** Teem - image processing tools (mature)
** KWWidgets - Open source, cross platform GUI toolkit (mature, but development continues to support workflow).
** BatchMake - Support large-scale computing, including grid computing, for performing large population studies and statistical analysis (under active development).
** GridWizard - an application scheduler aimed at allowing researchers to easily harness the power of large computational grids. It lets you run tens of thousands of commands simultaneously on multiple clusters of computers by typing a single command, without writing scripts. It can be used by itself, and is currently being integrated with Slicer3 and as part of a web-based portal environment. (http://www.na-mic.org/Wiki/index.php/Slicer3:Grid_Interface)

These tools address key problems in imaging including segmentation, registration, visualization, and shape analysis; or provide facilities supporting researchers and developers who wish to create advanced software applications. One of key characteristics of NAMIC is that we treat the development of advanced medical image analysis software holistically; that is, the complete cycle of algorithm design, efficient impmentations, quality control and dissemination are needed to effectively address challenges provided by the driving biological problems. Examples of how these tools are being used include the following:

(a) Segmentation: Here there are a variety of tools of varying degrees of maturity ranging from the EM Segmentor (a widely distributed, mature algorithm included in Slicer3 as an application plug-in) to more recent work on DTI segmentation based on directional flows which are Matlab and C++ based. Powerful mature tools such as Bayesian segmentation have been recently included in Slicer (and have been available in ITK for some time now) which can be combined with very recent work on the semi-automated segmentation of the DPFC done in collaboration with Core 3 researchers. Further, tools previously developed by NAMIC researchers which have had a wide distribution have been put into Slicer. For example, geometric based segmentation methods (some of which were included in packages marketed by GE) were tailored for cortical segmentation, and included in the Slicer, and in fact even improved with the inclusion of statistical based approaches.

(b) Registration: Similar remarks can be made for registration in which we have a spectrum ranging from very mature methods to very recent ones which are still being tested. In particular, mature widely distributed methodologies for rigid registration are now included in ITK, as well as spline-based registration methodologies. These are well-tested methods which have been made accessible to the general imaging community. Newer methodologies such as those based on optimal transport for elastic registration are being included in ITK. NAMIC has also pushed for fast implementations of its algorithms to be used on cheap widely available platforms. Taking a cue from the game industry, some algorithms have been ported to GPUs (graphics cards) which are being employed now as computing devices. This has led to a speed-up of almost two orders of magnitude on some of the registration algorithms being tested.

(c) Shape Analysis: Again a number of methodologies have been developed and implemented with varying levels of maturity. Shape methodologies based on spherical harmonics are quite mature, and are available in pipelines developed by NAMIC researchers and have been distributed to the general community. A newer spherical based wavelet shape analysis package has been put into ITK, which also drives a novel shape-based segmentation procedure. More globally based spherical harmonic ideas have been combined with the multi-resolution spherical wavelet approach as a statistical shape based package for schizophrenia. This general technique may be used for other purposes as well, and is presently being ported to some work being done on the prostate. Work has also been accomplished on particle based approaches to this important problem area with the code put into ITK. Many times we work with a Matlab/C++ initial version of our codes, then move to ITK, and finally to Slicer. However, even at the Matlab/C++ stage, algorithms have been distributed and used in a clinical setting (for example, rule-based brain segmentation approaches).

(d) Diffusion Weighted Image (DWI) Analysis: A number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical analysis of fiber bundles have been contributed to the NAMIC toolkit. Some of these tools have been already integrated into diffusion dedicated ITK package with GUI as part of the NAMIC toolkit (e.g., FiberViewer-UNC) and into the Slicer platform (BWH tractography, clustering). The impact of NAMIC DWI analysis activities is best characterized by most recent journal articles and journal articles in print. The application of the NAMIC FiberViewer tool (UNC) in large clinical studies at UNC and Duke are in print (Taylor et al, Gilmore et al., Cascio et al.). Clinical application of the Slicer DTI package by BWH/MIT is reported in O’Donnell et al. Kuroki N. et al. and Nakamura et al.. The research by MGH is found in two journal publications by Tuch et al.. The description of the methodologies also appeared or will appear in peer reviewed journals (Corouge et al., Fletcher et al., Corouge et al.). New methods in development (Finsler metric GT, volumetric PDE based path analysis Utah, stochastic tractography BWH), and path of interest MGH, are currently tested on Core-3 DBP data, with the goal to give recommendations on which type of solution is appropriate to solve specific clinical analysis questions.

(e) Visualization: Core 2 researchers involved with NAMIC now (e.g., the founders of Kitware) were at the forefront of developing VTK (and of course ITK). Thus here we are considering technologies which are at a commercial level of development, and used at thousands of sites. Algorithms developed at NAMIC have driven new directions for these packages. Newer visualization methods, for example, the conformal flattening procedure have been ported to an ITK filter and is in the NAMIC Sandbox. Quasi-isometric methods for brain flattening from the MGH Free Surfer have become part of the NAMIC enterprise as well. These flattening procedures are very easy to use, and may also be employed for registration. Code for the control of distortion of the area in flattening has been incorporated which give area-preservation with minimal distortion. The techniques may be also used for several other purposes included automatic fly-throughs in endoscopy (incorporated into Slicer), and for texture mappings for general visualization purposes.

==Logistics==
When completed, the information should be sent to:

Gwen Jacobs, PhD

Professor of Neuroscience

Asst. CIO and Director of Academic Computing

1 Lewis Hall

Montana State University

Bozeman, MT 59717

406-994-7334 - phone

406-994-7077 - FAX

gwen@cns.montana.edu <mailto:gwen@cns.montana.edu>

2007 Materials for NCBC Program Review

2007-06-08T11:50:52Z

Shenton: /* Q2.2 In what ways has the NCBC initiative advanced biomedical computing? */

==Materials requested for NCBC Program Review==

These are due to Gwen Jacobs by Friday, June 08, 2007.

=='''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''==

Summary: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#1._Introduction

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with respect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs include a focus on Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

Highlights: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#3._Highlights

The third year of the NA-MIC project saw continued development and dissemination of medical image analysis software. The current progress is clearly characterized by a significant increase in the application of Core-1 and Core-2 tools to image data provided by the Core-3 DBP groups. The main reasons for this progress is two-fold, first there are several new methods that are out of the prototype stage and ready to be applied to large sets of imaging datasets, and second there are new high-resolution imaging data from high-field scanners that are more appropriate for the new tools than historical data with often very coarse slice resolution.

With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NA-MIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NA-MIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

*Advanced Algorithms
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NA-MIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NA-MIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The NA-MIC toolkit includes a comprehensive set of modules for analysis of diffusion weighted images (DWI), including improved calculation of tensors, interpolation, nonlinear deformation and statistics on tensor fields, novel methods for tractography and for optimal path finding, and clustering of sets of streamlines.
* A quantitative tractography package for user-guided geometric parametrization and statistical analysis of fiber bundles (FiberViewer) has been contributed to the NAMIC toolbox. This tool used in several ongoing clinical DTI studies.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NA-MIC Sandbox in preparation for formal acceptance into the NA-MIC Kit.
*Technology Deployment Platform: Slicer3
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NA-MIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* Slicer3's execution model is also allowing researchers to access large computational resources. Two applications that support the use of such resources, BatchMake and GridWizard, have been recently integrated with Slicer3 for specific modules. The GUI for the modules that are to be run via these computational support applications allow for the creation of large population studies or parametric studies of an algorithm.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NA-MIC Kit, and are now available to all other modules built on the Slicer3 framework.

*Outreach and Technology Transfer
Cores 4-5-6 continue to support, train and disseminate to the NA-MIC community, and the broader biomedical computing community.
* NA-MIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NA-MIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair. The training and dissemination will continue with a DTI workshop at the forthcoming Human Brain Mapping meeting and activities at MICCAI 2007, among others.

==Q2==
'''A brief statement - (one page per question, max) addressing each of the questions listed below. These are the questions that we have been asked to address in our report. Our goal in asking for this information is to be able to produce a report that reviews the program as a whole. Your view, from the vantage point of the center you direct, is critical to our work. In addition, your answers will provide us with more information that we can use in our discussion with program staff on June 11th. We know that some of this information can be found on your websites, so in those cases a link to the information would be most helpful.'''

===Q2.1 To what extent does the vision and direction of the NCBC initiative promote biomedical computing?===

The RFA for the creation of the NCBC program laid out a very explicit vision.

The NCBCs are to be the core of the networked national effort to build the computational infrastructure for biomedical computing in the US. From the [http://www.bisti.nih.gov/ncbc/ NIH website] in 2004: Four new National Centers for Biomedical Computing (NCBC) will develop and implement the core of a universal computing infrastructure that is urgently needed to speed progress in biomedical research. The centers will create innovative software programs and other tools that enable the biomedical community to integrate, analyze, model, simulate, and share data on human health and disease. The original [http://grants1.nih.gov/grants/guide/rfa-files/RFA-RM-04-003.html RFA] stated: 1. The software should be freely available to biomedical researchers and educators in the non-profit sector and 2. The terms of software availability should permit the commercialization of enhanced or customized versions of the software.

As the amount of data produced by biomedical researchers is increasing at an ever accelerating pace, the relative importance of computing as an integral part of analysis is increasing as well. In addition both data and analyses are getting more and more complex. Increasingly, science is performed by interdisciplinary teams at multiple locations. Industrial strength research platforms are one of the emerging needs of biomedical research. This need was only been partially addressed before the creation of the NCBC program. The NCBC centers are in an outstanding position to develop stable, maintainable, and expandable software to address these needs.

In addition to building the infrastructure for biomedical computing, NCBCs play an important role in fostering a community of computational scientists dedicated to solving problems in the biomedical domain. In its short three-year life span, NA-MIC NCBC has contributed to this vision by holding numerous workshops and tutorials on open source software for biomedical image analysis, by supporting the Insight Journal, a peer-review venue for publications accompanied with open-source implementation, and by engaging a large number of graduate students in Computer Science programs in biomedical computing research. By creating high-profile bio-computation programs, the NCBC initiative brings the biomedical computing as a field of research to the attention of the computation community and actively promotes collaborations between computational and biological sciences.

The participants in our center signed on for this project because they believe in the vision of developing a universal computing infrastructure for medical image computing. NA-MIC is creating an open source platform that embodies this vision for the field of medical image computing. We have settled on a very liberal open source license for our software plattform. All the components of that plattform, called the NA-MIC kit, are distributed under a BSD style licence without restrictions on commercial use. Other centers have adopted different strategies to address the requirement for open source and to enable commercial use, as required in the original RFA; we believe the NA-MIC liberal license approach minimizes barriers to wide adoption and is will maximize return on the NIH investment. The availability of software platforms commoditizes infrastructure for research and allows individual researchers to spend more time on their core research. Over time, this will promote biomedical computing by lowering the hurdle for scientists to use the technology.

===Q2.2 In what ways has the NCBC initiative advanced biomedical computing?===
Ron:(Marty edits also)

It is too early to say how the NCBC initiative advanced biomedical computing, though the results thus far are promising. Four of the NCBC's have been funded since October 2004 (less than three years), and funding for the other three began in late 2005. Center efforts of this size need significant time and effort to get organized and to synchornize activities of all participants. This startup effort is where the primary focus of the centers has been until now. There are early signs that some of the centers are beginning to emerge from this phase of their evolution and turning toward activities aimed at the field at large. However, it will be several years before the full impact of this program will become visible.

Below is a more detailed discussion of the specifics of this evolution from the vantage point of NA-MIC

(Tina)

Answer:http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#4._Impact_and_Value_to_Biocomputing

In NA-MIC's third year, it is clear that NA-MIC is developing a culture, environment, and resources to foster collaborative research in medical image analysis that draws together mathematicians, computer scientists, software engineers, and clinical researchers. These artefacts of NA-MIC impact how NA-MIC operates, make NA-MIC a fulcrum for NIH funded research, and draws new collaborators from across the country and around the world to NA-MIC.

*Impact within the Center

**Within the center, the NA-MIC organization is nimble, forming ad hoc distributed teams within and between cores to address specific biocomputing tasks. Information is shared freely on the NA-MIC Wiki, on the weekly Engineering telephone conferences, and in the NA-MIC Subversion source code repository. The software engineering tools of CMake, Dart 2 and CTest, CPack, and KWWidgets facilitate a cross platform software environment for medical image analysis that can be easily built, tested, and distributed to end-users. Core 2 has provided a platform, Slicer 3, that allows Core 1 to easily integrate new technology and deliver this technology in an end user application to Core 3. Core 1 has developed a host of techniques to apply to structural and diffusion analysis which are under evaluation by Core 3. Additionally, Core 3 members are heavy users of the Slicer 3 software and they provide ongoing feedback to Core 2 with respect to features that are optimal and those that need to be refined. They also assist in refining the user interface of this software platform. Core 3 members are also actively using the software and new tools developed by applying them to address scientific questions of interest, as is evident from the new publications using these tools. Major NA-MIC events, such as the annual All Hands Meeting, the Summer Project Week, the Spring Algorithms meeting, and Engineering Teleconferences are avidly attended by NA-MIC researchers as opportunities to foster collaborations.

*Impact within NIH Funded Research

** Within NIH funded research, NA-MIC continues to forge relationships with other large NIH funded projects such as BIRN, caBIG, NAC, and IGT. Here, we are sharing the NA-MIC culture, engineering practices, and tools. The BIRN infrastructure, built on widely-accepted grid middleware, allows NA-MIC researchers to share data, access computational resources and provides a rich collaborative environment through a science portal. caBIG lists the 3D Slicer among the applications available on the National Cancer Imaging Archive. NAC and IGT use the NA-MIC infrastructure and are involved in the development of the 3D Slicer. BIRN recently held an event modeled after the NA-MIC Project Week. NA-MIC has become a resource on open source licensing to the medical image analysis community.

NA-MIC is also attracting NIH funded collaborations. Two grants have been funded under PAR-05-063 to collaborate with NA-MIC: Automated FE Mesh Development and Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI. Five additional applications to collaborate with NA-MIC via the NCBC collaborative grant mechanism are under consideration. Additional grant applications submitted under other calls are planning to use and extend the NA-MIC tools. The first collaborative R01 from a previous driving biological problem has also been submitted by Drs. Shenton and Saykin to follow up on important tool development ideas that will combine multi-modal imaging to address specific questions relevant to brain circuitry abnormalities in schizophrenia.

*National and International Impact

**NA-MIC events and tools garner national and international interest. There were nearly 100 participants at the NA-MIC All Hands Meeting in January 2007, with many of these participants from outside of NA-MIC. Several researchers from outside the NA-MIC community have attended the Summer Project Weeks and the Winter Project Half-Weeks to gain access to the NA-MIC tools and people. These external researchers are contributing ideas and technology back into NA-MIC.

**Components of the NA-MIC kit are used globally. The software engineering tools of CMake, Dart 2 and CTest are used by many open source projects and commercial applications. For example, the K Desktop Environment (KDE) for Linux and Unix workstations uses CMake and Dart. KDE is one of the largest open source projects in the world. Many open source projects and commercial products are benefiting from the NA-MIC related contributions to ITK and VTK. Finally, Slicer 3 is being used as an image analysis platform in several fields outside of medical image analysis, in particular, biological image analysis, astronomy, and industrial inspection.

**NA-MIC co-sponsored the Workshop on Open Science at the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2006 conference. The proceedings of the workshop are published on the electronic Insight Journal, another NIH-funded activity.

**Over 50 NA-MIC related publications have been produced since the inception of the center.

===Q2.3 Are the NCBCs interfacing appropriately?===

At this point in the project cycle, the NCBC's have focused on interactions with the DBPs, developing requirements, creating foundational technology, and integrating existing technologies within the Center. In many cases such as with NAMIC, the resulting technologies are actively being disseminated to the research community. It is only recently that outreach across NCBCs is an effective use of resources. There have been initial efforts towards collaboration, such as cross-center use of software tools (for example, SimBios/SimTk actively uses NAMICs VTK toolkit), and the promising Software and Data Integration Working Group (SDIWG). In addition, NA-MIC as has interactions with CCB and Simbios. NA-MIC also interfaces with a number of large-scale efforts which are not NCBC but are comparable in scale: BIRN, several national resource centers and to some extent, with CaBIG.

In that sense, the answer is yes, the NCBC's are interfacing appropriately at this time. Within the next few years there will be a need to increase the extent of interfacing significantly. That will require either reallocation of resources or the allocation of new additional resources. Due to the large variety of approaches adopted by the different centers it will be likely necessary to to allocate significant amounts of time by both senior leadership at the centers for identifying objectives of such interface efforts and by the engineering cores to actually execute the plans. A serious effort in this direction will probably require allocation of multiple FTE's.

===Q2.4. What new collaborations have been formed through the NCBC initiative?===

NA-MIC’s structure and organization has facilitated many new collaborations. NA-MIC is a distributed center, bringing together mathematicians, computer scientists, software engineers, and clinicians from multiple sites. This distributed structure provided two types of new collaborations within NA-MIC: new collaborations between cores and new collaborations within cores. For between core collaborations, many of the algorithm and engineering core researchers had not collaborated previously with the researchers in either the first or second round of Driving Biological Projects (DBPs). Thus, the NCBC provided a unique opportunity for the algorithm and engineering core researchers to gain clinical insight and to adapt and tune their algorithms and tools to new clinical contexts. Conversely, the DBPs gained access to algorithms and tools that they previously had not utilized. Similarly, many of the algorithm core researchers and engineering core researchers had not previously collaborated. Thus, the NCBC exposed the researchers in the algorithm core to the tools and engineering practices of the engineering core and exposed the researchers in the engineering core to the computational techniques and data structures utilized by the algorithms core. For within core collaborations, many of the researchers within the algorithm core had not previously collaborated. Through NA-MIC, these researchers have been able to cooperate and also amicably compete to address the issues brought forth by the DBPs.

Below is a list of new collaborations within NA-MIC. This list was compiled from the complete list of [[NA-MIC_Collaborations | NA-MIC Collaborations]] and project lists from the 5 NA-MIC [[Engineering:Programming_Events |Project week events]]. Best effort was made to filter these lists down to just the new collaborations (groups of researchers) formed under NA-MIC.

* Georgia Tech + UC Irvine – Rule based segmentation algorithm for DLPFC
* Georgia Tech + Kitware - Knowledge-based Bayesian classification and segmentation
* BWH + MIT + Kitware - Brain tissue classification and subparcellation of brain structures
* Georgia Tech + UNC + BWH - Multiscale shape segmentation techniques
* BWH + Dartmouth + UNC + Georgia Tech - Shape analysis of the caudate and corpus callosum
* Georgia Tech + UNC + BWH - Spherical wavelet based shape analysis for Caudate
* Georgia Tech + UNC + BWH - Multiscale shape analysis of the hippocampus
* Dartmouth + UNC + BWH - Shape analysis of tbe hippocampus
* Utah + UNC + BWH - Automated shape model construction
* Dartmouth + Isomics - Neural substrates of apathy in schizophrenia
* Georgia Tech + GE Research + Kitware - Spherical Wavelet Transforms
* Georgia Tech + UNC - Shape analysis with Spherical Wavelets
* Utah + UNC - Adaptive, particle-based sampling for shapes and complexes
* UNC + Utah + Harvard - Tensor estimation and Monte-Carlo simulation
* Harvard + MIT + UNC - Corpus Callosum Regional FA analysis in Schizophrenia
* Dartmouth + MGH + Isomics + BWH - Integrity of Fronto-Temporal Circuitry in Schizophrenia using Path of Interest Analysis
* MGH + Isomics - ITK implementation of POIStats, and Integration into Slicer3
* UC Irvine + MGH + UNC + MIT - DTI Validation
* Utah + UNC + GE Research - DTI Software and Algorithm Infrastructure
* Utah + BWH - Tensor based statistics
* Utah + BWH - Diffusion tensor image filtering
* MGH + Dartmouth + Kitware + GE Research - Non-rigid EPI registration
* Dartmouth + BWH - Neural Substrates of Working Memory in Schizophrenia: A Parametric 3-Back Study
* Dartmouth + BWH - Brain Activation during a Continuous Verbal Encoding and Recognition Task in Schizophrenia
* Dartmouth + BWH - Fronto-Temporal Connectivity in Schizophrenia during Semantic Memory
* UC Irvine + Toronto - Imaging Phenotypes in Schizophrenics and Controls
* MIT + Isomics + GE Research + Kitware - fMRI statistics software
* MIND + Isomics + MGH - Analysis of Brain Lesions in Neuropsychiatric Systemic Lupus Erythematosis
* JHU + Queen's + BWH + Georgia Tech - Segmentation and Registration Tools for Robotic Prostate Interventions
* UNC + GE Research - Longitudinal MRI study of early brain development
* BWH + Kitware + MIT - Velocardiofacial Syndrome (VCFS) as a genetic model for schizophrenia
* UNC + GE Research + BWH - DTI population analysis
* Georgia Tech + BWH - Geodesic tractography
* BWH + Queen's + GE Research - Display optimization
* UCSD + Isomics - Dendritic Spine Morphometrics
* <others to be filled in>

NA-MIC has also attracted researchers from the field who were not originally part of NA-MIC. Some of these new collaborations are formally organized using the NIH NCBC Collaborative R01 program. But other collaborations are being driven solely by the opportunity to share resources, techniques, capabilities, and ideas.

Below is a list of new collaborations between external researchers and NA-MIC. Again, best effort was made to only list the new collaborations with external parties.

* Mario Negri + GE Research – Integration of vmtk with Slicer 3
* Iowa + Isomics - Finger Bone Biomechanics
* CalTech + Kitware - Systems Biology and Genomic Science
* BWH + Wake Forest + Virginia Tech - Alcohol Stress in Primates
* BWH + MGH - Radiation Treatment Planning'
* Iowa + Kitware + BWH - Non Linear Registration Tools
* Northwestern + Isomics - Radiology Translation Station
* Harvard IIC + Isomics + GE Research - Astronomy Analysis and Visualization
* Virginia Tech + BWH - Applying EMSegmenter to nonhuman primate neuroimaging
* JHU + Queen's + BWH - Brachytherapy needle positioning robot integration
* Iowa + Kitware + BWH - Nonrigid registration
* Iowa + BWH - Developing electronic atlas
* Iowa + Kitware - GUI for nonridig image registration
* Canary Islands Technological Institute + Isomics + GE Research - DICOM Query/Retrieve
* Canary Islands Technological Institute + GE Research - Block matching registration
* UNC + Duke University Medical Center - DTI tractography analysis in depression study
* <others to be filled in>

The collaborative nature of NA-MIC is exemplified by the attendance at the NA-MIC All Hands Meeting and the NA-MIC Summer Project Week. Researchers from within and external to NA-MIC come together at these two events to forge collaborations. At the 2007 NA-MIC All Hands Meeting alone, there were 96 attendees: 56 NA-MIC researchers, 32 NA-MIC collaborators from 13 institutions, and 8 members of the External Advisory Board and NIH. At the Project Half-Week run in conjunction with the All Hands Meeting, there were 38 projects: 16 initiated from the algorithm core, 10 specific to the engineering core, and 11 from external collaborators.

More detailed information on collaborations as well as Project Week events can be found at:
* http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations
* http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events

===Q2.5. What new training opportunities have the centers provided?===

**Answer: http://www.na-mic.org/Wiki/index.php/Training:Main

* The requirement for all NCBC's to dedicate funds for training provides the opportunity to develop a targeted and deep portfolio of training resources. The unique perspective of providing these training opportunities and resources specifically targeted to a multi-disciplinary audience of basic and clinical biomedical scientists, computer scientists and medical imaging scientists is fostered by the NCBC Program. Traditional funding by NIH research grants results in allocation of all funds into the primary research; NIH training grants allocate funding for the support of trainees; the few education grants are restricted in budget and overhead thus severely limiting the quality and quantity of educational resources that can be offered. The NCBCs have created a new opportunity for our cadre of experienced clinician scientists, computer scientists and medical image analysis experts affiliated with our large centers to be supported to work on outreach activities.

* Within NA-MIC, this perspective has given rise to a thriving training program that supports the biomedical research community within NAMIC, across the NIH community and around the world. The strong demand for our training resources is evident from the large number of hits to our training web pages, from the rapid enrollment in all offered workshops, and the positive feedback from participants. We believe, and our belief is supported by the documented backgrounds of our workshop attendees, that a key aspect of our training materials that makes them useful to the community is that they are learner-centered, goal-oriented, and targeted to bridge the gaps in technical knowledge and language that exist between basic and clinical biomedical scientists, computer scientists and medical imaging scientists. For example, a tutorial that teaches how to use Slicer to register two images includes not only the necessary details of how to implement the algorithm, but also the conceptual framework for the registration approach, the mathematical underpinnings of the algorithm and a detailed anatomical approach for visually inspecting and refining the registration. This rich, but simple approach provides a consistently educational experience for every new user of the NAMIC toolkit.

* NAMIC supported new training opportunities are developed to maximize impact on the wider scientific community. The primary vehicle for this is "Slicer 101", our portfolio of Slicer training tutorials (http://www.na-mic.org/Wiki/index.php/Slicer:Workshops:User_Training_101). We have focused our efforts on making all our tutorial materials available via the NA-MIC Wiki as downloadable Powerpoint presentations and accompanying curated, anonymized datasets. The tutorials are all carefully tested on multiple computer platforms and by our team before being used in live Workshops (http://www.na-mic.org/Wiki/index.php/Training:Events_Timeline). Refinements are made based on the feedback of the audience and our experience during the teaching sessions. The final product of our work allows any new users, regardless of educational background, to not only use the NAMIC tools and algorithms, but to understand what they are doing and why. To date we have had over 7,880 hits to the Slicer 101 webpage.

* NAMIC supported Workshops are another unique venue for multi-disciplinary training. In addition to the all the points made regarding the content of the training materials, the 14 Workshops run by the NAMIC Training core over the past 3 years have each provided the opportunity for new connections to be made among basic and clinical biomedical scientists, computer scientists and medical imaging scientists. All our Workshops provide opportunities for formal and informal discussions among attendees of diverse backgrounds and strengths. These hands-on, interactive workshops allow participants to translate concepts of medical image processing into skills through instructor-led training. The simplicity of our approach, and the exceptional quality of the NAMIC toolkit, ensures a very high success rate for knowledge and skill acquisition. We estimate that 370 people from 52 different universities and companies attended our Workshops between 2005 and 2006.

* We are currently focusing our efforts on reaching a wider community by delivering a more didatic based Workshop in conjunction with the upcoming Organization for Human Brain Mapping meeting in Chicago next week. We held our enrollment to 50 so that we could offer the same hands-on interactive training experience to the attendees and our registration filled within a few weeks of the offering being posted. Tenatively, we anticipate that more than 12 countries and 14 states within the US will be represented at this upcoming Workshop.

* A final point is that this commitment and focus on training permeates all aspects of the NAMIC program. All large gatherings of NAMIC personnel including All Hands Meetings, Programming/Project Weeks, and Core meetings provide venues for our culture of training to be expressed. Each gathering creates an opportunity to build bridges between our participating disciplines and to improve the communication skills of each member. This culture includes implicit aspects such as a supportive and collegial environment that encourages questions and critical feedback, as well as explicit aspects such as encouraging junior level participants to make presentations and scheduling educational presentations from domain experts within and outside of the community. We believe that the positive attitude towards sharing knowledge and skills is fostered in all who are associated with our Project.

===Q2.6. What changes could make the program more effective in the future?===

* ''National Visibility''

The overall objective statement of the NCBC program calls for building computational infrastructure for the nation's biomedical research efforts -- this ambitious goal includes both computational and biomedical science, areas in which the existing centers have extensive experience, but also in nationwide "marketing" efforts where the scientific community is less adept. Several centers, including NA-MIC, have developed novel approaches to this problem through their training and dissemination cores, but as the output of the centers grows along with the number of potential users in the community and the potential impact, these critical resources will be increasingly strained. There are several approaches the overall program could take to address this issue including supplemental funding to host workshops or conferences, providing small travel grants for researchers or students to visit the centers, or actively encouraging a wider range of NIH funded researchers to adopt the tools generated by the NCBCs. In particular, collaboration PAR has been very effective as a mechanism for encouraging busy scientists to consider adopting the NCBC tools and is an excellent example of how the program can extend the impact of the basic investment in scientific infrastructure.

* ''Local Autonomy''

The program should avoid adding extra layers of uniformity to what are fundamentally unique centers. The NCBC program has successfully established a distributed network of centers drawing on the expertise of some of the nation's leading researchers drawn to the program for the opportunity to develop and apply their know-how to this ambitious effort. This rich environment, predictably, yields a diversity of approaches and organizational structures as each of the centers works to implement their particular vision of how to fulfill the overall mission of the NCBC program. Preserving the vitality of the effort depends on retaining this autonomy as each center strives to meet the individual objectives suited to their communities. The program needs well defined goals that each center must meet, but the overall program should facilitate the individual solutions of the center's leadership.

* ''Stability''

One critical mission of the NCBC program is the creation of infrastructure. These large-scale efforts require time-horizons that are incompatible with the current framework of many programs at NIH. It might take two or three iterations until a software package is sufficiently mature to be attractive to a larger community of scientists. Large software platforms require engineering staff of 10-20 in addition to the biomedical scientists developing functionality aimed at solving a particular problem. Many of the NCBCs are working on several packages. This results in underfunded projects which compromises the timeliness and performance of the resulting software.

It would be advisable to increase funding for the engineering and outreach activities of the centers and provide the funding in a reliable way. The continous stream of cuts from of the original budget has made this discrepancy even more pronounced. In 2007/2008 we will receive only 77.7% of the money that was budgeted in the application for that year. Following the RFA guidelines, the original budget did not contain adjustments for inflation. Furthermore, in deviation from the way that most NIH programs are funded, the budget was frozen in total dollars not direct dollars. Institutional overhead rates, and fringe and benefit rates have increased for several of the NA-MIC participants during the last three years and have resulted in further decreases in the amount of money available to actually do research.

===Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing?===

* ''Greater Start Up Time and Steeper Learning Curve Than Anticipated''

Bringing together computer scientists, engineers, and biomedical researchers, with diverse interests, training, and background, for the purpose of working on a set of biological problems, is no easy feat and, in retrospect required a steeper learning curve than was anticipated. This steeper learning curve is understandable, since the main focus initially was on developing alliances among the cores in order to increase awareness about the kinds of tools needed for the specific imaging problems posed by the biomedical researchers who were driving the biological problems. The first year of the grant, as noted in our annual report, thus reflected a "core" emphasis, as an interdisciplinary team was brought together, many members for the first time. It was not until the second year of the grant that the focus on a "core" emphasis shifted to a focus on "themes", which cut across "core" boundaries. While this shift was viewed as part of a natural evolution, now that we know this, we can help guide future NIH efforts by suggesting that specific projects/clinical applications should be highlighted in the first few months of the grant, based on meetings among core members, so as to facilitate a focus on clinical applications from the outset.

Such an early emphasis on clinical applications/problems would also facilitate an early focus on the development and application of computational tools, which could be more closely aligned with specific clinical problems and applications. This would breakdown artificial barriers that a "core" focus involves, which, while seemingly an inherent part of the initial stages, could be curtailed by highlighting early the need to focus on specific needed applications. In this way, the needed applications of the driving biological problems could form natural groupings that involve members from all cores, and work groups could be set up from the beginning that reflect a "theme"/"application" approach. This would also assist in more communication between core members, which would also likely facilitate ongoing communication among computer scientists, engineers, and biomedical researchers.

* ''Developing Robust Software for Advanced Applications is Difficult''

Creating industrial-strength software solutions to support scientific investigations is time consuming and requires skill not usually found in the academic environment. NA-MIC has been successful at bringing in commercial software development expertise to help accomplish the center's goals (GE, Kitware, Isomics) but these resources are routinely stretched well beyond the allocated budgets due to the many research directions of the center's scientists. When considering biomedical computing projects, the NIH must not attempt to short change the development process or the resulting systems run a greater risk of being difficult to maintain, difficult to scale up, and incompatible across systems.

An excellent example of the level of effort needed for a successful, extensible, cross-platform product is the National Library of Medicine's Insight Toolkit (ITK). The organizational meeting for ITK was held in October 1999. Between October 1999 and October 2002, fifty developers contributed code from six prime contractors (GE, Kitware, Insightful, UNC, Utah, and UPenn) and four sub-contractors (BWH, UPenn, Pitt, and Columbia) to produce ITK Version 1.0. To date, over $13.5 million has been awarded by the NIH for the development, use, and expansion of ITK. That total includes 20, one-year contracts that were given to early adopters of ITK. When assessing the level of effort expended on ITK, it is important to consider that ITK's funding did not cover algorithm, graphical user interface, or visualization developments. ITK costs only funded the integration of existing methods into a common library. Developing end applications, involving user interfaces and visualizations tailored for clinical users, requires significant additional effort.

* ''Algorithm Development Needs to be Interactive and Not Sequential''

In reviewing the last three years of the driving biological problem, schizophrenia, it is evident that tool development that involved multiple interactions among members of Core 1 (Computer Scientists), 2 (Engineers), and 3 (Driving Biological Problem), at all stages of development, led to the development of computational tools that were both more tailored to the specific applications needed by Core 3 members, as well as to the development of tools that were more optimized for general use. This interactive mode of tool development is in contrast to tool development that proceeded more sequentially, where one or several members of Core 1 and 3 met, and then Core 1 proceeded with what their understanding was of the problem and went off and developed a tool with very little further input from Core 3 until the tool was delivered. The latter approach often resulted in delays in receiving the tool, as there was less communication between Core 1 and Core 3 members in these instances, and often the tool did not really meet the specific needs of the application without further work. In the future, and based on this experience, NIH initiatives should emphasize the importance of encouraging a more "interactive" approach to tool development and to discouraging what is termed here as a more "sequential" approach to tool development. With a more interactive approach, progress can be more readily evaluated at each phase of tool development, and input and testing can be provided based on more communication among members of Core 1, 2, and 3. A more "interactive" model is also far more responsive to the needs of the driving biological problem, and also keeps the focus on the clinical application.

Focusing on interactions across core members will also likely facilitate breaking down the steep learning curve inherent in early interactions across cores members (see above).

* ''Funding''

As mentioned above, there is a serious mismatch between the objectives set by NIH and the resources available to address them. A further aggravation were the additional cuts imposed over the last three years, combined with a "requirements creep". The future increased interaction between the centers will require either additional resources or decommittment for existing activities. If the centers are as successful as envisioned, an increasing number of scientists will want to interact with the scientists at the centers which will also require additional resources.

NIH will need to allocate significant additional resources to the centers in order to fulfill the vision of a network of centers of biomedical computing at the center of an ever increasing network of biomedical scientific programs benefiting form their software and services.

=='''Q3:A list of publications and/or software tools produced by the Center.''' If this information is provided in your progress report or is available on your website, a link will be sufficient. We are especially interested in your assessment of the maturity of your software tools and the impact they are having on the scientific community.===

A3:
*NA-MIC Publications are available here: http://www.na-mic.org/Wiki/index.php/Publications.
*NA-MIC Software Tools are available here: http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit
The Center has created and extended a number of software tools to handle some of the key problems in medical imaging analysis and to deliver these computational technologies via a suite of applications, toolkits, and infrastructure facilities. A summary description of these tools includes:
* Slicer3 (application) - a application platform for deploying imaging technologies, newly architected with an execution model facilitating integration, work flow, and large scale computing. While Slicer3 is the newest addition to the NAMIC Kit, it is built on pre-existing, mature toolkits so that the application is relatively mature, and is already in use. Because Slicer3 supports plug-in modules, active development is proceeding to create and package various modules for dissemination to the NAMIC community.
* ITK (toolkit) - a mature system for image analysis, registration and segmentation (initially created in 1999). ITK is in use worldwide for medical imaging research and development.
* VTK (toolkit) - a mature system for visualization, graphics, volume rendering, and interaction (initially created in 1993). VTK is used worldwide for research, teaching and commercial development.
* DART (computational infrastructure) - a key component of the NAMIC quality control process, DART is used to coordinate and report the software testing process. It was created in the first year of NAMIC and is in constant use, therefore a mature system.
* CMake/CTest/CPack (computational infrastructure) - CMake and CTest are relatively mature systems used to manage the building and testing of software across diverse computer platforms. CMake is used worldwide by some of the [http://lwn.net/Articles// world's largest open source systems such as KDE]. CPack, a recent addition to the NAMIC kit, is used to simplify the packaging and dissemination of software across platforms. Thus in NAMIC we can easily deploy our software across Windows, Linux, Unix, and Mac platforms.
* Other tools (computational infrastructure) - Many other software tools are used to support the development of advanced imaging applications, and to assist with large scale computing, including
** Teem - image processing tools (mature)
** KWWidgets - Open source, cross platform GUI toolkit (mature, but development continues to support workflow).
** BatchMake - Support large-scale computing, including grid computing, for performing large population studies and statistical analysis (under active development).
** GridWizard - an application scheduler aimed at allowing researchers to easily harness the power of large computational grids. It lets you run tens of thousands of commands simultaneously on multiple clusters of computers by typing a single command, without writing scripts. It can be used by itself, and is currently being integrated with Slicer3 and as part of a web-based portal environment. (http://www.na-mic.org/Wiki/index.php/Slicer3:Grid_Interface)

These tools address key problems in imaging including segmentation, registration, visualization, and shape analysis; or provide facilities supporting researchers and developers who wish to create advanced software applications. One of key characteristics of NAMIC is that we treat the development of advanced medical image analysis software holistically; that is, the complete cycle of algorithm design, efficient impmentations, quality control and dissemination are needed to effectively address challenges provided by the driving biological problems. Examples of how these tools are being used include the following:

(a) Segmentation: Here there are a variety of tools of varying degrees of maturity ranging from the EM Segmentor (a widely distributed, mature algorithm included in Slicer3 as an application plug-in) to more recent work on DTI segmentation based on directional flows which are Matlab and C++ based. Powerful mature tools such as Bayesian segmentation have been recently included in Slicer (and have been available in ITK for some time now) which can be combined with very recent work on the semi-automated segmentation of the DPFC done in collaboration with Core 3 researchers. Further, tools previously developed by NAMIC researchers which have had a wide distribution have been put into Slicer. For example, geometric based segmentation methods (some of which were included in packages marketed by GE) were tailored for cortical segmentation, and included in the Slicer, and in fact even improved with the inclusion of statistical based approaches.

(b) Registration: Similar remarks can be made for registration in which we have a spectrum ranging from very mature methods to very recent ones which are still being tested. In particular, mature widely distributed methodologies for rigid registration are now included in ITK, as well as spline-based registration methodologies. These are well-tested methods which have been made accessible to the general imaging community. Newer methodologies such as those based on optimal transport for elastic registration are being included in ITK. NAMIC has also pushed for fast implementations of its algorithms to be used on cheap widely available platforms. Taking a cue from the game industry, some algorithms have been ported to GPUs (graphics cards) which are being employed now as computing devices. This has led to a speed-up of almost two orders of magnitude on some of the registration algorithms being tested.

(c) Shape Analysis: Again a number of methodologies have been developed and implemented with varying levels of maturity. Shape methodologies based on spherical harmonics are quite mature, and are available in pipelines developed by NAMIC researchers and have been distributed to the general community. A newer spherical based wavelet shape analysis package has been put into ITK, which also drives a novel shape-based segmentation procedure. More globally based spherical harmonic ideas have been combined with the multi-resolution spherical wavelet approach as a statistical shape based package for schizophrenia. This general technique may be used for other purposes as well, and is presently being ported to some work being done on the prostate. Work has also been accomplished on particle based approaches to this important problem area with the code put into ITK. Many times we work with a Matlab/C++ initial version of our codes, then move to ITK, and finally to Slicer. However, even at the Matlab/C++ stage, algorithms have been distributed and used in a clinical setting (for example, rule-based brain segmentation approaches).

(d) Diffusion Weighted Image (DWI) Analysis: A number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical analysis of fiber bundles have been contributed to the NAMIC toolkit. Some of these tools have been already integrated into diffusion dedicated ITK package with GUI as part of the NAMIC toolkit (e.g., FiberViewer-UNC) and into the Slicer platform (BWH tractography, clustering). The impact of NAMIC DWI analysis activities is best characterized by most recent journal articles and journal articles in print. The application of the NAMIC FiberViewer tool (UNC) in large clinical studies at UNC and Duke are in print (Taylor et al, Gilmore et al., Cascio et al.). Clinical application of the Slicer DTI package by BWH/MIT is reported in O’Donnell et al. Kuroki N. et al. and Nakamura et al.. The research by MGH is found in two journal publications by Tuch et al.. The description of the methodologies also appeared or will appear in peer reviewed journals (Corouge et al., Fletcher et al., Corouge et al.). New methods in development (Finsler metric GT, volumetric PDE based path analysis Utah, stochastic tractography BWH), and path of interest MGH, are currently tested on Core-3 DBP data, with the goal to give recommendations on which type of solution is appropriate to solve specific clinical analysis questions.

(e) Visualization: Core 2 researchers involved with NAMIC now (e.g., the founders of Kitware) were at the forefront of developing VTK (and of course ITK). Thus here we are considering technologies which are at a commercial level of development, and used at thousands of sites. Algorithms developed at NAMIC have driven new directions for these packages. Newer visualization methods, for example, the conformal flattening procedure have been ported to an ITK filter and is in the NAMIC Sandbox. Quasi-isometric methods for brain flattening from the MGH Free Surfer have become part of the NAMIC enterprise as well. These flattening procedures are very easy to use, and may also be employed for registration. Code for the control of distortion of the area in flattening has been incorporated which give area-preservation with minimal distortion. The techniques may be also used for several other purposes included automatic fly-throughs in endoscopy (incorporated into Slicer), and for texture mappings for general visualization purposes.

==Logistics==
When completed, the information should be sent to:

Gwen Jacobs, PhD

Professor of Neuroscience

Asst. CIO and Director of Academic Computing

1 Lewis Hall

Montana State University

Bozeman, MT 59717

406-994-7334 - phone

406-994-7077 - FAX

gwen@cns.montana.edu <mailto:gwen@cns.montana.edu>

2007 Materials for NCBC Program Review

2007-06-08T11:41:05Z

Shenton: /* Q2.1 To what extent does the vision and direction of the NCBC initiative promote biomedical computing? */

==Materials requested for NCBC Program Review==

These are due to Gwen Jacobs by Friday, June 08, 2007.

=='''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''==

Summary: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#1._Introduction

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with respect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs include a focus on Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

Highlights: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#3._Highlights

The third year of the NA-MIC project saw continued development and dissemination of medical image analysis software. The current progress is clearly characterized by a significant increase in the application of Core-1 and Core-2 tools to image data provided by the Core-3 DBP groups. The main reasons for this progress is two-fold, first there are several new methods that are out of the prototype stage and ready to be applied to large sets of imaging datasets, and second there are new high-resolution imaging data from high-field scanners that are more appropriate for the new tools than historical data with often very coarse slice resolution.

With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NA-MIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NA-MIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

*Advanced Algorithms
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NA-MIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NA-MIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The NA-MIC toolkit includes a comprehensive set of modules for analysis of diffusion weighted images (DWI), including improved calculation of tensors, interpolation, nonlinear deformation and statistics on tensor fields, novel methods for tractography and for optimal path finding, and clustering of sets of streamlines.
* A quantitative tractography package for user-guided geometric parametrization and statistical analysis of fiber bundles (FiberViewer) has been contributed to the NAMIC toolbox. This tool used in several ongoing clinical DTI studies.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NA-MIC Sandbox in preparation for formal acceptance into the NA-MIC Kit.
*Technology Deployment Platform: Slicer3
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NA-MIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* Slicer3's execution model is also allowing researchers to access large computational resources. Two applications that support the use of such resources, BatchMake and GridWizard, have been recently integrated with Slicer3 for specific modules. The GUI for the modules that are to be run via these computational support applications allow for the creation of large population studies or parametric studies of an algorithm.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NA-MIC Kit, and are now available to all other modules built on the Slicer3 framework.

*Outreach and Technology Transfer
Cores 4-5-6 continue to support, train and disseminate to the NA-MIC community, and the broader biomedical computing community.
* NA-MIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NA-MIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair. The training and dissemination will continue with a DTI workshop at the forthcoming Human Brain Mapping meeting and activities at MICCAI 2007, among others.

==Q2==
'''A brief statement - (one page per question, max) addressing each of the questions listed below. These are the questions that we have been asked to address in our report. Our goal in asking for this information is to be able to produce a report that reviews the program as a whole. Your view, from the vantage point of the center you direct, is critical to our work. In addition, your answers will provide us with more information that we can use in our discussion with program staff on June 11th. We know that some of this information can be found on your websites, so in those cases a link to the information would be most helpful.'''

===Q2.1 To what extent does the vision and direction of the NCBC initiative promote biomedical computing?===

The RFA for the creation of the NCBC program laid out a very explicit vision.

The NCBCs are to be the core of the networked national effort to build the computational infrastructure for biomedical computing in the US. From the [http://www.bisti.nih.gov/ncbc/ NIH website] in 2004: Four new National Centers for Biomedical Computing (NCBC) will develop and implement the core of a universal computing infrastructure that is urgently needed to speed progress in biomedical research. The centers will create innovative software programs and other tools that enable the biomedical community to integrate, analyze, model, simulate, and share data on human health and disease. The original [http://grants1.nih.gov/grants/guide/rfa-files/RFA-RM-04-003.html RFA] stated: 1. The software should be freely available to biomedical researchers and educators in the non-profit sector and 2. The terms of software availability should permit the commercialization of enhanced or customized versions of the software.

As the amount of data produced by biomedical researchers is increasing at an ever accelerating pace, the relative importance of computing as an integral part of analysis is increasing as well. In addition both data and analyses are getting more and more complex. Increasingly, science is performed by interdisciplinary teams at multiple locations. Industrial strength research platforms are one of the emerging needs of biomedical research. This need was only been partially addressed before the creation of the NCBC program. The NCBC centers are in an outstanding position to develop stable, maintainable, and expandable software to address these needs.

In addition to building the infrastructure for biomedical computing, NCBCs play an important role in fostering a community of computational scientists dedicated to solving problems in the biomedical domain. In its short three-year life span, NA-MIC NCBC has contributed to this vision by holding numerous workshops and tutorials on open source software for biomedical image analysis, by supporting the Insight Journal, a peer-review venue for publications accompanied with open-source implementation, and by engaging a large number of graduate students in Computer Science programs in biomedical computing research. By creating high-profile bio-computation programs, the NCBC initiative brings the biomedical computing as a field of research to the attention of the computation community and actively promotes collaborations between computational and biological sciences.

The participants in our center signed on for this project because they believe in the vision of developing a universal computing infrastructure for medical image computing. NA-MIC is creating an open source platform that embodies this vision for the field of medical image computing. We have settled on a very liberal open source license for our software plattform. All the components of that plattform, called the NA-MIC kit, are distributed under a BSD style licence without restrictions on commercial use. Other centers have adopted different strategies to address the requirement for open source and to enable commercial use, as required in the original RFA; we believe the NA-MIC liberal license approach minimizes barriers to wide adoption and is will maximize return on the NIH investment. The availability of software platforms commoditizes infrastructure for research and allows individual researchers to spend more time on their core research. Over time, this will promote biomedical computing by lowering the hurdle for scientists to use the technology.

===Q2.2 In what ways has the NCBC initiative advanced biomedical computing?===
Ron:

It is too early to say how the NCBC initiative advanced biomedical computing. 4 of the NCBC's have been funded since October 2004 (less than three years). Funding for the other three began in late 2005. Center efforts of this size need significant time and effort to get organized and to synchornize the activities of the participants. This startup effort is where the primary focus of the centers has been until now. There are early signs that some of the centers are beginning to emerge from this phase of their evolution and turning toward activities aimed at the field at large. However, it will be several years before the full impact of this program will become visible.

Below is a more detailed discussion of the specifics of this evolution from the vantage point of NA-MIC

(Tina)

Answer:http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#4._Impact_and_Value_to_Biocomputing

In NA-MIC's third year, it is evident that NA-MIC is developing a culture, environment, and resources to foster and incite collaborative research in medical image analysis that draws together mathematicians, computer scientists, software engineers, and clinical researchers. These artefacts of NA-MIC impact how NA-MIC operates, make NA-MIC a fulcrum for NIH funded research, and draws new collaborators from across the country and around the world to NA-MIC.

*Impact within the Center

**Within the center, the NA-MIC organization, NA-MIC processes, and the NA-MIC calendar has permeated the research. The organization is nimble, forming ad hoc distributed teams within and between cores to address specific biocomputing tasks. Information is shared freely on the NA-MIC Wiki, on the weekly Engineering telephone conferences, and in the NA-MIC Subversion source code repository. The software engineering tools of CMake, Dart 2 and CTest, CPack, and KWWidgets facilitate a cross platform software environment for medical image analysis that be easily built, tested, and distributed to end-users. Core 2 has provided a platform, Slicer 3, that allows Core 1 to easily integrate new technology and deliver this technology in an end user application to Core 3. Core 1 has developed a host of techniques to apply to structural and diffusion analysis which are under evaluation by Core 3. Major NA-MIC events, such as the annual All Hands Meeting, the Summer Project Week, the Spring Algorithms meeting, and Engineering Teleconferences are avidly attended by NA-MIC researchers as opportunities to foster collaborations.

*Impact within NIH Funded Research

** Within NIH funded research, NA-MIC continues to forge relationships with other large NIH funded projects such as BIRN, caBIG, NAC, and IGT. Here, we are sharing the NA-MIC culture, engineering practices, and tools. The BIRN infrastructure, built on widely-accepted grid middleware, allows NA-MIC researchers to share data, access computational resources and provides a rich collaborative environment through a science portal. caBIG lists the 3D Slicer among the applications available on the National Cancer Imaging Archive. NAC and IGT use the NA-MIC infrastructure and are involved in the development of the 3D Slicer. BIRN recently held an event modeled after the NA-MIC Project Week. NA-MIC has become a resource on open source licensing to the medical image analysis community.

NA-MIC is also attracting NIH funded collaborations. Two grants have been funded under PAR-05-063 to collaborate with NA-MIC: Automated FE Mesh Development and Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI. Five additional applications to collaborate with NA-MIC via the NCBC collaborative grant mechanism are under consideration. Additional grant applications submitted under other calls are planning to use and extend the NA-MIC tools.

*National and International Impact

**NA-MIC events and tools garner national and international interest. There were nearly 100 participants at the NA-MIC All Hands Meeting in January 2007, with many of these participants from outside of NA-MIC. Several researchers from outside the NA-MIC community have attended the Summer Project Weeks and the Winter Project Half-Weeks to gain access to the NA-MIC tools and people. These external researchers are contributing ideas and technology back into NA-MIC.

**Components of the NA-MIC kit are used globally. The software engineering tools of CMake, Dart 2 and CTest are used by many open source projects and commercial applications. For example, the K Desktop Environment (KDE) for Linux and Unix workstations uses CMake and Dart. KDE is one of the largest open source projects in the world. Many open source projects and commercial products are benefiting from the NA-MIC related contributions to ITK and VTK. Finally, Slicer 3 is being used as an image analysis platform in several fields outside of medical image analysis, in particular, biological image analysis, astronomy, and industrial inspection.

**NA-MIC co-sponsored the Workshop on Open Science at the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2006 conference. The proceedings of the workshop are published on the electronic Insight Journal, another NIH-funded activity.

**Over 50 NA-MIC related publications have been produced since the inception of the center.

===Q2.3 Are the NCBCs interfacing appropriately?===

At this point in the project cycle, the NCBC's have focused on interactions with the DBPs, developing requirements, creating foundational technology, and integrating existing technologies within the Center. In many cases such as with NAMIC, the resulting technologies are actively being disseminated to the research community. It is only recently that outreach across NCBCs is an effective use of resources. There have been initial efforts towards collaboration, such as cross-center use of software tools (for example, SimBios/SimTk actively uses NAMICs VTK toolkit), and the promising Software and Data Integration Working Group (SDIWG). In addition, NA-MIC as has interactions with CCB and Simbios. NA-MIC also interfaces with a number of large-scale efforts which are not NCBC but are comparable in scale: BIRN, several national resource centers and to some extent, with CaBIG.

In that sense, the answer is yes, the NCBC's are interfacing appropriately at this time. Within the next few years there will be a need to increase the extent of interfacing significantly. That will require either reallocation of resources or the allocation of new additional resources. Due to the large variety of approaches adopted by the different centers it will be likely necessary to to allocate significant amounts of time by both senior leadership at the centers for identifying objectives of such interface efforts and by the engineering cores to actually execute the plans. A serious effort in this direction will probably require allocation of multiple FTE's.

===Q2.4. What new collaborations have been formed through the NCBC initiative?===

NA-MIC’s structure and organization has facilitated many new collaborations. NA-MIC is a distributed center, bringing together mathematicians, computer scientists, software engineers, and clinicians from multiple sites. This distributed structure provided two types of new collaborations within NA-MIC: new collaborations between cores and new collaborations within cores. For between core collaborations, many of the algorithm and engineering core researchers had not collaborated previously with the researchers in either the first or second round of Driving Biological Projects (DBPs). Thus, the NCBC provided a unique opportunity for the algorithm and engineering core researchers to gain clinical insight and to adapt and tune their algorithms and tools to new clinical contexts. Conversely, the DBPs gained access to algorithms and tools that they previously had not utilized. Similarly, many of the algorithm core researchers and engineering core researchers had not previously collaborated. Thus, the NCBC exposed the researchers in the algorithm core to the tools and engineering practices of the engineering core and exposed the researchers in the engineering core to the computational techniques and data structures utilized by the algorithms core. For within core collaborations, many of the researchers within the algorithm core had not previously collaborated. Through NA-MIC, these researchers have been able to cooperate and also amicably compete to address the issues brought forth by the DBPs.

Below is a list of new collaborations within NA-MIC. This list was compiled from the complete list of [[NA-MIC_Collaborations | NA-MIC Collaborations]] and project lists from the 5 NA-MIC [[Engineering:Programming_Events |Project week events]]. Best effort was made to filter these lists down to just the new collaborations (groups of researchers) formed under NA-MIC.

* Georgia Tech + UC Irvine – Rule based segmentation algorithm for DLPFC
* Georgia Tech + Kitware - Knowledge-based Bayesian classification and segmentation
* BWH + MIT + Kitware - Brain tissue classification and subparcellation of brain structures
* Georgia Tech + UNC + BWH - Multiscale shape segmentation techniques
* BWH + Dartmouth + UNC + Georgia Tech - Shape analysis of the caudate and corpus callosum
* Georgia Tech + UNC + BWH - Spherical wavelet based shape analysis for Caudate
* Georgia Tech + UNC + BWH - Multiscale shape analysis of the hippocampus
* Dartmouth + UNC + BWH - Shape analysis of tbe hippocampus
* Utah + UNC + BWH - Automated shape model construction
* Dartmouth + Isomics - Neural substrates of apathy in schizophrenia
* Georgia Tech + GE Research + Kitware - Spherical Wavelet Transforms
* Georgia Tech + UNC - Shape analysis with Spherical Wavelets
* Utah + UNC - Adaptive, particle-based sampling for shapes and complexes
* UNC + Utah + Harvard - Tensor estimation and Monte-Carlo simulation
* Harvard + MIT + UNC - Corpus Callosum Regional FA analysis in Schizophrenia
* Dartmouth + MGH + Isomics + BWH - Integrity of Fronto-Temporal Circuitry in Schizophrenia using Path of Interest Analysis
* MGH + Isomics - ITK implementation of POIStats, and Integration into Slicer3
* UC Irvine + MGH + UNC + MIT - DTI Validation
* Utah + UNC + GE Research - DTI Software and Algorithm Infrastructure
* Utah + BWH - Tensor based statistics
* Utah + BWH - Diffusion tensor image filtering
* MGH + Dartmouth + Kitware + GE Research - Non-rigid EPI registration
* Dartmouth + BWH - Neural Substrates of Working Memory in Schizophrenia: A Parametric 3-Back Study
* Dartmouth + BWH - Brain Activation during a Continuous Verbal Encoding and Recognition Task in Schizophrenia
* Dartmouth + BWH - Fronto-Temporal Connectivity in Schizophrenia during Semantic Memory
* UC Irvine + Toronto - Imaging Phenotypes in Schizophrenics and Controls
* MIT + Isomics + GE Research + Kitware - fMRI statistics software
* MIND + Isomics + MGH - Analysis of Brain Lesions in Neuropsychiatric Systemic Lupus Erythematosis
* JHU + Queen's + BWH + Georgia Tech - Segmentation and Registration Tools for Robotic Prostate Interventions
* UNC + GE Research - Longitudinal MRI study of early brain development
* BWH + Kitware + MIT - Velocardiofacial Syndrome (VCFS) as a genetic model for schizophrenia
* UNC + GE Research + BWH - DTI population analysis
* Georgia Tech + BWH - Geodesic tractography
* BWH + Queen's + GE Research - Display optimization
* UCSD + Isomics - Dendritic Spine Morphometrics
* <others to be filled in>

NA-MIC has also attracted researchers from the field who were not originally part of NA-MIC. Some of these new collaborations are formally organized using the NIH NCBC Collaborative R01 program. But other collaborations are being driven solely by the opportunity to share resources, techniques, capabilities, and ideas.

Below is a list of new collaborations between external researchers and NA-MIC. Again, best effort was made to only list the new collaborations with external parties.

* Mario Negri + GE Research – Integration of vmtk with Slicer 3
* Iowa + Isomics - Finger Bone Biomechanics
* CalTech + Kitware - Systems Biology and Genomic Science
* BWH + Wake Forest + Virginia Tech - Alcohol Stress in Primates
* BWH + MGH - Radiation Treatment Planning'
* Iowa + Kitware + BWH - Non Linear Registration Tools
* Northwestern + Isomics - Radiology Translation Station
* Harvard IIC + Isomics + GE Research - Astronomy Analysis and Visualization
* Virginia Tech + BWH - Applying EMSegmenter to nonhuman primate neuroimaging
* JHU + Queen's + BWH - Brachytherapy needle positioning robot integration
* Iowa + Kitware + BWH - Nonrigid registration
* Iowa + BWH - Developing electronic atlas
* Iowa + Kitware - GUI for nonridig image registration
* Canary Islands Technological Institute + Isomics + GE Research - DICOM Query/Retrieve
* Canary Islands Technological Institute + GE Research - Block matching registration
* UNC + Duke University Medical Center - DTI tractography analysis in depression study
* <others to be filled in>

The collaborative nature of NA-MIC is exemplified by the attendance at the NA-MIC All Hands Meeting and the NA-MIC Summer Project Week. Researchers from within and external to NA-MIC come together at these two events to forge collaborations. At the 2007 NA-MIC All Hands Meeting alone, there were 96 attendees: 56 NA-MIC researchers, 32 NA-MIC collaborators from 13 institutions, and 8 members of the External Advisory Board and NIH. At the Project Half-Week run in conjunction with the All Hands Meeting, there were 38 projects: 16 initiated from the algorithm core, 10 specific to the engineering core, and 11 from external collaborators.

More detailed information on collaborations as well as Project Week events can be found at:
* http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations
* http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events

===Q2.5. What new training opportunities have the centers provided?===

**Answer: http://www.na-mic.org/Wiki/index.php/Training:Main

* The requirement for all NCBC's to dedicate funds for training provides the opportunity to develop a targeted and deep portfolio of training resources. The unique perspective of providing these training opportunities and resources specifically targeted to a multi-disciplinary audience of basic and clinical biomedical scientists, computer scientists and medical imaging scientists is fostered by the NCBC Program. Traditional funding by NIH research grants results in allocation of all funds into the primary research; NIH training grants allocate funding for the support of trainees; the few education grants are restricted in budget and overhead thus severely limiting the quality and quantity of educational resources that can be offered. The NCBCs have created a new opportunity for our cadre of experienced clinician scientists, computer scientists and medical image analysis experts affiliated with our large centers to be supported to work on outreach activities.

* Within NA-MIC, this perspective has given rise to a thriving training program that supports the biomedical research community within NAMIC, across the NIH community and around the world. The strong demand for our training resources is evident from the large number of hits to our training web pages, from the rapid enrollment in all offered workshops, and the positive feedback from participants. We believe, and our belief is supported by the documented backgrounds of our workshop attendees, that a key aspect of our training materials that makes them useful to the community is that they are learner-centered, goal-oriented, and targeted to bridge the gaps in technical knowledge and language that exist between basic and clinical biomedical scientists, computer scientists and medical imaging scientists. For example, a tutorial that teaches how to use Slicer to register two images includes not only the necessary details of how to implement the algorithm, but also the conceptual framework for the registration approach, the mathematical underpinnings of the algorithm and a detailed anatomical approach for visually inspecting and refining the registration. This rich, but simple approach provides a consistently educational experience for every new user of the NAMIC toolkit.

* NAMIC supported new training opportunities are developed to maximize impact on the wider scientific community. The primary vehicle for this is "Slicer 101", our portfolio of Slicer training tutorials (http://www.na-mic.org/Wiki/index.php/Slicer:Workshops:User_Training_101). We have focused our efforts on making all our tutorial materials available via the NA-MIC Wiki as downloadable Powerpoint presentations and accompanying curated, anonymized datasets. The tutorials are all carefully tested on multiple computer platforms and by our team before being used in live Workshops (http://www.na-mic.org/Wiki/index.php/Training:Events_Timeline). Refinements are made based on the feedback of the audience and our experience during the teaching sessions. The final product of our work allows any new users, regardless of educational background, to not only use the NAMIC tools and algorithms, but to understand what they are doing and why. To date we have had over 7,880 hits to the Slicer 101 webpage.

* NAMIC supported Workshops are another unique venue for multi-disciplinary training. In addition to the all the points made regarding the content of the training materials, the 14 Workshops run by the NAMIC Training core over the past 3 years have each provided the opportunity for new connections to be made among basic and clinical biomedical scientists, computer scientists and medical imaging scientists. All our Workshops provide opportunities for formal and informal discussions among attendees of diverse backgrounds and strengths. These hands-on, interactive workshops allow participants to translate concepts of medical image processing into skills through instructor-led training. The simplicity of our approach, and the exceptional quality of the NAMIC toolkit, ensures a very high success rate for knowledge and skill acquisition. We estimate that 370 people from 52 different universities and companies attended our Workshops between 2005 and 2006.

* We are currently focusing our efforts on reaching a wider community by delivering a more didatic based Workshop in conjunction with the upcoming Organization for Human Brain Mapping meeting in Chicago next week. We held our enrollment to 50 so that we could offer the same hands-on interactive training experience to the attendees and our registration filled within a few weeks of the offering being posted. Tenatively, we anticipate that more than 12 countries and 14 states within the US will be represented at this upcoming Workshop.

* A final point is that this commitment and focus on training permeates all aspects of the NAMIC program. All large gatherings of NAMIC personnel including All Hands Meetings, Programming/Project Weeks, and Core meetings provide venues for our culture of training to be expressed. Each gathering creates an opportunity to build bridges between our participating disciplines and to improve the communication skills of each member. This culture includes implicit aspects such as a supportive and collegial environment that encourages questions and critical feedback, as well as explicit aspects such as encouraging junior level participants to make presentations and scheduling educational presentations from domain experts within and outside of the community. We believe that the positive attitude towards sharing knowledge and skills is fostered in all who are associated with our Project.

===Q2.6. What changes could make the program more effective in the future?===

* ''National Visibility''

The overall objective statement of the NCBC program calls for building computational infrastructure for the nation's biomedical research efforts -- this ambitious goal includes both computational and biomedical science, areas in which the existing centers have extensive experience, but also in nationwide "marketing" efforts where the scientific community is less adept. Several centers, including NA-MIC, have developed novel approaches to this problem through their training and dissemination cores, but as the output of the centers grows along with the number of potential users in the community and the potential impact, these critical resources will be increasingly strained. There are several approaches the overall program could take to address this issue including supplemental funding to host workshops or conferences, providing small travel grants for researchers or students to visit the centers, or actively encouraging a wider range of NIH funded researchers to adopt the tools generated by the NCBCs. In particular, collaboration PAR has been very effective as a mechanism for encouraging busy scientists to consider adopting the NCBC tools and is an excellent example of how the program can extend the impact of the basic investment in scientific infrastructure.

* ''Local Autonomy''

The program should avoid adding extra layers of uniformity to what are fundamentally unique centers. The NCBC program has successfully established a distributed network of centers drawing on the expertise of some of the nation's leading researchers drawn to the program for the opportunity to develop and apply their know-how to this ambitious effort. This rich environment, predictably, yields a diversity of approaches and organizational structures as each of the centers works to implement their particular vision of how to fulfill the overall mission of the NCBC program. Preserving the vitality of the effort depends on retaining this autonomy as each center strives to meet the individual objectives suited to their communities. The program needs well defined goals that each center must meet, but the overall program should facilitate the individual solutions of the center's leadership.

* ''Stability''

One critical mission of the NCBC program is the creation of infrastructure. These large-scale efforts require time-horizons that are incompatible with the current framework of many programs at NIH. It might take two or three iterations until a software package is sufficiently mature to be attractive to a larger community of scientists. Large software platforms require engineering staff of 10-20 in addition to the biomedical scientists developing functionality aimed at solving a particular problem. Many of the NCBCs are working on several packages. This results in underfunded projects which compromises the timeliness and performance of the resulting software.

It would be advisable to increase funding for the engineering and outreach activities of the centers and provide the funding in a reliable way. The continous stream of cuts from of the original budget has made this discrepancy even more pronounced. In 2007/2008 we will receive only 77.7% of the money that was budgeted in the application for that year. Following the RFA guidelines, the original budget did not contain adjustments for inflation. Furthermore, in deviation from the way that most NIH programs are funded, the budget was frozen in total dollars not direct dollars. Institutional overhead rates, and fringe and benefit rates have increased for several of the NA-MIC participants during the last three years and have resulted in further decreases in the amount of money available to actually do research.

===Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing?===

* ''Greater Start Up Time and Steeper Learning Curve Than Anticipated''

Bringing together computer scientists, engineers, and biomedical researchers, with diverse interests, training, and background, for the purpose of working on a set of biological problems, is no easy feat and, in retrospect required a steeper learning curve than was anticipated. This steeper learning curve is understandable, since the main focus initially was on developing alliances among the cores in order to increase awareness about the kinds of tools needed for the specific imaging problems posed by the biomedical researchers who were driving the biological problems. The first year of the grant, as noted in our annual report, thus reflected a "core" emphasis, as an interdisciplinary team was brought together, many members for the first time. It was not until the second year of the grant that the focus on a "core" emphasis shifted to a focus on "themes", which cut across "core" boundaries. While this shift was viewed as part of a natural evolution, now that we know this, we can help guide future NIH efforts by suggesting that specific projects/clinical applications should be highlighted in the first few months of the grant, based on meetings among core members, so as to facilitate a focus on clinical applications from the outset.

Such an early emphasis on clinical applications/problems would also facilitate an early focus on the development and application of computational tools, which could be more closely aligned with specific clinical problems and applications. This would breakdown artificial barriers that a "core" focus involves, which, while seemingly an inherent part of the initial stages, could be curtailed by highlighting early the need to focus on specific needed applications. In this way, the needed applications of the driving biological problems could form natural groupings that involve members from all cores, and work groups could be set up from the beginning that reflect a "theme"/"application" approach. This would also assist in more communication between core members, which would also likely facilitate ongoing communication among computer scientists, engineers, and biomedical researchers.

* ''Developing Robust Software for Advanced Applications is Difficult''

Creating industrial-strength software solutions to support scientific investigations is time consuming and requires skill not usually found in the academic environment. NA-MIC has been successful at bringing in commercial software development expertise to help accomplish the center's goals (GE, Kitware, Isomics) but these resources are routinely stretched well beyond the allocated budgets due to the many research directions of the center's scientists. When considering biomedical computing projects, the NIH must not attempt to short change the development process or the resulting systems run a greater risk of being difficult to maintain, difficult to scale up, and incompatible across systems.

An excellent example of the level of effort needed for a successful, extensible, cross-platform product is the National Library of Medicine's Insight Toolkit (ITK). The organizational meeting for ITK was held in October 1999. Between October 1999 and October 2002, fifty developers contributed code from six prime contractors (GE, Kitware, Insightful, UNC, Utah, and UPenn) and four sub-contractors (BWH, UPenn, Pitt, and Columbia) to produce ITK Version 1.0. To date, over $13.5 million has been awarded by the NIH for the development, use, and expansion of ITK. That total includes 20, one-year contracts that were given to early adopters of ITK. When assessing the level of effort expended on ITK, it is important to consider that ITK's funding did not cover algorithm, graphical user interface, or visualization developments. ITK costs only funded the integration of existing methods into a common library. Developing end applications, involving user interfaces and visualizations tailored for clinical users, requires significant additional effort.

* ''Algorithm Development Needs to be Interactive and Not Sequential''

In reviewing the last three years of the driving biological problem, schizophrenia, it is evident that tool development that involved multiple interactions among members of Core 1 (Computer Scientists), 2 (Engineers), and 3 (Driving Biological Problem), at all stages of development, led to the development of computational tools that were both more tailored to the specific applications needed by Core 3 members, as well as to the development of tools that were more optimized for general use. This interactive mode of tool development is in contrast to tool development that proceeded more sequentially, where one or several members of Core 1 and 3 met, and then Core 1 proceeded with what their understanding was of the problem and went off and developed a tool with very little further input from Core 3 until the tool was delivered. The latter approach often resulted in delays in receiving the tool, as there was less communication between Core 1 and Core 3 members in these instances, and often the tool did not really meet the specific needs of the application without further work. In the future, and based on this experience, NIH initiatives should emphasize the importance of encouraging a more "interactive" approach to tool development and to discouraging what is termed here as a more "sequential" approach to tool development. With a more interactive approach, progress can be more readily evaluated at each phase of tool development, and input and testing can be provided based on more communication among members of Core 1, 2, and 3. A more "interactive" model is also far more responsive to the needs of the driving biological problem, and also keeps the focus on the clinical application.

Focusing on interactions across core members will also likely facilitate breaking down the steep learning curve inherent in early interactions across cores members (see above).

* ''Funding''

As mentioned above, there is a serious mismatch between the objectives set by NIH and the resources available to address them. A further aggravation were the additional cuts imposed over the last three years, combined with a "requirements creep". The future increased interaction between the centers will require either additional resources or decommittment for existing activities. If the centers are as successful as envisioned, an increasing number of scientists will want to interact with the scientists at the centers which will also require additional resources.

NIH will need to allocate significant additional resources to the centers in order to fulfill the vision of a network of centers of biomedical computing at the center of an ever increasing network of biomedical scientific programs benefiting form their software and services.

=='''Q3:A list of publications and/or software tools produced by the Center.''' If this information is provided in your progress report or is available on your website, a link will be sufficient. We are especially interested in your assessment of the maturity of your software tools and the impact they are having on the scientific community.===

A3:
*NA-MIC Publications are available here: http://www.na-mic.org/Wiki/index.php/Publications.
*NA-MIC Software Tools are available here: http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit
The Center has created and extended a number of software tools to handle some of the key problems in medical imaging analysis and to deliver these computational technologies via a suite of applications, toolkits, and infrastructure facilities. A summary description of these tools includes:
* Slicer3 (application) - a application platform for deploying imaging technologies, newly architected with an execution model facilitating integration, work flow, and large scale computing. While Slicer3 is the newest addition to the NAMIC Kit, it is built on pre-existing, mature toolkits so that the application is relatively mature, and is already in use. Because Slicer3 supports plug-in modules, active development is proceeding to create and package various modules for dissemination to the NAMIC community.
* ITK (toolkit) - a mature system for image analysis, registration and segmentation (initially created in 1999). ITK is in use worldwide for medical imaging research and development.
* VTK (toolkit) - a mature system for visualization, graphics, volume rendering, and interaction (initially created in 1993). VTK is used worldwide for research, teaching and commercial development.
* DART (computational infrastructure) - a key component of the NAMIC quality control process, DART is used to coordinate and report the software testing process. It was created in the first year of NAMIC and is in constant use, therefore a mature system.
* CMake/CTest/CPack (computational infrastructure) - CMake and CTest are relatively mature systems used to manage the building and testing of software across diverse computer platforms. CMake is used worldwide by some of the [http://lwn.net/Articles// world's largest open source systems such as KDE]. CPack, a recent addition to the NAMIC kit, is used to simplify the packaging and dissemination of software across platforms. Thus in NAMIC we can easily deploy our software across Windows, Linux, Unix, and Mac platforms.
* Other tools (computational infrastructure) - Many other software tools are used to support the development of advanced imaging applications, and to assist with large scale computing, including
** Teem - image processing tools (mature)
** KWWidgets - Open source, cross platform GUI toolkit (mature, but development continues to support workflow).
** BatchMake - Support large-scale computing, including grid computing, for performing large population studies and statistical analysis (under active development).
** GridWizard - an application scheduler aimed at allowing researchers to easily harness the power of large computational grids. It lets you run tens of thousands of commands simultaneously on multiple clusters of computers by typing a single command, without writing scripts. It can be used by itself, and is currently being integrated with Slicer3 and as part of a web-based portal environment. (http://www.na-mic.org/Wiki/index.php/Slicer3:Grid_Interface)

These tools address key problems in imaging including segmentation, registration, visualization, and shape analysis; or provide facilities supporting researchers and developers who wish to create advanced software applications. One of key characteristics of NAMIC is that we treat the development of advanced medical image analysis software holistically; that is, the complete cycle of algorithm design, efficient impmentations, quality control and dissemination are needed to effectively address challenges provided by the driving biological problems. Examples of how these tools are being used include the following:

(a) Segmentation: Here there are a variety of tools of varying degrees of maturity ranging from the EM Segmentor (a widely distributed, mature algorithm included in Slicer3 as an application plug-in) to more recent work on DTI segmentation based on directional flows which are Matlab and C++ based. Powerful mature tools such as Bayesian segmentation have been recently included in Slicer (and have been available in ITK for some time now) which can be combined with very recent work on the semi-automated segmentation of the DPFC done in collaboration with Core 3 researchers. Further, tools previously developed by NAMIC researchers which have had a wide distribution have been put into Slicer. For example, geometric based segmentation methods (some of which were included in packages marketed by GE) were tailored for cortical segmentation, and included in the Slicer, and in fact even improved with the inclusion of statistical based approaches.

(b) Registration: Similar remarks can be made for registration in which we have a spectrum ranging from very mature methods to very recent ones which are still being tested. In particular, mature widely distributed methodologies for rigid registration are now included in ITK, as well as spline-based registration methodologies. These are well-tested methods which have been made accessible to the general imaging community. Newer methodologies such as those based on optimal transport for elastic registration are being included in ITK. NAMIC has also pushed for fast implementations of its algorithms to be used on cheap widely available platforms. Taking a cue from the game industry, some algorithms have been ported to GPUs (graphics cards) which are being employed now as computing devices. This has led to a speed-up of almost two orders of magnitude on some of the registration algorithms being tested.

(c) Shape Analysis: Again a number of methodologies have been developed and implemented with varying levels of maturity. Shape methodologies based on spherical harmonics are quite mature, and are available in pipelines developed by NAMIC researchers and have been distributed to the general community. A newer spherical based wavelet shape analysis package has been put into ITK, which also drives a novel shape-based segmentation procedure. More globally based spherical harmonic ideas have been combined with the multi-resolution spherical wavelet approach as a statistical shape based package for schizophrenia. This general technique may be used for other purposes as well, and is presently being ported to some work being done on the prostate. Work has also been accomplished on particle based approaches to this important problem area with the code put into ITK. Many times we work with a Matlab/C++ initial version of our codes, then move to ITK, and finally to Slicer. However, even at the Matlab/C++ stage, algorithms have been distributed and used in a clinical setting (for example, rule-based brain segmentation approaches).

(d) Diffusion Weighted Image (DWI) Analysis: A number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical analysis of fiber bundles have been contributed to the NAMIC toolkit. Some of these tools have been already integrated into diffusion dedicated ITK package with GUI as part of the NAMIC toolkit (e.g., FiberViewer-UNC) and into the Slicer platform (BWH tractography, clustering). The impact of NAMIC DWI analysis activities is best characterized by most recent journal articles and journal articles in print. The application of the NAMIC FiberViewer tool (UNC) in large clinical studies at UNC and Duke are in print (Taylor et al, Gilmore et al., Cascio et al.). Clinical application of the Slicer DTI package by BWH/MIT is reported in O’Donnell et al. Kuroki N. et al. and Nakamura et al.. The research by MGH is found in two journal publications by Tuch et al.. The description of the methodologies also appeared or will appear in peer reviewed journals (Corouge et al., Fletcher et al., Corouge et al.). New methods in development (Finsler metric GT, volumetric PDE based path analysis Utah, stochastic tractography BWH), and path of interest MGH, are currently tested on Core-3 DBP data, with the goal to give recommendations on which type of solution is appropriate to solve specific clinical analysis questions.

(e) Visualization: Core 2 researchers involved with NAMIC now (e.g., the founders of Kitware) were at the forefront of developing VTK (and of course ITK). Thus here we are considering technologies which are at a commercial level of development, and used at thousands of sites. Algorithms developed at NAMIC have driven new directions for these packages. Newer visualization methods, for example, the conformal flattening procedure have been ported to an ITK filter and is in the NAMIC Sandbox. Quasi-isometric methods for brain flattening from the MGH Free Surfer have become part of the NAMIC enterprise as well. These flattening procedures are very easy to use, and may also be employed for registration. Code for the control of distortion of the area in flattening has been incorporated which give area-preservation with minimal distortion. The techniques may be also used for several other purposes included automatic fly-throughs in endoscopy (incorporated into Slicer), and for texture mappings for general visualization purposes.

==Logistics==
When completed, the information should be sent to:

Gwen Jacobs, PhD

Professor of Neuroscience

Asst. CIO and Director of Academic Computing

1 Lewis Hall

Montana State University

Bozeman, MT 59717

406-994-7334 - phone

406-994-7077 - FAX

gwen@cns.montana.edu <mailto:gwen@cns.montana.edu>

2007 Materials for NCBC Program Review

2007-06-06T20:26:38Z

Shenton: /* Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing? */

==Materials requested for NCBC Program Review==

These are due to Gwen Jacobs by Friday, June 08, 2007.

=='''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''===
(Tina/some edits from Marty --Tina--you had schizophrenia listed as a NEW DBP, I put in VCFS--if this was in the annual report, it needs to be fixed. Also, under highlights--- I see what Core 1 was able to do because of data provided by core 3, but there are no highlights of what core 3 did with what Core 1 gave them --- don't you think this should be fixed? That is, there are NO HIGHLIGHTS listed for CORE 3 --)

Summary: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#1._Introduction

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with respect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs include a focus on Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

Highlights: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#3._Highlights

The third year of the NA-MIC project saw continued development and dissemination of medical image analysis software. The current progress is clearly characterized by a significant increase in the application of Core-1 and Core-2 tools to image data provided by the Core-3 DBP groups. The main reasons for this progress is two-fold, first there are several new methods that are out of the prototype stage and ready to be applied to large sets of imaging datasets, and second there are new high-resolution imaging data from high-field scanners that are more appropriate for the new tools than historical data with often very coarse slice resolution.

With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NA-MIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NA-MIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

*Advanced Algorithms
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NA-MIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NA-MIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The NA-MIC toolkit includes a comprehensive set of modules for analysis of diffusion weighted images (DWI), including improved calculation of tensors, interpolation, nonlinear deformation and statistics on tensor fields, novel methods for tractography and for optimal path finding, and clustering of sets of streamlines.
* A quantitative tractography package for user-guided geometric parametrization and statistical analysis of fiber bundles (FiberViewer) has been contributed to the NAMIC toolbox. This tool used in several ongoing clinical DTI studies.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NA-MIC Sandbox in preparation for formal acceptance into the NA-MIC Kit.
*Technology Deployment Platform: Slicer3
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NA-MIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NA-MIC Kit, and are now available to all other modules built on the Slicer3 framework.

*Outreach and Technology Transfer
Cores 4-5-6 continue to support, train and disseminate to the NA-MIC community, and the broader biomedical computing community.
* NA-MIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NA-MIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair. The training and dissemination will continue with a DTI workshop at the forthcoming Human Brain Mapping meeting and activities at MICCAI 2007, among others.

==Q2==
'''A brief statement - (one page per question, max) addressing each of the questions listed below. These are the questions that we have been asked to address in our report. Our goal in asking for this information is to be able to produce a report that reviews the program as a whole. Your view, from the vantage point of the center you direct, is critical to our work. In addition, your answers will provide us with more information that we can use in our discussion with program staff on June 11th. We know that some of this information can be found on your websites, so in those cases a link to the information would be most helpful.'''

===Q2.1 To what extent does the vision and direction of the NCBC initiative promote biomedical computing?===
(Eric/Polina)

(From Ross/edits Marty--THIS SECTION I THINK NEEDS MORE WORK AS I DON"T UNDERSTAND HOW IT ANSWERS THE QUESTION BEING ASKED) The fields of biological and medical imaging are exploding. Moreover, the combination of new acquisition and reconstruction techniques, new computing resources, and diverse biological and clinical applications have resulted in a massive proliferation of imaging data. This imaging data has the potential for having an important impact on basic biological science, in the development of new drugs and medical technologies, and in direct clinical practice - in both diagnosis and treatment. Thus, biomedical imaging analysis is one of the most important applications of biomedical computing.

To realize this potential will require new computational tools for image analysis. These tools will rely on a diverse set of technical knowledge including physics, systems, computer science, mathmatics, and statistics. The development of these tools will also incorporate in-depth knowledge of clinical and biological applications in diverse areas such as neuroscience, psychiatry, encology, cardiology, and biochemistry.

These computational tools must be scalable in several ways. First, they must be computationally scalable. Tools for image analysis must be suitable for quantative analyses on large sets of 3D data, and many current software packages for image processing are not appropriate for this. In order to serve computational needs across NIH, image analysis tools must also scale across application domains and address a variety of clinical and biomedical problems. In the spirit of the National Center, these tools must also scale across institutions and research groups; that is, they must be usable and sustainable outside the context of the Center itself.

I DONT REALLY UNDERSTAND WHAT POINT IS BEING MADE IN THE PARAGRAPH BELOW??? (MARTY)
The NAMIC NCBC addresses these issues through a variety of mechanisms. First it is truly an national center, with researchers from seven (?) institutions across the US that represent expertise in the wide range of disciplines described above. The distributed nature of the project provides technical ald clinical expertise, but it also enforces openness of the resource - the infrastructure must accessible in order for the center to operate effectively. NAMIC also includes a set of industrial partners in the engineering core (Core 2), who provide a set of scalable tools for developing, maintaining, and distributing software. The software is universally managable and maintainable, and it does not belong to any one research group --- it belongs to the community. The tools area also scalable across appliation domains. HUH??? While the initial DBPs focused on neuroscience, the new DBPs include oncology and associated R01s include biomechanics/orthopedics. Users of the software are even more diverse, and even include fields outside of medicine and biology. The strategy of NAMIC is to make sure that the effort scales over time as well. The liscensing agreement which is enforced on all of our development activities does not restrict use, and thus we anticipate (and promote) commerical use of the NAMIC software so that the impact of the project could be realized beyond the lifetime of the center.

===Q2.2 In what ways has the NCBC initiative advanced biomedical computing?===
(Tina)

Answer:http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#4._Impact_and_Value_to_Biocomputing

===Q2.3 Are the NCBCs interfacing appropriately? (recommended by RICC)===
(Will Schroeder)

===Q2.4. What new collaborations have been formed through the NCBC initiative?===
(Jim Miller)
**Answer: http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations
**http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events

*discuss what is new: interactions inside cores and between cores.

===Q2.5. What new training opportunities have the centers provided?===
(Randy Gollub)

**Answer: http://www.na-mic.org/Wiki/index.php/Training:Main

** The entire Slicer tutorial portfolio would not exist without NA-MIC. explain what this portfolio consists of.

===Q2.6. What changes could make the program more effective in the future?===
(Steve Pieper)

* ''National Visibility''

The overall objectives of the NCBC program calls for building computational infrastructure for the nation's biomedical research efforts -- this ambitious goal includes both computational and biomedical science, areas in which the existing centers have extensive experience, but also in nationwide "marketing" efforts where the scientific community is less adept. Several centers, including NA-MIC, have developed novel approaches to this problem through their training and dissemination cores, but as the output of the centers grows along with the number of potential users in the community and the potential impact, these critical resources will be increasingly strained. There are several approaches the overall program could take to address this issue including supplemental funding to host workshops or conferences, providing small travel grants for researchers or students to visit the centers, or actively encouraging a wider range of NIH funded researchers to adopt the tools generated by the NCBCs. In particular, collaboration PAR has been very effective as a mechanism for encouraging busy scientists to consider adopting the NCBC tools and is an excellent example of how the program can extend the impact of the basic investment in scientific infrastructure.

* ''Local Autonomy''

The program should avoid adding extra layers of uniformity to what are fundamentally unique centers. The NCBC program has successfully established a distributed network of centers drawing on the expertise of some of the nation's leading researchers drawn to the program for the opportunity to develop and apply their know-how to this ambitious effort. This rich environment, predictably, yields a diversity of approaches and organizational structures as each of the centers works to implement their particular vision of how to fulfill the overall mission of the NCBC program. Preserving the vitality of the effort depends on retaining this autonomy as each center strives to meet the individual objectives suited to their communities. The program needs well defined goals that each center must meet, but the overall program should facilitate the individual solutions of the center's leadership.

===Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing?===
(Martha Shenton)

* ''Greater Start Up Time and Steeper Learning Curve Than Anticipated''

Bringing together computer scientists, engineers, and biomedical researchers, with diverse interests, training, and background, for the purpose of working on a set of biological problems, is no easy feat and, in retrospect required a steeper learning curve than was anticipated. This steeper learning curve is understandable, since the main focus initially was on developing alliances among the cores in order to increase awareness about the kinds of tools needed for the specific imaging problems posed by the biomedical researchers who were driving the biological problems. The first year of the grant, as noted in our annual report, thus reflected a "core" emphasis, as an interdisciplinary team was brought together, many members for the first time. It was not until the second year of the grant that the focus on a "core" emphasis shifted to a focus on "themes", which cut across "core" boundaries. While this shift was viewed as part of a natural evolution, now that we know this, we can help guide future NIH efforts by suggesting that specific projects/clinical applications should be highlighted in the first few months of the grant, based on meetings among core members, so as to facilitate a focus on clinical applications from the outset.

Such an early emphasis on clinical applications/problems would also facilitate an early focus on the development and application of computational tools, which could be more closely aligned with specific clinical problems and applications. This would breakdown artifical barriers that a "core" focus involves, which, while seemingly an inherent part of the initial stages, could be curtailed by highlighting early the need to focus on specific needed applications. In this way, the needed applications of the driving biological problems could form natural groupings that involve members from all cores, and work groups could be set up from the beginning that reflect a "theme"/"application" approach. This would also assist in more communication between core members, which would also likely facilitate ongoing communication among computer scientists, engineers, and biomedical researchers.

* ''Algorithm Development Needs to be Interactive and Not Sequential''

In reviewing the last three years of the driving biological problem, schizophrenia, it is evident that tool development that involved multiple interactions among members of Core 1 (Computer Scientists), 2 (Engineers), and 3 (Driving Biological Problem), at all stages of development, led to the development of computational tools that were both more tailored to the specific applications needed by Core 3 members, as well as to the development of tools that were more optimized for general use. This interactive mode of tool development is in contrast to tool development that proceeded more sequentially, where one or several members of Core 1 and 3 met, and then Core 1 proceeded with what their understanding was of the problem and went off and developed a tool with very little further input from Core 3 until the tool was delivered. The latter approach often resulted in delays in receiving the tool, as there was less communication between Core 1 and Core 3 members in these instances, and often the tool did not really meet the specific needs of the application without further work. In the future, and based on this experience, NIH initiatives should emphasize the importance of encouraging a more "interactive" approach to tool development and to discouraging what is termed here as a more "sequential" approach to tool development. With a more interactive approach, progress can be more readily evaluated at each phase of tool development, and input and testing can be provided based on more communication among members of Core 1, 2, and 3. A more "interactive" model is also far more responsive to the needs of the driving biological problem, and also keeps the focus on the clinical application.

Focusing on interactions across core members will also likely facilitate breaking down the steep learning curve inherent in early interactions across cores members (see above).

* ''Do Not Limit Driving Biological Problems to 3 Years''

Given the problems of: (1) bringing new investigators together from diverse backgrounds, and (2)a "core" focus that detracts from focusing on the clinical problems, another problem is (3)limiting the time of the driving biological problem to three years. Even if the time table could be improved for getting investigators working together more quickly, and even if researchers across cores focused on specific clinical problems right from the start, limiting the driving biological problem to 3 years is not realistic. This is particularly the case given that it is only in the third year the application of tools to clinical problems really begins to take shape. This is also a time period when the driving biological problems are ready to reap not only the benefits of the new tools, but also a time when members representing the driving biological problem are ready to provide further feedback to computer scientists and engineers with respect to refining the tools so as to make them more suited to the task at hand, as well as making the new tools more user friendly for wider use. To end the driving biological problems at a time when the fruits of labor are just being reaped severely curtails the completion of the application of new tools to clinical problems. There is also less time to confirm and validate findings, so as to determine that the findings are not a reflection of a methodological confound introduced by the new tool.

SYLVAIN OTHER??

=='''Q3:A list of publications and/or software tools produced by the Center.''' If this information is provided in your progress report or is available on your website, a link will be sufficient. We are especially interested in your assessment of the maturity of your software tools and the impact they are having on the scientific community.===

(Will Schroeder, Allen Tannenbaum--THE PUBLICATION LIST NEEDS UPDATING. O'Donnell paper, for example was out there a while ago in AJNR--things missing from our group also)

A3:
*NA-MIC Publications are available here: http://www.na-mic.org/Wiki/index.php/Publications.
*NA-MIC Software Tools are available here: http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit
The Center has created and extended a number of software tools to handle some of the key problems in medical imaging analysis and to deliver these computational technologies via a suite of applications, toolkits, and infrastructure facilities. A summary description of these tools includes:
* Slicer3 (application) - a application platform for deploying imaging technologies, newly architected with an execution model facilitating integration, work flow, and large scale computing. While Slicer3 is the newest addition to the NAMIC Kit, it is built on pre-existing, mature toolkits so that the application is relatively mature, and is already in use. Because Slicer3 supports plug-in modules, active development is proceeding to create and package various modules for dissemination to the NAMIC community.
* ITK (toolkit) - a mature system for image analysis, registration and segmentation (initially created in 1999). ITK is in use worldwide for medical imaging research and development.
* VTK (toolkit) - a mature system for visualization, graphics, volume rendering, and interaction (initially created in 1993). VTK is used worldwide for research, teaching and commercial development.
* DART (computational infrastructure) - a key component of the NAMIC quality control process, DART is used to coordinate and report the software testing process. It was created in the first year of NAMIC and is in constant use, therefore a mature system.
* CMake/CTest/CPack (computational infrastructure) - CMake and CTest are relatively mature systems used to manage the building and testing of software across diverse computer platforms. CMake is used worldwide by some of the [http://lwn.net/Articles// world's largest open source systems such as KDE]. CPack, a recent addition to the NAMIC kit, is used to simplify the packaging and dissemination of software across platforms. Thus in NAMIC we can easily deploy our software across Windows, Linux, Unix, and Mac platforms.
* Other tools (computational infrastructure) - Many other software tools are used to support the development of advanced imaging applications, and to assist with large scale computing, including
** Teem - image processing tools (mature)
** KWWidgets - Open source, cross platform GUI toolkit (mature, but development continues to support workflow).
** BatchMake - Support large-scale computing, including grid computing, for performing large population studies and statistical analysis (under active development).

These tools address key problems in imaging including segmentation, registration, visualization, and shape analysis; or provide facilities supporting researchers and developers who wish to create advanced software applications. One of key characteristics of NAMIC is that we treat the development of advanced medical image analysis software holistically; that is, the complete cycle of algorithm design, efficient impmentations, quality control and dissemination are needed to effectively address challenges provided by the driving biological problems. Examples of how these tools are being used include the following:

(a) Segmentation: Here there are a variety of tools of varying degrees of maturity ranging from the EM Segmentor (a widely distributed, mature algorithm included in Slicer3 as an application plug-in) to more recent work on DTI segmentation based on directional flows which are Matlab and C++ based. Powerful mature tools such as Bayesian segmentation have been recently included in Slicer (and have been available in ITK for some time now) which can be combined with very recent work on the semi-automated segmentation of the DPFC done in collaboration with Core 3 researchers. Further, tools previously developed by NAMIC researchers which have had a wide distribution have been put into Slicer. For example, geometric based segmentation methods (some of which were included in packages marketed by GE) were tailored for cortical segmentation, and included in the Slicer, and in fact even improved with the inclusion of statistical based approaches.

(b) Registration: Similar remarks can be made for registration in which we have a spectrum ranging from very mature methods to very recent ones which are still being tested. In particular, mature widely distributed methodologies for rigid registration are now included in ITK, as well as spline-based registration methodologies. These are well-tested methods which have been made accessible to the general imaging community. Newer methodologies such as those based on optimal transport for elastic registration are being included in ITK. NAMIC has also pushed for fast implementations of its algorithms to be used on cheap widely available platforms. Taking a cue from the game industry, some algorithms have been ported to GPUs (graphics cards) which are being employed now as computing devices. This has led to a speed-up of almost two orders of magnitude on some of the registration algorithms being tested.

(c) Shape Analysis: Again a number of methodologies have been developed and implemented with varying levels of maturity. Shape methodologies based on spherical harmonics are quite mature, and are available in pipelines developed by NAMIC researchers and have been distributed to the general community. A newer spherical based wavelet shape analysis package has been put into ITK, which also drives a novel shape-based segmentation procedure. More globally based spherical harmonic ideas have been combined with the multi-resolution spherical wavelet approach as a statistical shape based package for schizophrenia. This general technique may be used for other purposes as well, and is presently being ported to some work being done on the prostate. Work has also been accomplished on particle based approaches to this important problem area with the code put into ITK. Many times we work with a Matlab/C++ initial version of our codes, then move to ITK, and finally to Slicer. However, even at the Matlab/C++ stage, algorithms have been distributed and used in a clinical setting (for example, rule-based brain segmentation approaches).

(d) Diffusion Weighted Image (DWI) Analysis: A number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical analysis of fiber bundles have been contributed to the NAMIC toolkit. Some of these tools have been already integrated into diffusion dedicated ITK package with GUI as part of the NAMIC toolkit (e.g., FiberViewer-UNC) and into the Slicer platform (BWH tractography, clustering). The impact of NAMIC DWI analysis activities is best characterized by most recent journal articles and journal articles in print. The application of the NAMIC FiberViewer tool (UNC) in large clinical studies at UNC and Duke are in print (Taylor et al, Gilmore et al., Cascio et al.). Clinical application of the Slicer DTI package by BWH/MIT is reported in O’Donnell et al. Kuroki N. et al. and Nakamura et al.. The research by MGH is found in two journal publications by Tuch et al.. The description of the methodologies also appeared or will appear in peer reviewed journals (Corouge et al., Fletcher et al., Corouge et al.). New methods in development (Finsler metric GT, volumetric PDE based path analysis Utah, stochastic tractography BWH), and path of interest MGH, are currently tested on Core-3 DBP data, with the goal to give recommendations on which type of solution is appropriate to solve specific clinical analysis questions.

(e) Visualization: Core 2 researchers involved with NAMIC now (e.g., the founders of Kitware) were at the forefront of developing VTK (and of course ITK). Thus here we are considering technologies which are at a commercial level of development, and used at thousands of sites. Algorithms developed at NAMIC have driven new directions for these packages. Newer visualization methods, for example, the conformal flattening procedure have been ported to an ITK filter and is in the NAMIC Sandbox. Quasi-isometric methods for brain flattening from the MGH Free Surfer have become part of the NAMIC enterprise as well. These flattening procedures are very easy to use, and may also be employed for registration. Code for the control of distortion of the area in flattening has been incorporated which give area-preservation with minimal distortion. The techniques may be also used for several other purposes included automatic fly-throughs in endoscopy (incorporated into Slicer), and for texture mappings for general visualization purposes.

==Logistics==
When completed, the information should be sent to:

Gwen Jacobs, PhD

Professor of Neuroscience

Asst. CIO and Director of Academic Computing

1 Lewis Hall

Montana State University

Bozeman, MT 59717

406-994-7334 - phone

406-994-7077 - FAX

gwen@cns.montana.edu <mailto:gwen@cns.montana.edu>

2007 Materials for NCBC Program Review

2007-06-06T20:11:44Z

Shenton: /* Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing? */

==Materials requested for NCBC Program Review==

These are due to Gwen Jacobs by Friday, June 08, 2007.

=='''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''===
(Tina/some edits from Marty --Tina--you had schizophrenia listed as a NEW DBP, I put in VCFS--if this was in the annual report, it needs to be fixed. Also, under highlights--- I see what Core 1 was able to do because of data provided by core 3, but there are no highlights of what core 3 did with what Core 1 gave them --- don't you think this should be fixed? That is, there are NO HIGHLIGHTS listed for CORE 3 --)

Summary: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#1._Introduction

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with respect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs include a focus on Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

Highlights: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#3._Highlights

The third year of the NA-MIC project saw continued development and dissemination of medical image analysis software. The current progress is clearly characterized by a significant increase in the application of Core-1 and Core-2 tools to image data provided by the Core-3 DBP groups. The main reasons for this progress is two-fold, first there are several new methods that are out of the prototype stage and ready to be applied to large sets of imaging datasets, and second there are new high-resolution imaging data from high-field scanners that are more appropriate for the new tools than historical data with often very coarse slice resolution.

With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NA-MIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NA-MIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

*Advanced Algorithms
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NA-MIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NA-MIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The NA-MIC toolkit includes a comprehensive set of modules for analysis of diffusion weighted images (DWI), including improved calculation of tensors, interpolation, nonlinear deformation and statistics on tensor fields, novel methods for tractography and for optimal path finding, and clustering of sets of streamlines.
* A quantitative tractography package for user-guided geometric parametrization and statistical analysis of fiber bundles (FiberViewer) has been contributed to the NAMIC toolbox. This tool used in several ongoing clinical DTI studies.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NA-MIC Sandbox in preparation for formal acceptance into the NA-MIC Kit.
*Technology Deployment Platform: Slicer3
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NA-MIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NA-MIC Kit, and are now available to all other modules built on the Slicer3 framework.

*Outreach and Technology Transfer
Cores 4-5-6 continue to support, train and disseminate to the NA-MIC community, and the broader biomedical computing community.
* NA-MIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NA-MIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair. The training and dissemination will continue with a DTI workshop at the forthcoming Human Brain Mapping meeting and activities at MICCAI 2007, among others.

==Q2==
'''A brief statement - (one page per question, max) addressing each of the questions listed below. These are the questions that we have been asked to address in our report. Our goal in asking for this information is to be able to produce a report that reviews the program as a whole. Your view, from the vantage point of the center you direct, is critical to our work. In addition, your answers will provide us with more information that we can use in our discussion with program staff on June 11th. We know that some of this information can be found on your websites, so in those cases a link to the information would be most helpful.'''

===Q2.1 To what extent does the vision and direction of the NCBC initiative promote biomedical computing?===
(Eric/Polina)

(From Ross/edits Marty--THIS SECTION I THINK NEEDS MORE WORK AS I DON"T UNDERSTAND HOW IT ANSWERS THE QUESTION BEING ASKED) The fields of biological and medical imaging are exploding. Moreover, the combination of new acquisition and reconstruction techniques, new computing resources, and diverse biological and clinical applications have resulted in a massive proliferation of imaging data. This imaging data has the potential for having an important impact on basic biological science, in the development of new drugs and medical technologies, and in direct clinical practice - in both diagnosis and treatment. Thus, biomedical imaging analysis is one of the most important applications of biomedical computing.

To realize this potential will require new computational tools for image analysis. These tools will rely on a diverse set of technical knowledge including physics, systems, computer science, mathmatics, and statistics. The development of these tools will also incorporate in-depth knowledge of clinical and biological applications in diverse areas such as neuroscience, psychiatry, encology, cardiology, and biochemistry.

These computational tools must be scalable in several ways. First, they must be computationally scalable. Tools for image analysis must be suitable for quantative analyses on large sets of 3D data, and many current software packages for image processing are not appropriate for this. In order to serve computational needs across NIH, image analysis tools must also scale across application domains and address a variety of clinical and biomedical problems. In the spirit of the National Center, these tools must also scale across institutions and research groups; that is, they must be usable and sustainable outside the context of the Center itself.

I DONT REALLY UNDERSTAND WHAT POINT IS BEING MADE IN THE PARAGRAPH BELOW??? (MARTY)
The NAMIC NCBC addresses these issues through a variety of mechanisms. First it is truly an national center, with researchers from seven (?) institutions across the US that represent expertise in the wide range of disciplines described above. The distributed nature of the project provides technical ald clinical expertise, but it also enforces openness of the resource - the infrastructure must accessible in order for the center to operate effectively. NAMIC also includes a set of industrial partners in the engineering core (Core 2), who provide a set of scalable tools for developing, maintaining, and distributing software. The software is universally managable and maintainable, and it does not belong to any one research group --- it belongs to the community. The tools area also scalable across appliation domains. HUH??? While the initial DBPs focused on neuroscience, the new DBPs include oncology and associated R01s include biomechanics/orthopedics. Users of the software are even more diverse, and even include fields outside of medicine and biology. The strategy of NAMIC is to make sure that the effort scales over time as well. The liscensing agreement which is enforced on all of our development activities does not restrict use, and thus we anticipate (and promote) commerical use of the NAMIC software so that the impact of the project could be realized beyond the lifetime of the center.

===Q2.2 In what ways has the NCBC initiative advanced biomedical computing?===
(Tina)

Answer:http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#4._Impact_and_Value_to_Biocomputing

===Q2.3 Are the NCBCs interfacing appropriately? (recommended by RICC)===
(Will Schroeder)

===Q2.4. What new collaborations have been formed through the NCBC initiative?===
(Jim Miller)
**Answer: http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations
**http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events

*discuss what is new: interactions inside cores and between cores.

===Q2.5. What new training opportunities have the centers provided?===
(Randy Gollub)

**Answer: http://www.na-mic.org/Wiki/index.php/Training:Main

** The entire Slicer tutorial portfolio would not exist without NA-MIC. explain what this portfolio consists of.

===Q2.6. What changes could make the program more effective in the future?===
(Steve Pieper)

* ''National Visibility''

The overall objectives of the NCBC program calls for building computational infrastructure for the nation's biomedical research efforts -- this ambitious goal includes both computational and biomedical science, areas in which the existing centers have extensive experience, but also in nationwide "marketing" efforts where the scientific community is less adept. Several centers, including NA-MIC, have developed novel approaches to this problem through their training and dissemination cores, but as the output of the centers grows along with the number of potential users in the community and the potential impact, these critical resources will be increasingly strained. There are several approaches the overall program could take to address this issue including supplemental funding to host workshops or conferences, providing small travel grants for researchers or students to visit the centers, or actively encouraging a wider range of NIH funded researchers to adopt the tools generated by the NCBCs. In particular, collaboration PAR has been very effective as a mechanism for encouraging busy scientists to consider adopting the NCBC tools and is an excellent example of how the program can extend the impact of the basic investment in scientific infrastructure.

* ''Local Autonomy''

The program should avoid adding extra layers of uniformity to what are fundamentally unique centers. The NCBC program has successfully established a distributed network of centers drawing on the expertise of some of the nation's leading researchers drawn to the program for the opportunity to develop and apply their know-how to this ambitious effort. This rich environment, predictably, yields a diversity of approaches and organizational structures as each of the centers works to implement their particular vision of how to fulfill the overall mission of the NCBC program. Preserving the vitality of the effort depends on retaining this autonomy as each center strives to meet the individual objectives suited to their communities. The program needs well defined goals that each center must meet, but the overall program should facilitate the individual solutions of the center's leadership.

===Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing?===
(Martha Shenton)

* ''Greater Start Up Time and Steeper Learning Curve Than Anticipated''

Bringing together computer scientists, engineers, and biomedical researchers, with diverse interests, training, and background, for the purpose of working on a set of biological problems, is no easy feat and, in retrospect required a steeper learning curve than was anticipated. This steeper learning curve is understandable, since the main focus initially was on developing alliances among the cores in order to increase awareness about the kinds of tools needed for the specific imaging problems posed by the biomedical researchers who were driving the biological problems posed. The first year of the grant, as noted in our annual report, thus reflected a "core" emphasis, as an interdisciplinary team was brought together. It was not until the second year of the grant that the focus on a "core" emphasis shifted to a focus on "themes", which cut across "core" boundaries. While this shift was viewed as part of a natural evolution, knowing this now could help guide future NIH efforts in that specific projects could be highlighted in the first few months of the grant, based on meetings among core members, so as to facilitate a project/problem/theme approach from the outset. Such an early emphasis would also facilitate an early focus on the development and application of computational tools, which could be more closely aligned with specific clinical problems and applications. This would breakdown artifical barriers that a "core" focus involves, which while seemingly necessary for the initial stages, could be curtailed by highlighting early the need to focus on specific needed applications. In this way, the needed applications of the driving biological problems could form natural groupings that involve members from all cores, and work groups could be set up from the beginning that reflect a "theme"/"application" approach. This would also assist in more communication between core members, which would also likely facilitate ongoing communication among computer scientists, engineers, and biomedical researchers.

* ''Algorithm Development Needs to be Interactive and Not Sequential''

In reviewing the last three years of the driving biological problem, schizophrenia, it is evident that the tool development that involved multiple interactions among members of Core 1 (Computer Scientists), 2 (Engineers), and 3 (Driving Biological Problem), at all stages of development, led to the development of computational tools that were both more tailored to the specific applications needed by Core 3 members, as well as tools that were more optimized for general use. This interactive mode of tool development is in contrast to algorithm development that proceeded more sequentially, where one or several members of Core 1 and 3 met, and then Core 1 proceeded with what their understanding was of the problem and went off and developed a tool with very little further input from Core 3 until the tool was delivered. The latter approach often resulted in delays in receiving the tool, as there was less communication between Core 1 and Core 3 in these instances, and often the tool did not really meet the specific needs of the application without further work. In the future, and based on this experience, future NIH initiatives should emphasize the importance of encouraging a more "interactive" approach to algorithm development and to discourage what is termed here a more "sequential" approach to algorithm development. With a more interactive approach, progress can be more readily evaluated at each phase of tool development, and input and testing can be provided based on more communication among members of Core 1, 2, and 3. A more "interactive" model also ends up being far more responsive to the needs of the driving biological problem and also keeps the focus on the clinical application. Focusing on interactions across core members also will likely facilitate in breaking down the steep learning curve that seems inherent in early interactions across cores (see above).

* ''Do Not Limit Driving Biological Problems to 3 Years''

Given the problem of: (1) bringing new investigators together from diverse backgrounds, and (2) the problem with a "core" focus that detracts from focusing on the clinical problems posed by the driving biological problems, another issue is (3)limiting the time of the driving biological problem to three years. Even if the time table could be improved for getting investigators working together more quickly, and focusing on specific clinical problems right from the start, the limitation of the driving biological problem to 3 years is not realistic, particularly given that the third year is a time when the clinical applications begin to take shape following tool development. This is a time period when the driving biological problems are ready to reap not only the benefits of the new tools, but also a time when members representing the driving biological problem are ready to provide further feedback to computer scientists and engineers with respect to refining the tools so as to make them more suited to the task at hand, as well as making the new tools more user friendly for wider use. To end the driving biological problems at a time when the fruits of labor are just being reaped severely curtails the logical completion of the application of tools to clinical problems.

=='''Q3:A list of publications and/or software tools produced by the Center.''' If this information is provided in your progress report or is available on your website, a link will be sufficient. We are especially interested in your assessment of the maturity of your software tools and the impact they are having on the scientific community.===

(Will Schroeder, Allen Tannenbaum--THE PUBLICATION LIST NEEDS UPDATING. O'Donnell paper, for example was out there a while ago in AJNR--things missing from our group also)

A3:
*NA-MIC Publications are available here: http://www.na-mic.org/Wiki/index.php/Publications.
*NA-MIC Software Tools are available here: http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit
The Center has created and extended a number of software tools to handle some of the key problems in medical imaging analysis and to deliver these computational technologies via a suite of applications, toolkits, and infrastructure facilities. A summary description of these tools includes:
* Slicer3 (application) - a application platform for deploying imaging technologies, newly architected with an execution model facilitating integration, work flow, and large scale computing. While Slicer3 is the newest addition to the NAMIC Kit, it is built on pre-existing, mature toolkits so that the application is relatively mature, and is already in use. Because Slicer3 supports plug-in modules, active development is proceeding to create and package various modules for dissemination to the NAMIC community.
* ITK (toolkit) - a mature system for image analysis, registration and segmentation (initially created in 1999). ITK is in use worldwide for medical imaging research and development.
* VTK (toolkit) - a mature system for visualization, graphics, volume rendering, and interaction (initially created in 1993). VTK is used worldwide for research, teaching and commercial development.
* DART (computational infrastructure) - a key component of the NAMIC quality control process, DART is used to coordinate and report the software testing process. It was created in the first year of NAMIC and is in constant use, therefore a mature system.
* CMake/CTest/CPack (computational infrastructure) - CMake and CTest are relatively mature systems used to manage the building and testing of software across diverse computer platforms. CMake is used worldwide by some of the [http://lwn.net/Articles// world's largest open source systems such as KDE]. CPack, a recent addition to the NAMIC kit, is used to simplify the packaging and dissemination of software across platforms. Thus in NAMIC we can easily deploy our software across Windows, Linux, Unix, and Mac platforms.
* Other tools (computational infrastructure) - Many other software tools are used to support the development of advanced imaging applications, and to assist with large scale computing, including
** Teem - image processing tools (mature)
** KWWidgets - Open source, cross platform GUI toolkit (mature, but development continues to support workflow).
** BatchMake - Support large-scale computing, including grid computing, for performing large population studies and statistical analysis (under active development).

These tools address key problems in imaging including segmentation, registration, visualization, and shape analysis; or provide facilities supporting researchers and developers who wish to create advanced software applications. One of key characteristics of NAMIC is that we treat the development of advanced medical image analysis software holistically; that is, the complete cycle of algorithm design, efficient impmentations, quality control and dissemination are needed to effectively address challenges provided by the driving biological problems. Examples of how these tools are being used include the following:

(a) Segmentation: Here there are a variety of tools of varying degrees of maturity ranging from the EM Segmentor (a widely distributed, mature algorithm included in Slicer3 as an application plug-in) to more recent work on DTI segmentation based on directional flows which are Matlab and C++ based. Powerful mature tools such as Bayesian segmentation have been recently included in Slicer (and have been available in ITK for some time now) which can be combined with very recent work on the semi-automated segmentation of the DPFC done in collaboration with Core 3 researchers. Further, tools previously developed by NAMIC researchers which have had a wide distribution have been put into Slicer. For example, geometric based segmentation methods (some of which were included in packages marketed by GE) were tailored for cortical segmentation, and included in the Slicer, and in fact even improved with the inclusion of statistical based approaches.

(b) Registration: Similar remarks can be made for registration in which we have a spectrum ranging from very mature methods to very recent ones which are still being tested. In particular, mature widely distributed methodologies for rigid registration are now included in ITK, as well as spline-based registration methodologies. These are well-tested methods which have been made accessible to the general imaging community. Newer methodologies such as those based on optimal transport for elastic registration are being included in ITK. NAMIC has also pushed for fast implementations of its algorithms to be used on cheap widely available platforms. Taking a cue from the game industry, some algorithms have been ported to GPUs (graphics cards) which are being employed now as computing devices. This has led to a speed-up of almost two orders of magnitude on some of the registration algorithms being tested.

(c) Shape Analysis: Again a number of methodologies have been developed and implemented with varying levels of maturity. Shape methodologies based on spherical harmonics are quite mature, and are available in pipelines developed by NAMIC researchers and have been distributed to the general community. A newer spherical based wavelet shape analysis package has been put into ITK, which also drives a novel shape-based segmentation procedure. More globally based spherical harmonic ideas have been combined with the multi-resolution spherical wavelet approach as a statistical shape based package for schizophrenia. This general technique may be used for other purposes as well, and is presently being ported to some work being done on the prostate. Work has also been accomplished on particle based approaches to this important problem area with the code put into ITK. Many times we work with a Matlab/C++ initial version of our codes, then move to ITK, and finally to Slicer. However, even at the Matlab/C++ stage, algorithms have been distributed and used in a clinical setting (for example, rule-based brain segmentation approaches).

(d) Diffusion Weighted Image (DWI) Analysis: A number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical analysis of fiber bundles have been contributed to the NAMIC toolkit. Some of these tools have been already integrated into diffusion dedicated ITK package with GUI as part of the NAMIC toolkit (e.g., FiberViewer-UNC) and into the Slicer platform (BWH tractography, clustering). The impact of NAMIC DWI analysis activities is best characterized by most recent journal articles and journal articles in print. The application of the NAMIC FiberViewer tool (UNC) in large clinical studies at UNC and Duke are in print (Taylor et al, Gilmore et al., Cascio et al.). Clinical application of the Slicer DTI package by BWH/MIT is reported in O’Donnell et al. Kuroki N. et al. and Nakamura et al.. The research by MGH is found in two journal publications by Tuch et al.. The description of the methodologies also appeared or will appear in peer reviewed journals (Corouge et al., Fletcher et al., Corouge et al.). New methods in development (Finsler metric GT, volumetric PDE based path analysis Utah, stochastic tractography BWH), and path of interest MGH, are currently tested on Core-3 DBP data, with the goal to give recommendations on which type of solution is appropriate to solve specific clinical analysis questions.

(e) Visualization: Core 2 researchers involved with NAMIC now (e.g., the founders of Kitware) were at the forefront of developing VTK (and of course ITK). Thus here we are considering technologies which are at a commercial level of development, and used at thousands of sites. Algorithms developed at NAMIC have driven new directions for these packages. Newer visualization methods, for example, the conformal flattening procedure have been ported to an ITK filter and is in the NAMIC Sandbox. Quasi-isometric methods for brain flattening from the MGH Free Surfer have become part of the NAMIC enterprise as well. These flattening procedures are very easy to use, and may also be employed for registration. Code for the control of distortion of the area in flattening has been incorporated which give area-preservation with minimal distortion. The techniques may be also used for several other purposes included automatic fly-throughs in endoscopy (incorporated into Slicer), and for texture mappings for general visualization purposes.

==Logistics==
When completed, the information should be sent to:

Gwen Jacobs, PhD

Professor of Neuroscience

Asst. CIO and Director of Academic Computing

1 Lewis Hall

Montana State University

Bozeman, MT 59717

406-994-7334 - phone

406-994-7077 - FAX

gwen@cns.montana.edu <mailto:gwen@cns.montana.edu>

2007 Materials for NCBC Program Review

2007-06-06T19:25:33Z

Shenton: /* Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing? */

==Materials requested for NCBC Program Review==

These are due to Gwen Jacobs by Friday, June 08, 2007.

=='''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''===
(Tina/some edits from Marty --Tina--you had schizophrenia listed as a NEW DBP, I put in VCFS--if this was in the annual report, it needs to be fixed. Also, under highlights--- I see what Core 1 was able to do because of data provided by core 3, but there are no highlights of what core 3 did with what Core 1 gave them --- don't you think this should be fixed? That is, there are NO HIGHLIGHTS listed for CORE 3 --)

Summary: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#1._Introduction

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with respect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs include a focus on Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

Highlights: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#3._Highlights

The third year of the NA-MIC project saw continued development and dissemination of medical image analysis software. The current progress is clearly characterized by a significant increase in the application of Core-1 and Core-2 tools to image data provided by the Core-3 DBP groups. The main reasons for this progress is two-fold, first there are several new methods that are out of the prototype stage and ready to be applied to large sets of imaging datasets, and second there are new high-resolution imaging data from high-field scanners that are more appropriate for the new tools than historical data with often very coarse slice resolution.

With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NA-MIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NA-MIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

*Advanced Algorithms
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NA-MIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NA-MIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The NA-MIC toolkit includes a comprehensive set of modules for analysis of diffusion weighted images (DWI), including improved calculation of tensors, interpolation, nonlinear deformation and statistics on tensor fields, novel methods for tractography and for optimal path finding, and clustering of sets of streamlines.
* A quantitative tractography package for user-guided geometric parametrization and statistical analysis of fiber bundles (FiberViewer) has been contributed to the NAMIC toolbox. This tool used in several ongoing clinical DTI studies.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NA-MIC Sandbox in preparation for formal acceptance into the NA-MIC Kit.
*Technology Deployment Platform: Slicer3
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NA-MIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NA-MIC Kit, and are now available to all other modules built on the Slicer3 framework.

*Outreach and Technology Transfer
Cores 4-5-6 continue to support, train and disseminate to the NA-MIC community, and the broader biomedical computing community.
* NA-MIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NA-MIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair. The training and dissemination will continue with a DTI workshop at the forthcoming Human Brain Mapping meeting and activities at MICCAI 2007, among others.

==Q2==
'''A brief statement - (one page per question, max) addressing each of the questions listed below. These are the questions that we have been asked to address in our report. Our goal in asking for this information is to be able to produce a report that reviews the program as a whole. Your view, from the vantage point of the center you direct, is critical to our work. In addition, your answers will provide us with more information that we can use in our discussion with program staff on June 11th. We know that some of this information can be found on your websites, so in those cases a link to the information would be most helpful.'''

===Q2.1 To what extent does the vision and direction of the NCBC initiative promote biomedical computing?===
(Eric/Polina)

(From Ross/edits Marty--THIS SECTION I THINK NEEDS MORE WORK AS I DON"T UNDERSTAND HOW IT ANSWERS THE QUESTION BEING ASKED) The fields of biological and medical imaging are exploding. Moreover, the combination of new acquisition and reconstruction techniques, new computing resources, and diverse biological and clinical applications have resulted in a massive proliferation of imaging data. This imaging data has the potential for having an important impact on basic biological science, in the development of new drugs and medical technologies, and in direct clinical practice - in both diagnosis and treatment. Thus, biomedical imaging analysis is one of the most important applications of biomedical computing.

To realize this potential will require new computational tools for image analysis. These tools will rely on a diverse set of technical knowledge including physics, systems, computer science, mathmatics, and statistics. The development of these tools will also incorporate in-depth knowledge of clinical and biological applications in diverse areas such as neuroscience, psychiatry, encology, cardiology, and biochemistry.

These computational tools must be scalable in several ways. First, they must be computationally scalable. Tools for image analysis must be suitable for quantative analyses on large sets of 3D data, and many current software packages for image processing are not appropriate for this. In order to serve computational needs across NIH, image analysis tools must also scale across application domains and address a variety of clinical and biomedical problems. In the spirit of the National Center, these tools must also scale across institutions and research groups; that is, they must be usable and sustainable outside the context of the Center itself.

I DONT REALLY UNDERSTAND WHAT POINT IS BEING MADE IN THE PARAGRAPH BELOW??? (MARTY)
The NAMIC NCBC addresses these issues through a variety of mechanisms. First it is truly an national center, with researchers from seven (?) institutions across the US that represent expertise in the wide range of disciplines described above. The distributed nature of the project provides technical ald clinical expertise, but it also enforces openness of the resource - the infrastructure must accessible in order for the center to operate effectively. NAMIC also includes a set of industrial partners in the engineering core (Core 2), who provide a set of scalable tools for developing, maintaining, and distributing software. The software is universally managable and maintainable, and it does not belong to any one research group --- it belongs to the community. The tools area also scalable across appliation domains. HUH??? While the initial DBPs focused on neuroscience, the new DBPs include oncology and associated R01s include biomechanics/orthopedics. Users of the software are even more diverse, and even include fields outside of medicine and biology. The strategy of NAMIC is to make sure that the effort scales over time as well. The liscensing agreement which is enforced on all of our development activities does not restrict use, and thus we anticipate (and promote) commerical use of the NAMIC software so that the impact of the project could be realized beyond the lifetime of the center.

===Q2.2 In what ways has the NCBC initiative advanced biomedical computing?===
(Tina)

Answer:http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#4._Impact_and_Value_to_Biocomputing

===Q2.3 Are the NCBCs interfacing appropriately? (recommended by RICC)===
(Will Schroeder)

===Q2.4. What new collaborations have been formed through the NCBC initiative?===
(Jim Miller)
**Answer: http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations
**http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events

*discuss what is new: interactions inside cores and between cores.

===Q2.5. What new training opportunities have the centers provided?===
(Randy Gollub)

**Answer: http://www.na-mic.org/Wiki/index.php/Training:Main

** The entire Slicer tutorial portfolio would not exist without NA-MIC. explain what this portfolio consists of.

===Q2.6. What changes could make the program more effective in the future?===
(Steve Pieper)

* ''National Visibility''

The overall objectives of the NCBC program calls for building computational infrastructure for the nation's biomedical research efforts -- this ambitious goal includes both computational and biomedical science, areas in which the existing centers have extensive experience, but also in nationwide "marketing" efforts where the scientific community is less adept. Several centers, including NA-MIC, have developed novel approaches to this problem through their training and dissemination cores, but as the output of the centers grows along with the number of potential users in the community and the potential impact, these critical resources will be increasingly strained. There are several approaches the overall program could take to address this issue including supplemental funding to host workshops or conferences, providing small travel grants for researchers or students to visit the centers, or actively encouraging a wider range of NIH funded researchers to adopt the tools generated by the NCBCs. In particular, collaboration PAR has been very effective as a mechanism for encouraging busy scientists to consider adopting the NCBC tools and is an excellent example of how the program can extend the impact of the basic investment in scientific infrastructure.

* ''Local Autonomy''

The program should avoid adding extra layers of uniformity to what are fundamentally unique centers. The NCBC program has successfully established a distributed network of centers drawing on the expertise of some of the nation's leading researchers drawn to the program for the opportunity to develop and apply their know-how to this ambitious effort. This rich environment, predictably, yields a diversity of approaches and organizational structures as each of the centers works to implement their particular vision of how to fulfill the overall mission of the NCBC program. Preserving the vitality of the effort depends on retaining this autonomy as each center strives to meet the individual objectives suited to their communities. The program needs well defined goals that each center must meet, but the overall program should facilitate the individual solutions of the center's leadership.

===Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing?===
(Martha Shenton)

* "Greater Start Up Time and Steeper Learning Curve Than Anticipated"

Bringing together computer scientists, engineers, and biomedical researchers, who are from very different backgrounds, to work on a core set of biological problems is no easy feat, and, in fact, takes longer than we would have anticipated. In retrospect this is understandable as the initial time was spent in defining the Cores and in forming Core identities that would be brought to bear in challenges presented by the driving biological problem. As described in the first year progress report,

kdkdkdIn the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit.

There are several lessons that have been learned from the initial phases of this NCBC that might help to guide future NIH efforts in biomedical computing. Briefly, they fall into:
First, in bringing together computer scientists, engineers, and application scientists, the learning curve is initially much greater than many of us would have anticipated, as the different skills sets of such a multidisclinary enterprise requires that the application scientists who are driving the biological problems, must learn to communicate with the computer scientists and engineers. Coming from different disciplines, this is not always easy and it takes some amount of time to define the problems succinctly, concretely, and with sufficient clarity that computer scientists and engineers understand what tasks and needed to assist in solving the challenges posed by the application scientists, and how the computer scientists and engineers might best develop algorithms to address these challenges. If the scientists coming together have not worked with each other before, and are from different scientific disciplines, there is a time period needed to define the problems and to discuss how they might be approached. This can take anywhere from a year to a year and a half.

=='''Q3:A list of publications and/or software tools produced by the Center.''' If this information is provided in your progress report or is available on your website, a link will be sufficient. We are especially interested in your assessment of the maturity of your software tools and the impact they are having on the scientific community.===

(Will Schroeder, Allen Tannenbaum--THE PUBLICATION LIST NEEDS UPDATING. O'Donnell paper, for example was out there a while ago in AJNR--things missing from our group also)

A3:
*NA-MIC Publications are available here: http://www.na-mic.org/Wiki/index.php/Publications.
*NA-MIC Software Tools are available here: http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit
The Center has created and extended a number of software tools to handle some of the key problems in medical imaging analysis and to deliver these computational technologies via a suite of applications, toolkits, and infrastructure facilities. A summary description of these tools includes:
* Slicer3 (application) - a application platform for deploying imaging technologies, newly architected with an execution model facilitating integration, work flow, and large scale computing. While Slicer3 is the newest addition to the NAMIC Kit, it is built on pre-existing, mature toolkits so that the application is relatively mature, and is already in use. Because Slicer3 supports plug-in modules, active development is proceeding to create and package various modules for dissemination to the NAMIC community.
* ITK (toolkit) - a mature system for image analysis, registration and segmentation (initially created in 1999). ITK is in use worldwide for medical imaging research and development.
* VTK (toolkit) - a mature system for visualization, graphics, volume rendering, and interaction (initially created in 1993). VTK is used worldwide for research, teaching and commercial development.
* DART (computational infrastructure) - a key component of the NAMIC quality control process, DART is used to coordinate and report the software testing process. It was created in the first year of NAMIC and is in constant use, therefore a mature system.
* CMake/CTest/CPack (computational infrastructure) - CMake and CTest are relatively mature systems used to manage the building and testing of software across diverse computer platforms. CMake is used worldwide by some of the [http://lwn.net/Articles// world's largest open source systems such as KDE]. CPack, a recent addition to the NAMIC kit, is used to simplify the packaging and dissemination of software across platforms. Thus in NAMIC we can easily deploy our software across Windows, Linux, Unix, and Mac platforms.
* Other tools (computational infrastructure) - Many other software tools are used to support the development of advanced imaging applications, and to assist with large scale computing, including
** Teem - image processing tools (mature)
** KWWidgets - Open source, cross platform GUI toolkit (mature, but development continues to support workflow).
** BatchMake - Support large-scale computing, including grid computing, for performing large population studies and statistical analysis (under active development).

These tools address key problems in imaging including segmentation, registration, visualization, and shape analysis; or provide facilities supporting researchers and developers who wish to create advanced software applications. One of key characteristics of NAMIC is that we treat the development of advanced medical image analysis software holistically; that is, the complete cycle of algorithm design, efficient impmentations, quality control and dissemination are needed to effectively address challenges provided by the driving biological problems. Examples of how these tools are being used include the following:

(a) Segmentation: Here there are a variety of tools of varying degrees of maturity ranging from the EM Segmentor (a widely distributed, mature algorithm included in Slicer3 as an application plug-in) to more recent work on DTI segmentation based on directional flows which are Matlab and C++ based. Powerful mature tools such as Bayesian segmentation have been recently included in Slicer (and have been available in ITK for some time now) which can be combined with very recent work on the semi-automated segmentation of the DPFC done in collaboration with Core 3 researchers. Further, tools previously developed by NAMIC researchers which have had a wide distribution have been put into Slicer. For example, geometric based segmentation methods (some of which were included in packages marketed by GE) were tailored for cortical segmentation, and included in the Slicer, and in fact even improved with the inclusion of statistical based approaches.

(b) Registration: Similar remarks can be made for registration in which we have a spectrum ranging from very mature methods to very recent ones which are still being tested. In particular, mature widely distributed methodologies for rigid registration are now included in ITK, as well as spline-based registration methodologies. These are well-tested methods which have been made accessible to the general imaging community. Newer methodologies such as those based on optimal transport for elastic registration are being included in ITK. NAMIC has also pushed for fast implementations of its algorithms to be used on cheap widely available platforms. Taking a cue from the game industry, some algorithms have been ported to GPUs (graphics cards) which are being employed now as computing devices. This has led to a speed-up of almost two orders of magnitude on some of the registration algorithms being tested.

(c) Shape Analysis: Again a number of methodologies have been developed and implemented with varying levels of maturity. Shape methodologies based on spherical harmonics are quite mature, and are available in pipelines developed by NAMIC researchers and have been distributed to the general community. A newer spherical based wavelet shape analysis package has been put into ITK, which also drives a novel shape-based segmentation procedure. More globally based spherical harmonic ideas have been combined with the multi-resolution spherical wavelet approach as a statistical shape based package for schizophrenia. This general technique may be used for other purposes as well, and is presently being ported to some work being done on the prostate. Work has also been accomplished on particle based approaches to this important problem area with the code put into ITK. Many times we work with a Matlab/C++ initial version of our codes, then move to ITK, and finally to Slicer. However, even at the Matlab/C++ stage, algorithms have been distributed and used in a clinical setting (for example, rule-based brain segmentation approaches).

(d) Diffusion Weighted Image (DWI) Analysis: A number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical analysis of fiber bundles have been contributed to the NAMIC toolkit. Some of these tools have been already integrated into diffusion dedicated ITK package with GUI as part of the NAMIC toolkit (e.g., FiberViewer-UNC) and into the Slicer platform (BWH tractography, clustering). The impact of NAMIC DWI analysis activities is best characterized by most recent journal articles and journal articles in print. The application of the NAMIC FiberViewer tool (UNC) in large clinical studies at UNC and Duke are in print (Taylor et al, Gilmore et al., Cascio et al.). Clinical application of the Slicer DTI package by BWH/MIT is reported in O’Donnell et al. Kuroki N. et al. and Nakamura et al.. The research by MGH is found in two journal publications by Tuch et al.. The description of the methodologies also appeared or will appear in peer reviewed journals (Corouge et al., Fletcher et al., Corouge et al.). New methods in development (Finsler metric GT, volumetric PDE based path analysis Utah, stochastic tractography BWH), and path of interest MGH, are currently tested on Core-3 DBP data, with the goal to give recommendations on which type of solution is appropriate to solve specific clinical analysis questions.

(e) Visualization: Core 2 researchers involved with NAMIC now (e.g., the founders of Kitware) were at the forefront of developing VTK (and of course ITK). Thus here we are considering technologies which are at a commercial level of development, and used at thousands of sites. Algorithms developed at NAMIC have driven new directions for these packages. Newer visualization methods, for example, the conformal flattening procedure have been ported to an ITK filter and is in the NAMIC Sandbox. Quasi-isometric methods for brain flattening from the MGH Free Surfer have become part of the NAMIC enterprise as well. These flattening procedures are very easy to use, and may also be employed for registration. Code for the control of distortion of the area in flattening has been incorporated which give area-preservation with minimal distortion. The techniques may be also used for several other purposes included automatic fly-throughs in endoscopy (incorporated into Slicer), and for texture mappings for general visualization purposes.

==Logistics==
When completed, the information should be sent to:

Gwen Jacobs, PhD

Professor of Neuroscience

Asst. CIO and Director of Academic Computing

1 Lewis Hall

Montana State University

Bozeman, MT 59717

406-994-7334 - phone

406-994-7077 - FAX

gwen@cns.montana.edu <mailto:gwen@cns.montana.edu>

2007 Materials for NCBC Program Review

2007-06-06T19:24:39Z

Shenton: /* Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing? */

==Materials requested for NCBC Program Review==

These are due to Gwen Jacobs by Friday, June 08, 2007.

=='''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''===
(Tina/some edits from Marty --Tina--you had schizophrenia listed as a NEW DBP, I put in VCFS--if this was in the annual report, it needs to be fixed. Also, under highlights--- I see what Core 1 was able to do because of data provided by core 3, but there are no highlights of what core 3 did with what Core 1 gave them --- don't you think this should be fixed? That is, there are NO HIGHLIGHTS listed for CORE 3 --)

Summary: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#1._Introduction

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with respect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs include a focus on Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

Highlights: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#3._Highlights

The third year of the NA-MIC project saw continued development and dissemination of medical image analysis software. The current progress is clearly characterized by a significant increase in the application of Core-1 and Core-2 tools to image data provided by the Core-3 DBP groups. The main reasons for this progress is two-fold, first there are several new methods that are out of the prototype stage and ready to be applied to large sets of imaging datasets, and second there are new high-resolution imaging data from high-field scanners that are more appropriate for the new tools than historical data with often very coarse slice resolution.

With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NA-MIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NA-MIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

*Advanced Algorithms
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NA-MIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NA-MIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The NA-MIC toolkit includes a comprehensive set of modules for analysis of diffusion weighted images (DWI), including improved calculation of tensors, interpolation, nonlinear deformation and statistics on tensor fields, novel methods for tractography and for optimal path finding, and clustering of sets of streamlines.
* A quantitative tractography package for user-guided geometric parametrization and statistical analysis of fiber bundles (FiberViewer) has been contributed to the NAMIC toolbox. This tool used in several ongoing clinical DTI studies.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NA-MIC Sandbox in preparation for formal acceptance into the NA-MIC Kit.
*Technology Deployment Platform: Slicer3
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NA-MIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NA-MIC Kit, and are now available to all other modules built on the Slicer3 framework.

*Outreach and Technology Transfer
Cores 4-5-6 continue to support, train and disseminate to the NA-MIC community, and the broader biomedical computing community.
* NA-MIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NA-MIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair. The training and dissemination will continue with a DTI workshop at the forthcoming Human Brain Mapping meeting and activities at MICCAI 2007, among others.

==Q2==
'''A brief statement - (one page per question, max) addressing each of the questions listed below. These are the questions that we have been asked to address in our report. Our goal in asking for this information is to be able to produce a report that reviews the program as a whole. Your view, from the vantage point of the center you direct, is critical to our work. In addition, your answers will provide us with more information that we can use in our discussion with program staff on June 11th. We know that some of this information can be found on your websites, so in those cases a link to the information would be most helpful.'''

===Q2.1 To what extent does the vision and direction of the NCBC initiative promote biomedical computing?===
(Eric/Polina)

(From Ross/edits Marty--THIS SECTION I THINK NEEDS MORE WORK AS I DON"T UNDERSTAND HOW IT ANSWERS THE QUESTION BEING ASKED) The fields of biological and medical imaging are exploding. Moreover, the combination of new acquisition and reconstruction techniques, new computing resources, and diverse biological and clinical applications have resulted in a massive proliferation of imaging data. This imaging data has the potential for having an important impact on basic biological science, in the development of new drugs and medical technologies, and in direct clinical practice - in both diagnosis and treatment. Thus, biomedical imaging analysis is one of the most important applications of biomedical computing.

To realize this potential will require new computational tools for image analysis. These tools will rely on a diverse set of technical knowledge including physics, systems, computer science, mathmatics, and statistics. The development of these tools will also incorporate in-depth knowledge of clinical and biological applications in diverse areas such as neuroscience, psychiatry, encology, cardiology, and biochemistry.

These computational tools must be scalable in several ways. First, they must be computationally scalable. Tools for image analysis must be suitable for quantative analyses on large sets of 3D data, and many current software packages for image processing are not appropriate for this. In order to serve computational needs across NIH, image analysis tools must also scale across application domains and address a variety of clinical and biomedical problems. In the spirit of the National Center, these tools must also scale across institutions and research groups; that is, they must be usable and sustainable outside the context of the Center itself.

I DONT REALLY UNDERSTAND WHAT POINT IS BEING MADE IN THE PARAGRAPH BELOW??? (MARTY)
The NAMIC NCBC addresses these issues through a variety of mechanisms. First it is truly an national center, with researchers from seven (?) institutions across the US that represent expertise in the wide range of disciplines described above. The distributed nature of the project provides technical ald clinical expertise, but it also enforces openness of the resource - the infrastructure must accessible in order for the center to operate effectively. NAMIC also includes a set of industrial partners in the engineering core (Core 2), who provide a set of scalable tools for developing, maintaining, and distributing software. The software is universally managable and maintainable, and it does not belong to any one research group --- it belongs to the community. The tools area also scalable across appliation domains. HUH??? While the initial DBPs focused on neuroscience, the new DBPs include oncology and associated R01s include biomechanics/orthopedics. Users of the software are even more diverse, and even include fields outside of medicine and biology. The strategy of NAMIC is to make sure that the effort scales over time as well. The liscensing agreement which is enforced on all of our development activities does not restrict use, and thus we anticipate (and promote) commerical use of the NAMIC software so that the impact of the project could be realized beyond the lifetime of the center.

===Q2.2 In what ways has the NCBC initiative advanced biomedical computing?===
(Tina)

Answer:http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#4._Impact_and_Value_to_Biocomputing

===Q2.3 Are the NCBCs interfacing appropriately? (recommended by RICC)===
(Will Schroeder)

===Q2.4. What new collaborations have been formed through the NCBC initiative?===
(Jim Miller)
**Answer: http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations
**http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events

*discuss what is new: interactions inside cores and between cores.

===Q2.5. What new training opportunities have the centers provided?===
(Randy Gollub)

**Answer: http://www.na-mic.org/Wiki/index.php/Training:Main

** The entire Slicer tutorial portfolio would not exist without NA-MIC. explain what this portfolio consists of.

===Q2.6. What changes could make the program more effective in the future?===
(Steve Pieper)

* ''National Visibility''

The overall objectives of the NCBC program calls for building computational infrastructure for the nation's biomedical research efforts -- this ambitious goal includes both computational and biomedical science, areas in which the existing centers have extensive experience, but also in nationwide "marketing" efforts where the scientific community is less adept. Several centers, including NA-MIC, have developed novel approaches to this problem through their training and dissemination cores, but as the output of the centers grows along with the number of potential users in the community and the potential impact, these critical resources will be increasingly strained. There are several approaches the overall program could take to address this issue including supplemental funding to host workshops or conferences, providing small travel grants for researchers or students to visit the centers, or actively encouraging a wider range of NIH funded researchers to adopt the tools generated by the NCBCs. In particular, collaboration PAR has been very effective as a mechanism for encouraging busy scientists to consider adopting the NCBC tools and is an excellent example of how the program can extend the impact of the basic investment in scientific infrastructure.

* ''Local Autonomy''

The program should avoid adding extra layers of uniformity to what are fundamentally unique centers. The NCBC program has successfully established a distributed network of centers drawing on the expertise of some of the nation's leading researchers drawn to the program for the opportunity to develop and apply their know-how to this ambitious effort. This rich environment, predictably, yields a diversity of approaches and organizational structures as each of the centers works to implement their particular vision of how to fulfill the overall mission of the NCBC program. Preserving the vitality of the effort depends on retaining this autonomy as each center strives to meet the individual objectives suited to their communities. The program needs well defined goals that each center must meet, but the overall program should facilitate the individual solutions of the center's leadership.

===Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing?===
(Martha Shenton)

* "Greater Start Up Time and Steeper Learning Curve Than Anticipated"

Bringing together computer scientists, engineers, and biomedical researchers, who are from very different backgrounds, to work on a core set of biological problems is no easy feat, and, in fact, takes longer than we would have anticipated. In retrospect this is understandable as the initial time was spent in defining the Cores and in forming Core identities that would be brought to bear in challenges presented by the driving biological problem. As described in the first year progress report,

kdkdkd

There are several lessons that have been learned from the initial phases of this NCBC that might help to guide future NIH efforts in biomedical computing. Briefly, they fall into:
First, in bringing together computer scientists, engineers, and application scientists, the learning curve is initially much greater than many of us would have anticipated, as the different skills sets of such a multidisclinary enterprise requires that the application scientists who are driving the biological problems, must learn to communicate with the computer scientists and engineers. Coming from different disciplines, this is not always easy and it takes some amount of time to define the problems succinctly, concretely, and with sufficient clarity that computer scientists and engineers understand what tasks and needed to assist in solving the challenges posed by the application scientists, and how the computer scientists and engineers might best develop algorithms to address these challenges. If the scientists coming together have not worked with each other before, and are from different scientific disciplines, there is a time period needed to define the problems and to discuss how they might be approached. This can take anywhere from a year to a year and a half.

=='''Q3:A list of publications and/or software tools produced by the Center.''' If this information is provided in your progress report or is available on your website, a link will be sufficient. We are especially interested in your assessment of the maturity of your software tools and the impact they are having on the scientific community.===

(Will Schroeder, Allen Tannenbaum--THE PUBLICATION LIST NEEDS UPDATING. O'Donnell paper, for example was out there a while ago in AJNR--things missing from our group also)

A3:
*NA-MIC Publications are available here: http://www.na-mic.org/Wiki/index.php/Publications.
*NA-MIC Software Tools are available here: http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit
The Center has created and extended a number of software tools to handle some of the key problems in medical imaging analysis and to deliver these computational technologies via a suite of applications, toolkits, and infrastructure facilities. A summary description of these tools includes:
* Slicer3 (application) - a application platform for deploying imaging technologies, newly architected with an execution model facilitating integration, work flow, and large scale computing. While Slicer3 is the newest addition to the NAMIC Kit, it is built on pre-existing, mature toolkits so that the application is relatively mature, and is already in use. Because Slicer3 supports plug-in modules, active development is proceeding to create and package various modules for dissemination to the NAMIC community.
* ITK (toolkit) - a mature system for image analysis, registration and segmentation (initially created in 1999). ITK is in use worldwide for medical imaging research and development.
* VTK (toolkit) - a mature system for visualization, graphics, volume rendering, and interaction (initially created in 1993). VTK is used worldwide for research, teaching and commercial development.
* DART (computational infrastructure) - a key component of the NAMIC quality control process, DART is used to coordinate and report the software testing process. It was created in the first year of NAMIC and is in constant use, therefore a mature system.
* CMake/CTest/CPack (computational infrastructure) - CMake and CTest are relatively mature systems used to manage the building and testing of software across diverse computer platforms. CMake is used worldwide by some of the [http://lwn.net/Articles// world's largest open source systems such as KDE]. CPack, a recent addition to the NAMIC kit, is used to simplify the packaging and dissemination of software across platforms. Thus in NAMIC we can easily deploy our software across Windows, Linux, Unix, and Mac platforms.
* Other tools (computational infrastructure) - Many other software tools are used to support the development of advanced imaging applications, and to assist with large scale computing, including
** Teem - image processing tools (mature)
** KWWidgets - Open source, cross platform GUI toolkit (mature, but development continues to support workflow).
** BatchMake - Support large-scale computing, including grid computing, for performing large population studies and statistical analysis (under active development).

These tools address key problems in imaging including segmentation, registration, visualization, and shape analysis; or provide facilities supporting researchers and developers who wish to create advanced software applications. One of key characteristics of NAMIC is that we treat the development of advanced medical image analysis software holistically; that is, the complete cycle of algorithm design, efficient impmentations, quality control and dissemination are needed to effectively address challenges provided by the driving biological problems. Examples of how these tools are being used include the following:

(a) Segmentation: Here there are a variety of tools of varying degrees of maturity ranging from the EM Segmentor (a widely distributed, mature algorithm included in Slicer3 as an application plug-in) to more recent work on DTI segmentation based on directional flows which are Matlab and C++ based. Powerful mature tools such as Bayesian segmentation have been recently included in Slicer (and have been available in ITK for some time now) which can be combined with very recent work on the semi-automated segmentation of the DPFC done in collaboration with Core 3 researchers. Further, tools previously developed by NAMIC researchers which have had a wide distribution have been put into Slicer. For example, geometric based segmentation methods (some of which were included in packages marketed by GE) were tailored for cortical segmentation, and included in the Slicer, and in fact even improved with the inclusion of statistical based approaches.

(b) Registration: Similar remarks can be made for registration in which we have a spectrum ranging from very mature methods to very recent ones which are still being tested. In particular, mature widely distributed methodologies for rigid registration are now included in ITK, as well as spline-based registration methodologies. These are well-tested methods which have been made accessible to the general imaging community. Newer methodologies such as those based on optimal transport for elastic registration are being included in ITK. NAMIC has also pushed for fast implementations of its algorithms to be used on cheap widely available platforms. Taking a cue from the game industry, some algorithms have been ported to GPUs (graphics cards) which are being employed now as computing devices. This has led to a speed-up of almost two orders of magnitude on some of the registration algorithms being tested.

(c) Shape Analysis: Again a number of methodologies have been developed and implemented with varying levels of maturity. Shape methodologies based on spherical harmonics are quite mature, and are available in pipelines developed by NAMIC researchers and have been distributed to the general community. A newer spherical based wavelet shape analysis package has been put into ITK, which also drives a novel shape-based segmentation procedure. More globally based spherical harmonic ideas have been combined with the multi-resolution spherical wavelet approach as a statistical shape based package for schizophrenia. This general technique may be used for other purposes as well, and is presently being ported to some work being done on the prostate. Work has also been accomplished on particle based approaches to this important problem area with the code put into ITK. Many times we work with a Matlab/C++ initial version of our codes, then move to ITK, and finally to Slicer. However, even at the Matlab/C++ stage, algorithms have been distributed and used in a clinical setting (for example, rule-based brain segmentation approaches).

(d) Diffusion Weighted Image (DWI) Analysis: A number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical analysis of fiber bundles have been contributed to the NAMIC toolkit. Some of these tools have been already integrated into diffusion dedicated ITK package with GUI as part of the NAMIC toolkit (e.g., FiberViewer-UNC) and into the Slicer platform (BWH tractography, clustering). The impact of NAMIC DWI analysis activities is best characterized by most recent journal articles and journal articles in print. The application of the NAMIC FiberViewer tool (UNC) in large clinical studies at UNC and Duke are in print (Taylor et al, Gilmore et al., Cascio et al.). Clinical application of the Slicer DTI package by BWH/MIT is reported in O’Donnell et al. Kuroki N. et al. and Nakamura et al.. The research by MGH is found in two journal publications by Tuch et al.. The description of the methodologies also appeared or will appear in peer reviewed journals (Corouge et al., Fletcher et al., Corouge et al.). New methods in development (Finsler metric GT, volumetric PDE based path analysis Utah, stochastic tractography BWH), and path of interest MGH, are currently tested on Core-3 DBP data, with the goal to give recommendations on which type of solution is appropriate to solve specific clinical analysis questions.

(e) Visualization: Core 2 researchers involved with NAMIC now (e.g., the founders of Kitware) were at the forefront of developing VTK (and of course ITK). Thus here we are considering technologies which are at a commercial level of development, and used at thousands of sites. Algorithms developed at NAMIC have driven new directions for these packages. Newer visualization methods, for example, the conformal flattening procedure have been ported to an ITK filter and is in the NAMIC Sandbox. Quasi-isometric methods for brain flattening from the MGH Free Surfer have become part of the NAMIC enterprise as well. These flattening procedures are very easy to use, and may also be employed for registration. Code for the control of distortion of the area in flattening has been incorporated which give area-preservation with minimal distortion. The techniques may be also used for several other purposes included automatic fly-throughs in endoscopy (incorporated into Slicer), and for texture mappings for general visualization purposes.

==Logistics==
When completed, the information should be sent to:

Gwen Jacobs, PhD

Professor of Neuroscience

Asst. CIO and Director of Academic Computing

1 Lewis Hall

Montana State University

Bozeman, MT 59717

406-994-7334 - phone

406-994-7077 - FAX

gwen@cns.montana.edu <mailto:gwen@cns.montana.edu>

2007 Materials for NCBC Program Review

2007-06-06T19:20:02Z

Shenton: /* Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing? */

==Materials requested for NCBC Program Review==

These are due to Gwen Jacobs by Friday, June 08, 2007.

=='''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''===
(Tina/some edits from Marty --Tina--you had schizophrenia listed as a NEW DBP, I put in VCFS--if this was in the annual report, it needs to be fixed. Also, under highlights--- I see what Core 1 was able to do because of data provided by core 3, but there are no highlights of what core 3 did with what Core 1 gave them --- don't you think this should be fixed? That is, there are NO HIGHLIGHTS listed for CORE 3 --)

Summary: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#1._Introduction

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with respect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs include a focus on Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

Highlights: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#3._Highlights

The third year of the NA-MIC project saw continued development and dissemination of medical image analysis software. The current progress is clearly characterized by a significant increase in the application of Core-1 and Core-2 tools to image data provided by the Core-3 DBP groups. The main reasons for this progress is two-fold, first there are several new methods that are out of the prototype stage and ready to be applied to large sets of imaging datasets, and second there are new high-resolution imaging data from high-field scanners that are more appropriate for the new tools than historical data with often very coarse slice resolution.

With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NA-MIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NA-MIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

*Advanced Algorithms
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NA-MIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NA-MIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The NA-MIC toolkit includes a comprehensive set of modules for analysis of diffusion weighted images (DWI), including improved calculation of tensors, interpolation, nonlinear deformation and statistics on tensor fields, novel methods for tractography and for optimal path finding, and clustering of sets of streamlines.
* A quantitative tractography package for user-guided geometric parametrization and statistical analysis of fiber bundles (FiberViewer) has been contributed to the NAMIC toolbox. This tool used in several ongoing clinical DTI studies.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NA-MIC Sandbox in preparation for formal acceptance into the NA-MIC Kit.
*Technology Deployment Platform: Slicer3
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NA-MIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NA-MIC Kit, and are now available to all other modules built on the Slicer3 framework.

*Outreach and Technology Transfer
Cores 4-5-6 continue to support, train and disseminate to the NA-MIC community, and the broader biomedical computing community.
* NA-MIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NA-MIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair. The training and dissemination will continue with a DTI workshop at the forthcoming Human Brain Mapping meeting and activities at MICCAI 2007, among others.

==Q2==
'''A brief statement - (one page per question, max) addressing each of the questions listed below. These are the questions that we have been asked to address in our report. Our goal in asking for this information is to be able to produce a report that reviews the program as a whole. Your view, from the vantage point of the center you direct, is critical to our work. In addition, your answers will provide us with more information that we can use in our discussion with program staff on June 11th. We know that some of this information can be found on your websites, so in those cases a link to the information would be most helpful.'''

===Q2.1 To what extent does the vision and direction of the NCBC initiative promote biomedical computing?===
(Eric/Polina)

(From Ross/edits Marty--THIS SECTION I THINK NEEDS MORE WORK AS I DON"T UNDERSTAND HOW IT ANSWERS THE QUESTION BEING ASKED) The fields of biological and medical imaging are exploding. Moreover, the combination of new acquisition and reconstruction techniques, new computing resources, and diverse biological and clinical applications have resulted in a massive proliferation of imaging data. This imaging data has the potential for having an important impact on basic biological science, in the development of new drugs and medical technologies, and in direct clinical practice - in both diagnosis and treatment. Thus, biomedical imaging analysis is one of the most important applications of biomedical computing.

To realize this potential will require new computational tools for image analysis. These tools will rely on a diverse set of technical knowledge including physics, systems, computer science, mathmatics, and statistics. The development of these tools will also incorporate in-depth knowledge of clinical and biological applications in diverse areas such as neuroscience, psychiatry, encology, cardiology, and biochemistry.

These computational tools must be scalable in several ways. First, they must be computationally scalable. Tools for image analysis must be suitable for quantative analyses on large sets of 3D data, and many current software packages for image processing are not appropriate for this. In order to serve computational needs across NIH, image analysis tools must also scale across application domains and address a variety of clinical and biomedical problems. In the spirit of the National Center, these tools must also scale across institutions and research groups; that is, they must be usable and sustainable outside the context of the Center itself.

I DONT REALLY UNDERSTAND WHAT POINT IS BEING MADE IN THE PARAGRAPH BELOW??? (MARTY)
The NAMIC NCBC addresses these issues through a variety of mechanisms. First it is truly an national center, with researchers from seven (?) institutions across the US that represent expertise in the wide range of disciplines described above. The distributed nature of the project provides technical ald clinical expertise, but it also enforces openness of the resource - the infrastructure must accessible in order for the center to operate effectively. NAMIC also includes a set of industrial partners in the engineering core (Core 2), who provide a set of scalable tools for developing, maintaining, and distributing software. The software is universally managable and maintainable, and it does not belong to any one research group --- it belongs to the community. The tools area also scalable across appliation domains. HUH??? While the initial DBPs focused on neuroscience, the new DBPs include oncology and associated R01s include biomechanics/orthopedics. Users of the software are even more diverse, and even include fields outside of medicine and biology. The strategy of NAMIC is to make sure that the effort scales over time as well. The liscensing agreement which is enforced on all of our development activities does not restrict use, and thus we anticipate (and promote) commerical use of the NAMIC software so that the impact of the project could be realized beyond the lifetime of the center.

===Q2.2 In what ways has the NCBC initiative advanced biomedical computing?===
(Tina)

Answer:http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#4._Impact_and_Value_to_Biocomputing

===Q2.3 Are the NCBCs interfacing appropriately? (recommended by RICC)===
(Will Schroeder)

===Q2.4. What new collaborations have been formed through the NCBC initiative?===
(Jim Miller)
**Answer: http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations
**http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events

*discuss what is new: interactions inside cores and between cores.

===Q2.5. What new training opportunities have the centers provided?===
(Randy Gollub)

**Answer: http://www.na-mic.org/Wiki/index.php/Training:Main

** The entire Slicer tutorial portfolio would not exist without NA-MIC. explain what this portfolio consists of.

===Q2.6. What changes could make the program more effective in the future?===
(Steve Pieper)

* ''National Visibility''

The overall objectives of the NCBC program calls for building computational infrastructure for the nation's biomedical research efforts -- this ambitious goal includes both computational and biomedical science, areas in which the existing centers have extensive experience, but also in nationwide "marketing" efforts where the scientific community is less adept. Several centers, including NA-MIC, have developed novel approaches to this problem through their training and dissemination cores, but as the output of the centers grows along with the number of potential users in the community and the potential impact, these critical resources will be increasingly strained. There are several approaches the overall program could take to address this issue including supplemental funding to host workshops or conferences, providing small travel grants for researchers or students to visit the centers, or actively encouraging a wider range of NIH funded researchers to adopt the tools generated by the NCBCs. In particular, collaboration PAR has been very effective as a mechanism for encouraging busy scientists to consider adopting the NCBC tools and is an excellent example of how the program can extend the impact of the basic investment in scientific infrastructure.

* ''Local Autonomy''

The program should avoid adding extra layers of uniformity to what are fundamentally unique centers. The NCBC program has successfully established a distributed network of centers drawing on the expertise of some of the nation's leading researchers drawn to the program for the opportunity to develop and apply their know-how to this ambitious effort. This rich environment, predictably, yields a diversity of approaches and organizational structures as each of the centers works to implement their particular vision of how to fulfill the overall mission of the NCBC program. Preserving the vitality of the effort depends on retaining this autonomy as each center strives to meet the individual objectives suited to their communities. The program needs well defined goals that each center must meet, but the overall program should facilitate the individual solutions of the center's leadership.

===Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing?===
(Martha Shenton)

* Greater Start Up Time and Steeper Learning Curve for Bringing Diverse Expertise for Biomedical Computing.

kdkdkd

There are several lessons that have been learned from the initial phases of this NCBC that might help to guide future NIH efforts in biomedical computing. Briefly, they fall into:
First, in bringing together computer scientists, engineers, and application scientists, the learning curve is initially much greater than many of us would have anticipated, as the different skills sets of such a multidisclinary enterprise requires that the application scientists who are driving the biological problems, must learn to communicate with the computer scientists and engineers. Coming from different disciplines, this is not always easy and it takes some amount of time to define the problems succinctly, concretely, and with sufficient clarity that computer scientists and engineers understand what tasks and needed to assist in solving the challenges posed by the application scientists, and how the computer scientists and engineers might best develop algorithms to address these challenges. If the scientists coming together have not worked with each other before, and are from different scientific disciplines, there is a time period needed to define the problems and to discuss how they might be approached. This can take anywhere from a year to a year and a half.

=='''Q3:A list of publications and/or software tools produced by the Center.''' If this information is provided in your progress report or is available on your website, a link will be sufficient. We are especially interested in your assessment of the maturity of your software tools and the impact they are having on the scientific community.===

(Will Schroeder, Allen Tannenbaum--THE PUBLICATION LIST NEEDS UPDATING. O'Donnell paper, for example was out there a while ago in AJNR--things missing from our group also)

A3:
*NA-MIC Publications are available here: http://www.na-mic.org/Wiki/index.php/Publications.
*NA-MIC Software Tools are available here: http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit
The Center has created and extended a number of software tools to handle some of the key problems in medical imaging analysis and to deliver these computational technologies via a suite of applications, toolkits, and infrastructure facilities. A summary description of these tools includes:
* Slicer3 (application) - a application platform for deploying imaging technologies, newly architected with an execution model facilitating integration, work flow, and large scale computing. While Slicer3 is the newest addition to the NAMIC Kit, it is built on pre-existing, mature toolkits so that the application is relatively mature, and is already in use. Because Slicer3 supports plug-in modules, active development is proceeding to create and package various modules for dissemination to the NAMIC community.
* ITK (toolkit) - a mature system for image analysis, registration and segmentation (initially created in 1999). ITK is in use worldwide for medical imaging research and development.
* VTK (toolkit) - a mature system for visualization, graphics, volume rendering, and interaction (initially created in 1993). VTK is used worldwide for research, teaching and commercial development.
* DART (computational infrastructure) - a key component of the NAMIC quality control process, DART is used to coordinate and report the software testing process. It was created in the first year of NAMIC and is in constant use, therefore a mature system.
* CMake/CTest/CPack (computational infrastructure) - CMake and CTest are relatively mature systems used to manage the building and testing of software across diverse computer platforms. CMake is used worldwide by some of the [http://lwn.net/Articles// world's largest open source systems such as KDE]. CPack, a recent addition to the NAMIC kit, is used to simplify the packaging and dissemination of software across platforms. Thus in NAMIC we can easily deploy our software across Windows, Linux, Unix, and Mac platforms.
* Other tools (computational infrastructure) - Many other software tools are used to support the development of advanced imaging applications, and to assist with large scale computing, including
** Teem - image processing tools (mature)
** KWWidgets - Open source, cross platform GUI toolkit (mature, but development continues to support workflow).
** BatchMake - Support large-scale computing, including grid computing, for performing large population studies and statistical analysis (under active development).

These tools address key problems in imaging including segmentation, registration, visualization, and shape analysis; or provide facilities supporting researchers and developers who wish to create advanced software applications. One of key characteristics of NAMIC is that we treat the development of advanced medical image analysis software holistically; that is, the complete cycle of algorithm design, efficient impmentations, quality control and dissemination are needed to effectively address challenges provided by the driving biological problems. Examples of how these tools are being used include the following:

(a) Segmentation: Here there are a variety of tools of varying degrees of maturity ranging from the EM Segmentor (a widely distributed, mature algorithm included in Slicer3 as an application plug-in) to more recent work on DTI segmentation based on directional flows which are Matlab and C++ based. Powerful mature tools such as Bayesian segmentation have been recently included in Slicer (and have been available in ITK for some time now) which can be combined with very recent work on the semi-automated segmentation of the DPFC done in collaboration with Core 3 researchers. Further, tools previously developed by NAMIC researchers which have had a wide distribution have been put into Slicer. For example, geometric based segmentation methods (some of which were included in packages marketed by GE) were tailored for cortical segmentation, and included in the Slicer, and in fact even improved with the inclusion of statistical based approaches.

(b) Registration: Similar remarks can be made for registration in which we have a spectrum ranging from very mature methods to very recent ones which are still being tested. In particular, mature widely distributed methodologies for rigid registration are now included in ITK, as well as spline-based registration methodologies. These are well-tested methods which have been made accessible to the general imaging community. Newer methodologies such as those based on optimal transport for elastic registration are being included in ITK. NAMIC has also pushed for fast implementations of its algorithms to be used on cheap widely available platforms. Taking a cue from the game industry, some algorithms have been ported to GPUs (graphics cards) which are being employed now as computing devices. This has led to a speed-up of almost two orders of magnitude on some of the registration algorithms being tested.

(c) Shape Analysis: Again a number of methodologies have been developed and implemented with varying levels of maturity. Shape methodologies based on spherical harmonics are quite mature, and are available in pipelines developed by NAMIC researchers and have been distributed to the general community. A newer spherical based wavelet shape analysis package has been put into ITK, which also drives a novel shape-based segmentation procedure. More globally based spherical harmonic ideas have been combined with the multi-resolution spherical wavelet approach as a statistical shape based package for schizophrenia. This general technique may be used for other purposes as well, and is presently being ported to some work being done on the prostate. Work has also been accomplished on particle based approaches to this important problem area with the code put into ITK. Many times we work with a Matlab/C++ initial version of our codes, then move to ITK, and finally to Slicer. However, even at the Matlab/C++ stage, algorithms have been distributed and used in a clinical setting (for example, rule-based brain segmentation approaches).

(d) Diffusion Weighted Image (DWI) Analysis: A number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical analysis of fiber bundles have been contributed to the NAMIC toolkit. Some of these tools have been already integrated into diffusion dedicated ITK package with GUI as part of the NAMIC toolkit (e.g., FiberViewer-UNC) and into the Slicer platform (BWH tractography, clustering). The impact of NAMIC DWI analysis activities is best characterized by most recent journal articles and journal articles in print. The application of the NAMIC FiberViewer tool (UNC) in large clinical studies at UNC and Duke are in print (Taylor et al, Gilmore et al., Cascio et al.). Clinical application of the Slicer DTI package by BWH/MIT is reported in O’Donnell et al. Kuroki N. et al. and Nakamura et al.. The research by MGH is found in two journal publications by Tuch et al.. The description of the methodologies also appeared or will appear in peer reviewed journals (Corouge et al., Fletcher et al., Corouge et al.). New methods in development (Finsler metric GT, volumetric PDE based path analysis Utah, stochastic tractography BWH), and path of interest MGH, are currently tested on Core-3 DBP data, with the goal to give recommendations on which type of solution is appropriate to solve specific clinical analysis questions.

(e) Visualization: Core 2 researchers involved with NAMIC now (e.g., the founders of Kitware) were at the forefront of developing VTK (and of course ITK). Thus here we are considering technologies which are at a commercial level of development, and used at thousands of sites. Algorithms developed at NAMIC have driven new directions for these packages. Newer visualization methods, for example, the conformal flattening procedure have been ported to an ITK filter and is in the NAMIC Sandbox. Quasi-isometric methods for brain flattening from the MGH Free Surfer have become part of the NAMIC enterprise as well. These flattening procedures are very easy to use, and may also be employed for registration. Code for the control of distortion of the area in flattening has been incorporated which give area-preservation with minimal distortion. The techniques may be also used for several other purposes included automatic fly-throughs in endoscopy (incorporated into Slicer), and for texture mappings for general visualization purposes.

==Logistics==
When completed, the information should be sent to:

Gwen Jacobs, PhD

Professor of Neuroscience

Asst. CIO and Director of Academic Computing

1 Lewis Hall

Montana State University

Bozeman, MT 59717

406-994-7334 - phone

406-994-7077 - FAX

gwen@cns.montana.edu <mailto:gwen@cns.montana.edu>

2007 Materials for NCBC Program Review

2007-06-06T19:16:21Z

Shenton: /* Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing? */

==Materials requested for NCBC Program Review==

These are due to Gwen Jacobs by Friday, June 08, 2007.

=='''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''===
(Tina/some edits from Marty --Tina--you had schizophrenia listed as a NEW DBP, I put in VCFS--if this was in the annual report, it needs to be fixed. Also, under highlights--- I see what Core 1 was able to do because of data provided by core 3, but there are no highlights of what core 3 did with what Core 1 gave them --- don't you think this should be fixed? That is, there are NO HIGHLIGHTS listed for CORE 3 --)

Summary: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#1._Introduction

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with respect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs include a focus on Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

Highlights: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#3._Highlights

The third year of the NA-MIC project saw continued development and dissemination of medical image analysis software. The current progress is clearly characterized by a significant increase in the application of Core-1 and Core-2 tools to image data provided by the Core-3 DBP groups. The main reasons for this progress is two-fold, first there are several new methods that are out of the prototype stage and ready to be applied to large sets of imaging datasets, and second there are new high-resolution imaging data from high-field scanners that are more appropriate for the new tools than historical data with often very coarse slice resolution.

With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NA-MIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NA-MIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

*Advanced Algorithms
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NA-MIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NA-MIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The NA-MIC toolkit includes a comprehensive set of modules for analysis of diffusion weighted images (DWI), including improved calculation of tensors, interpolation, nonlinear deformation and statistics on tensor fields, novel methods for tractography and for optimal path finding, and clustering of sets of streamlines.
* A quantitative tractography package for user-guided geometric parametrization and statistical analysis of fiber bundles (FiberViewer) has been contributed to the NAMIC toolbox. This tool used in several ongoing clinical DTI studies.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NA-MIC Sandbox in preparation for formal acceptance into the NA-MIC Kit.
*Technology Deployment Platform: Slicer3
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NA-MIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NA-MIC Kit, and are now available to all other modules built on the Slicer3 framework.

*Outreach and Technology Transfer
Cores 4-5-6 continue to support, train and disseminate to the NA-MIC community, and the broader biomedical computing community.
* NA-MIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NA-MIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair. The training and dissemination will continue with a DTI workshop at the forthcoming Human Brain Mapping meeting and activities at MICCAI 2007, among others.

==Q2==
'''A brief statement - (one page per question, max) addressing each of the questions listed below. These are the questions that we have been asked to address in our report. Our goal in asking for this information is to be able to produce a report that reviews the program as a whole. Your view, from the vantage point of the center you direct, is critical to our work. In addition, your answers will provide us with more information that we can use in our discussion with program staff on June 11th. We know that some of this information can be found on your websites, so in those cases a link to the information would be most helpful.'''

===Q2.1 To what extent does the vision and direction of the NCBC initiative promote biomedical computing?===
(Eric/Polina)

(From Ross/edits Marty--THIS SECTION I THINK NEEDS MORE WORK AS I DON"T UNDERSTAND HOW IT ANSWERS THE QUESTION BEING ASKED) The fields of biological and medical imaging are exploding. Moreover, the combination of new acquisition and reconstruction techniques, new computing resources, and diverse biological and clinical applications have resulted in a massive proliferation of imaging data. This imaging data has the potential for having an important impact on basic biological science, in the development of new drugs and medical technologies, and in direct clinical practice - in both diagnosis and treatment. Thus, biomedical imaging analysis is one of the most important applications of biomedical computing.

To realize this potential will require new computational tools for image analysis. These tools will rely on a diverse set of technical knowledge including physics, systems, computer science, mathmatics, and statistics. The development of these tools will also incorporate in-depth knowledge of clinical and biological applications in diverse areas such as neuroscience, psychiatry, encology, cardiology, and biochemistry.

These computational tools must be scalable in several ways. First, they must be computationally scalable. Tools for image analysis must be suitable for quantative analyses on large sets of 3D data, and many current software packages for image processing are not appropriate for this. In order to serve computational needs across NIH, image analysis tools must also scale across application domains and address a variety of clinical and biomedical problems. In the spirit of the National Center, these tools must also scale across institutions and research groups; that is, they must be usable and sustainable outside the context of the Center itself.

I DONT REALLY UNDERSTAND WHAT POINT IS BEING MADE IN THE PARAGRAPH BELOW??? (MARTY)
The NAMIC NCBC addresses these issues through a variety of mechanisms. First it is truly an national center, with researchers from seven (?) institutions across the US that represent expertise in the wide range of disciplines described above. The distributed nature of the project provides technical ald clinical expertise, but it also enforces openness of the resource - the infrastructure must accessible in order for the center to operate effectively. NAMIC also includes a set of industrial partners in the engineering core (Core 2), who provide a set of scalable tools for developing, maintaining, and distributing software. The software is universally managable and maintainable, and it does not belong to any one research group --- it belongs to the community. The tools area also scalable across appliation domains. HUH??? While the initial DBPs focused on neuroscience, the new DBPs include oncology and associated R01s include biomechanics/orthopedics. Users of the software are even more diverse, and even include fields outside of medicine and biology. The strategy of NAMIC is to make sure that the effort scales over time as well. The liscensing agreement which is enforced on all of our development activities does not restrict use, and thus we anticipate (and promote) commerical use of the NAMIC software so that the impact of the project could be realized beyond the lifetime of the center.

===Q2.2 In what ways has the NCBC initiative advanced biomedical computing?===
(Tina)

Answer:http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#4._Impact_and_Value_to_Biocomputing

===Q2.3 Are the NCBCs interfacing appropriately? (recommended by RICC)===
(Will Schroeder)

===Q2.4. What new collaborations have been formed through the NCBC initiative?===
(Jim Miller)
**Answer: http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations
**http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events

*discuss what is new: interactions inside cores and between cores.

===Q2.5. What new training opportunities have the centers provided?===
(Randy Gollub)

**Answer: http://www.na-mic.org/Wiki/index.php/Training:Main

** The entire Slicer tutorial portfolio would not exist without NA-MIC. explain what this portfolio consists of.

===Q2.6. What changes could make the program more effective in the future?===
(Steve Pieper)

* ''National Visibility''

The overall objectives of the NCBC program calls for building computational infrastructure for the nation's biomedical research efforts -- this ambitious goal includes both computational and biomedical science, areas in which the existing centers have extensive experience, but also in nationwide "marketing" efforts where the scientific community is less adept. Several centers, including NA-MIC, have developed novel approaches to this problem through their training and dissemination cores, but as the output of the centers grows along with the number of potential users in the community and the potential impact, these critical resources will be increasingly strained. There are several approaches the overall program could take to address this issue including supplemental funding to host workshops or conferences, providing small travel grants for researchers or students to visit the centers, or actively encouraging a wider range of NIH funded researchers to adopt the tools generated by the NCBCs. In particular, collaboration PAR has been very effective as a mechanism for encouraging busy scientists to consider adopting the NCBC tools and is an excellent example of how the program can extend the impact of the basic investment in scientific infrastructure.

* ''Local Autonomy''

The program should avoid adding extra layers of uniformity to what are fundamentally unique centers. The NCBC program has successfully established a distributed network of centers drawing on the expertise of some of the nation's leading researchers drawn to the program for the opportunity to develop and apply their know-how to this ambitious effort. This rich environment, predictably, yields a diversity of approaches and organizational structures as each of the centers works to implement their particular vision of how to fulfill the overall mission of the NCBC program. Preserving the vitality of the effort depends on retaining this autonomy as each center strives to meet the individual objectives suited to their communities. The program needs well defined goals that each center must meet, but the overall program should facilitate the individual solutions of the center's leadership.

===Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing?===
(Martha Shenton)

There are several lessons that have been learned from the initial phases of this NCBC that might help to guide future NIH efforts in biomedical computing. Briefly, they fall into:
First, in bringing together computer scientists, engineers, and application scientists, the learning curve is initially much greater than many of us would have anticipated, as the different skills sets of such a multidisclinary enterprise requires that the application scientists who are driving the biological problems, must learn to communicate with the computer scientists and engineers. Coming from different disciplines, this is not always easy and it takes some amount of time to define the problems succinctly, concretely, and with sufficient clarity that computer scientists and engineers understand what tasks and needed to assist in solving the challenges posed by the application scientists, and how the computer scientists and engineers might best develop algorithms to address these challenges. If the scientists coming together have not worked with each other before, and are from different scientific disciplines, there is a time period needed to define the problems and to discuss how they might be approached. This can take anywhere from a year to a year and a half.

=='''Q3:A list of publications and/or software tools produced by the Center.''' If this information is provided in your progress report or is available on your website, a link will be sufficient. We are especially interested in your assessment of the maturity of your software tools and the impact they are having on the scientific community.===

(Will Schroeder, Allen Tannenbaum--THE PUBLICATION LIST NEEDS UPDATING. O'Donnell paper, for example was out there a while ago in AJNR--things missing from our group also)

A3:
*NA-MIC Publications are available here: http://www.na-mic.org/Wiki/index.php/Publications.
*NA-MIC Software Tools are available here: http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit
The Center has created and extended a number of software tools to handle some of the key problems in medical imaging analysis and to deliver these computational technologies via a suite of applications, toolkits, and infrastructure facilities. A summary description of these tools includes:
* Slicer3 (application) - a application platform for deploying imaging technologies, newly architected with an execution model facilitating integration, work flow, and large scale computing. While Slicer3 is the newest addition to the NAMIC Kit, it is built on pre-existing, mature toolkits so that the application is relatively mature, and is already in use. Because Slicer3 supports plug-in modules, active development is proceeding to create and package various modules for dissemination to the NAMIC community.
* ITK (toolkit) - a mature system for image analysis, registration and segmentation (initially created in 1999). ITK is in use worldwide for medical imaging research and development.
* VTK (toolkit) - a mature system for visualization, graphics, volume rendering, and interaction (initially created in 1993). VTK is used worldwide for research, teaching and commercial development.
* DART (computational infrastructure) - a key component of the NAMIC quality control process, DART is used to coordinate and report the software testing process. It was created in the first year of NAMIC and is in constant use, therefore a mature system.
* CMake/CTest/CPack (computational infrastructure) - CMake and CTest are relatively mature systems used to manage the building and testing of software across diverse computer platforms. CMake is used worldwide by some of the [http://lwn.net/Articles// world's largest open source systems such as KDE]. CPack, a recent addition to the NAMIC kit, is used to simplify the packaging and dissemination of software across platforms. Thus in NAMIC we can easily deploy our software across Windows, Linux, Unix, and Mac platforms.
* Other tools (computational infrastructure) - Many other software tools are used to support the development of advanced imaging applications, and to assist with large scale computing, including
** Teem - image processing tools (mature)
** KWWidgets - Open source, cross platform GUI toolkit (mature, but development continues to support workflow).
** BatchMake - Support large-scale computing, including grid computing, for performing large population studies and statistical analysis (under active development).

These tools address key problems in imaging including segmentation, registration, visualization, and shape analysis; or provide facilities supporting researchers and developers who wish to create advanced software applications. One of key characteristics of NAMIC is that we treat the development of advanced medical image analysis software holistically; that is, the complete cycle of algorithm design, efficient impmentations, quality control and dissemination are needed to effectively address challenges provided by the driving biological problems. Examples of how these tools are being used include the following:

(a) Segmentation: Here there are a variety of tools of varying degrees of maturity ranging from the EM Segmentor (a widely distributed, mature algorithm included in Slicer3 as an application plug-in) to more recent work on DTI segmentation based on directional flows which are Matlab and C++ based. Powerful mature tools such as Bayesian segmentation have been recently included in Slicer (and have been available in ITK for some time now) which can be combined with very recent work on the semi-automated segmentation of the DPFC done in collaboration with Core 3 researchers. Further, tools previously developed by NAMIC researchers which have had a wide distribution have been put into Slicer. For example, geometric based segmentation methods (some of which were included in packages marketed by GE) were tailored for cortical segmentation, and included in the Slicer, and in fact even improved with the inclusion of statistical based approaches.

(b) Registration: Similar remarks can be made for registration in which we have a spectrum ranging from very mature methods to very recent ones which are still being tested. In particular, mature widely distributed methodologies for rigid registration are now included in ITK, as well as spline-based registration methodologies. These are well-tested methods which have been made accessible to the general imaging community. Newer methodologies such as those based on optimal transport for elastic registration are being included in ITK. NAMIC has also pushed for fast implementations of its algorithms to be used on cheap widely available platforms. Taking a cue from the game industry, some algorithms have been ported to GPUs (graphics cards) which are being employed now as computing devices. This has led to a speed-up of almost two orders of magnitude on some of the registration algorithms being tested.

(c) Shape Analysis: Again a number of methodologies have been developed and implemented with varying levels of maturity. Shape methodologies based on spherical harmonics are quite mature, and are available in pipelines developed by NAMIC researchers and have been distributed to the general community. A newer spherical based wavelet shape analysis package has been put into ITK, which also drives a novel shape-based segmentation procedure. More globally based spherical harmonic ideas have been combined with the multi-resolution spherical wavelet approach as a statistical shape based package for schizophrenia. This general technique may be used for other purposes as well, and is presently being ported to some work being done on the prostate. Work has also been accomplished on particle based approaches to this important problem area with the code put into ITK. Many times we work with a Matlab/C++ initial version of our codes, then move to ITK, and finally to Slicer. However, even at the Matlab/C++ stage, algorithms have been distributed and used in a clinical setting (for example, rule-based brain segmentation approaches).

(d) Diffusion Weighted Image (DWI) Analysis: A number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical analysis of fiber bundles have been contributed to the NAMIC toolkit. Some of these tools have been already integrated into diffusion dedicated ITK package with GUI as part of the NAMIC toolkit (e.g., FiberViewer-UNC) and into the Slicer platform (BWH tractography, clustering). The impact of NAMIC DWI analysis activities is best characterized by most recent journal articles and journal articles in print. The application of the NAMIC FiberViewer tool (UNC) in large clinical studies at UNC and Duke are in print (Taylor et al, Gilmore et al., Cascio et al.). Clinical application of the Slicer DTI package by BWH/MIT is reported in O’Donnell et al. Kuroki N. et al. and Nakamura et al.. The research by MGH is found in two journal publications by Tuch et al.. The description of the methodologies also appeared or will appear in peer reviewed journals (Corouge et al., Fletcher et al., Corouge et al.). New methods in development (Finsler metric GT, volumetric PDE based path analysis Utah, stochastic tractography BWH), and path of interest MGH, are currently tested on Core-3 DBP data, with the goal to give recommendations on which type of solution is appropriate to solve specific clinical analysis questions.

(e) Visualization: Core 2 researchers involved with NAMIC now (e.g., the founders of Kitware) were at the forefront of developing VTK (and of course ITK). Thus here we are considering technologies which are at a commercial level of development, and used at thousands of sites. Algorithms developed at NAMIC have driven new directions for these packages. Newer visualization methods, for example, the conformal flattening procedure have been ported to an ITK filter and is in the NAMIC Sandbox. Quasi-isometric methods for brain flattening from the MGH Free Surfer have become part of the NAMIC enterprise as well. These flattening procedures are very easy to use, and may also be employed for registration. Code for the control of distortion of the area in flattening has been incorporated which give area-preservation with minimal distortion. The techniques may be also used for several other purposes included automatic fly-throughs in endoscopy (incorporated into Slicer), and for texture mappings for general visualization purposes.

==Logistics==
When completed, the information should be sent to:

Gwen Jacobs, PhD

Professor of Neuroscience

Asst. CIO and Director of Academic Computing

1 Lewis Hall

Montana State University

Bozeman, MT 59717

406-994-7334 - phone

406-994-7077 - FAX

gwen@cns.montana.edu <mailto:gwen@cns.montana.edu>

2007 Materials for NCBC Program Review

2007-06-06T18:37:58Z

Shenton: /* '''Q3:A list of publications and/or software tools produced by the Center.''' If this information is provided in your progress report or is available on your website, a link will be sufficient. We

==Materials requested for NCBC Program Review==

These are due to Gwen Jacobs by Friday, June 08, 2007.

=='''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''===
(Tina/some edits from Marty --Tina--you had schizophrenia listed as a NEW DBP, I put in VCFS--if this was in the annual report, it needs to be fixed. Also, under highlights--- I see what Core 1 was able to do because of data provided by core 3, but there are no highlights of what core 3 did with what Core 1 gave them --- don't you think this should be fixed? That is, there are NO HIGHLIGHTS listed for CORE 3 --)

Summary: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#1._Introduction

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with respect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs include a focus on Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

Highlights: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#3._Highlights

The third year of the NA-MIC project saw continued development and dissemination of medical image analysis software. The current progress is clearly characterized by a significant increase in the application of Core-1 and Core-2 tools to image data provided by the Core-3 DBP groups. The main reasons for this progress is two-fold, first there are several new methods that are out of the prototype stage and ready to be applied to large sets of imaging datasets, and second there are new high-resolution imaging data from high-field scanners that are more appropriate for the new tools than historical data with often very coarse slice resolution.

With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NA-MIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NA-MIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

*Advanced Algorithms
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NA-MIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NA-MIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The NA-MIC toolkit includes a comprehensive set of modules for analysis of diffusion weighted images (DWI), including improved calculation of tensors, interpolation, nonlinear deformation and statistics on tensor fields, novel methods for tractography and for optimal path finding, and clustering of sets of streamlines.
* A quantitative tractography package for user-guided geometric parametrization and statistical analysis of fiber bundles (FiberViewer) has been contributed to the NAMIC toolbox. This tool used in several ongoing clinical DTI studies.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NA-MIC Sandbox in preparation for formal acceptance into the NA-MIC Kit.
*Technology Deployment Platform: Slicer3
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NA-MIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NA-MIC Kit, and are now available to all other modules built on the Slicer3 framework.

*Outreach and Technology Transfer
Cores 4-5-6 continue to support, train and disseminate to the NA-MIC community, and the broader biomedical computing community.
* NA-MIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NA-MIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair. The training and dissemination will continue with a DTI workshop at the forthcoming Human Brain Mapping meeting and activities at MICCAI 2007, among others.

==Q2==
'''A brief statement - (one page per question, max) addressing each of the questions listed below. These are the questions that we have been asked to address in our report. Our goal in asking for this information is to be able to produce a report that reviews the program as a whole. Your view, from the vantage point of the center you direct, is critical to our work. In addition, your answers will provide us with more information that we can use in our discussion with program staff on June 11th. We know that some of this information can be found on your websites, so in those cases a link to the information would be most helpful.'''

===Q2.1 To what extent does the vision and direction of the NCBC initiative promote biomedical computing?===
(Eric/Polina)

(From Ross/edits Marty--THIS SECTION I THINK NEEDS MORE WORK AS I DON"T UNDERSTAND HOW IT ANSWERS THE QUESTION BEING ASKED) The fields of biological and medical imaging are exploding. Moreover, the combination of new acquisition and reconstruction techniques, new computing resources, and diverse biological and clinical applications have resulted in a massive proliferation of imaging data. This imaging data has the potential for having an important impact on basic biological science, in the development of new drugs and medical technologies, and in direct clinical practice - in both diagnosis and treatment. Thus, biomedical imaging analysis is one of the most important applications of biomedical computing.

To realize this potential will require new computational tools for image analysis. These tools will rely on a diverse set of technical knowledge including physics, systems, computer science, mathmatics, and statistics. The development of these tools will also incorporate in-depth knowledge of clinical and biological applications in diverse areas such as neuroscience, psychiatry, encology, cardiology, and biochemistry.

These computational tools must be scalable in several ways. First, they must be computationally scalable. Tools for image analysis must be suitable for quantative analyses on large sets of 3D data, and many current software packages for image processing are not appropriate for this. In order to serve computational needs across NIH, image analysis tools must also scale across application domains and address a variety of clinical and biomedical problems. In the spirit of the National Center, these tools must also scale across institutions and research groups; that is, they must be usable and sustainable outside the context of the Center itself.

I DONT REALLY UNDERSTAND WHAT POINT IS BEING MADE IN THE PARAGRAPH BELOW??? (MARTY)
The NAMIC NCBC addresses these issues through a variety of mechanisms. First it is truly an national center, with researchers from seven (?) institutions across the US that represent expertise in the wide range of disciplines described above. The distributed nature of the project provides technical ald clinical expertise, but it also enforces openness of the resource - the infrastructure must accessible in order for the center to operate effectively. NAMIC also includes a set of industrial partners in the engineering core (Core 2), who provide a set of scalable tools for developing, maintaining, and distributing software. The software is universally managable and maintainable, and it does not belong to any one research group --- it belongs to the community. The tools area also scalable across appliation domains. HUH??? While the initial DBPs focused on neuroscience, the new DBPs include oncology and associated R01s include biomechanics/orthopedics. Users of the software are even more diverse, and even include fields outside of medicine and biology. The strategy of NAMIC is to make sure that the effort scales over time as well. The liscensing agreement which is enforced on all of our development activities does not restrict use, and thus we anticipate (and promote) commerical use of the NAMIC software so that the impact of the project could be realized beyond the lifetime of the center.

===Q2.2 In what ways has the NCBC initiative advanced biomedical computing?===
(Tina)

Answer:http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#4._Impact_and_Value_to_Biocomputing

===Q2.3 Are the NCBCs interfacing appropriately? (recommended by RICC)===
(Will Schroeder)

===Q2.4. What new collaborations have been formed through the NCBC initiative?===
(Jim Miller)
**Answer: http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations
**http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events

*discuss what is new: interactions inside cores and between cores.

===Q2.5. What new training opportunities have the centers provided?===
(Randy Gollub)

**Answer: http://www.na-mic.org/Wiki/index.php/Training:Main

** The entire Slicer tutorial portfolio would not exist without NA-MIC. explain what this portfolio consists of.

===Q2.6. What changes could make the program more effective in the future?===
(Steve Pieper)

===Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing?===
(Martha Shenton)

=='''Q3:A list of publications and/or software tools produced by the Center.''' If this information is provided in your progress report or is available on your website, a link will be sufficient. We are especially interested in your assessment of the maturity of your software tools and the impact they are having on the scientific community.===

(Will Schroeder, Allen Tannenbaum--THE PUBLICATION LIST NEEDS UPDATING. O'Donnell paper, for example was out there a while ago in AJNR--things missing from our group also)

A3:
*NA-MIC Publications are available here: http://www.na-mic.org/Wiki/index.php/Publications.
*NA-MIC Software Tools are available here: http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit
The Center has created and extended a number of software tools to handle some of the key problems in medical imaging analysis and to deliver these computational technologies via a suite of applications, toolkits, and infrastructure facilities. A summary description of these tools includes:
* Slicer3 (application) - a application platform for deploying imaging technologies, newly architected with an execution model facilitating integration, work flow, and large scale computing. While Slicer3 is the newest addition to the NAMIC Kit, it is built on pre-existing, mature toolkits so that the application is relatively mature, and is already in use. Because Slicer3 supports plug-in modules, active development is proceeding to create and package various modules for dissemination to the NAMIC community.
* ITK (toolkit) - a mature system for image analysis, registration and segmentation (initially created in 1999). ITK is in use worldwide for medical imaging research and development.
* VTK (toolkit) - a mature system for visualization, graphics, volume rendering, and interaction (initially created in 1993). VTK is used worldwide for research, teaching and commercial development.
* DART (computational infrastructure) - a key component of the NAMIC quality control process, DART is used to coordinate and report the software testing process. It was created in the first year of NAMIC and is in constant use, therefore a mature system.
* CMake/CTest/CPack (computational infrastructure) - CMake and CTest are relatively mature systems used to manage the building and testing of software across diverse computer platforms. CMake is used worldwide by some of the [http://lwn.net/Articles// world's largest open source systems such as KDE]. CPack, a recent addition to the NAMIC kit, is used to simplify the packaging and dissemination of software across platforms. Thus in NAMIC we can easily deploy our software across Windows, Linux, Unix, and Mac platforms.
* Other tools (computational infrastructure) - Many other software tools are used to support the development of advanced imaging applications, and to assist with large scale computing, including
** Teem - image processing tools (mature)
** KWWidgets - Open source, cross platform GUI toolkit (mature, but development continues to support workflow).
** BatchMake - Support large-scale computing, including grid computing, for performing large population studies and statistical analysis (under active development).

These tools address key problems in imaging including segmentation, registration, visualization, and shape analysis; or provide facilities supporting researchers and developers who wish to create advanced software applications. One of key characteristics of NAMIC is that we treat the development of advanced medical image analysis software holistically; that is, the complete cycle of algorithm design, efficient impmentations, quality control and dissemination are needed to effectively address challenges provided by the driving biological problems. Examples of how these tools are being used include the following:

(a) Segmentation: Here there are a variety of tools of varying degrees of maturity ranging from the EM Segmentor (a widely distributed, mature algorithm included in Slicer3 as an application plug-in) to more recent work on DTI segmentation based on directional flows which are Matlab and C++ based. Powerful mature tools such as Bayesian segmentation have been recently included in Slicer (and have been available in ITK for some time now) which can be combined with very recent work on the semi-automated segmentation of the DPFC done in collaboration with Core 3 researchers. Further, tools previously developed by NAMIC researchers which have had a wide distribution have been put into Slicer. For example, geometric based segmentation methods (some of which were included in packages marketed by GE) were tailored for cortical segmentation, and included in the Slicer, and in fact even improved with the inclusion of statistical based approaches.

(b) Registration: Similar remarks can be made for registration in which we have a spectrum ranging from very mature methods to very recent ones which are still being tested. In particular, mature widely distributed methodologies for rigid registration are now included in ITK, as well as spline-based registration methodologies. These are well-tested methods which have been made accessible to the general imaging community. Newer methodologies such as those based on optimal transport for elastic registration are being included in ITK. NAMIC has also pushed for fast implementations of its algorithms to be used on cheap widely available platforms. Taking a cue from the game industry, some algorithms have been ported to GPUs (graphics cards) which are being employed now as computing devices. This has led to a speed-up of almost two orders of magnitude on some of the registration algorithms being tested.

(c) Shape Analysis: Again a number of methodologies have been developed and implemented with varying levels of maturity. Shape methodologies based on spherical harmonics are quite mature, and are available in pipelines developed by NAMIC researchers and have been distributed to the general community. A newer spherical based wavelet shape analysis package has been put into ITK, which also drives a novel shape-based segmentation procedure. More globally based spherical harmonic ideas have been combined with the multi-resolution spherical wavelet approach as a statistical shape based package for schizophrenia. This general technique may be used for other purposes as well, and is presently being ported to some work being done on the prostate. Work has also been accomplished on particle based approaches to this important problem area with the code put into ITK. Many times we work with a Matlab/C++ initial version of our codes, then move to ITK, and finally to Slicer. However, even at the Matlab/C++ stage, algorithms have been distributed and used in a clinical setting (for example, rule-based brain segmentation approaches).

(d) Diffusion Weighted Image (DWI) Analysis: A number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical analysis of fiber bundles have been contributed to the NAMIC toolkit. Some of these tools have been already integrated into diffusion dedicated ITK package with GUI as part of the NAMIC toolkit (e.g., FiberViewer-UNC) and into the Slicer platform (BWH tractography, clustering). The impact of NAMIC DWI analysis activities is best characterized by most recent journal articles and journal articles in print. The application of the NAMIC FiberViewer tool (UNC) in large clinical studies at UNC and Duke are in print (Taylor et al, Gilmore et al., Cascio et al.). Clinical application of the Slicer DTI package by BWH/MIT is reported in O’Donnell et al. Kuroki N. et al. and Nakamura et al.. The research by MGH is found in two journal publications by Tuch et al.. The description of the methodologies also appeared or will appear in peer reviewed journals (Corouge et al., Fletcher et al., Corouge et al.). New methods in development (Finsler metric GT, volumetric PDE based path analysis Utah, stochastic tractography BWH), and path of interest MGH, are currently tested on Core-3 DBP data, with the goal to give recommendations on which type of solution is appropriate to solve specific clinical analysis questions.

(e) Visualization: Core 2 researchers involved with NAMIC now (e.g., the founders of Kitware) were at the forefront of developing VTK (and of course ITK). Thus here we are considering technologies which are at a commercial level of development, and used at thousands of sites. Algorithms developed at NAMIC have driven new directions for these packages. Newer visualization methods, for example, the conformal flattening procedure have been ported to an ITK filter and is in the NAMIC Sandbox. Quasi-isometric methods for brain flattening from the MGH Free Surfer have become part of the NAMIC enterprise as well. These flattening procedures are very easy to use, and may also be employed for registration. Code for the control of distortion of the area in flattening has been incorporated which give area-preservation with minimal distortion. The techniques may be also used for several other purposes included automatic fly-throughs in endoscopy (incorporated into Slicer), and for texture mappings for general visualization purposes.

==Logistics==
When completed, the information should be sent to:

Gwen Jacobs, PhD

Professor of Neuroscience

Asst. CIO and Director of Academic Computing

1 Lewis Hall

Montana State University

Bozeman, MT 59717

406-994-7334 - phone

406-994-7077 - FAX

gwen@cns.montana.edu <mailto:gwen@cns.montana.edu>

2007 Materials for NCBC Program Review

2007-06-06T18:36:13Z

Shenton: /* '''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''= */

==Materials requested for NCBC Program Review==

These are due to Gwen Jacobs by Friday, June 08, 2007.

=='''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''===
(Tina/some edits from Marty --Tina--you had schizophrenia listed as a NEW DBP, I put in VCFS--if this was in the annual report, it needs to be fixed. Also, under highlights--- I see what Core 1 was able to do because of data provided by core 3, but there are no highlights of what core 3 did with what Core 1 gave them --- don't you think this should be fixed? That is, there are NO HIGHLIGHTS listed for CORE 3 --)

Summary: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#1._Introduction

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with respect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs include a focus on Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

Highlights: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#3._Highlights

The third year of the NA-MIC project saw continued development and dissemination of medical image analysis software. The current progress is clearly characterized by a significant increase in the application of Core-1 and Core-2 tools to image data provided by the Core-3 DBP groups. The main reasons for this progress is two-fold, first there are several new methods that are out of the prototype stage and ready to be applied to large sets of imaging datasets, and second there are new high-resolution imaging data from high-field scanners that are more appropriate for the new tools than historical data with often very coarse slice resolution.

With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NA-MIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NA-MIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

*Advanced Algorithms
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NA-MIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NA-MIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The NA-MIC toolkit includes a comprehensive set of modules for analysis of diffusion weighted images (DWI), including improved calculation of tensors, interpolation, nonlinear deformation and statistics on tensor fields, novel methods for tractography and for optimal path finding, and clustering of sets of streamlines.
* A quantitative tractography package for user-guided geometric parametrization and statistical analysis of fiber bundles (FiberViewer) has been contributed to the NAMIC toolbox. This tool used in several ongoing clinical DTI studies.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NA-MIC Sandbox in preparation for formal acceptance into the NA-MIC Kit.
*Technology Deployment Platform: Slicer3
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NA-MIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NA-MIC Kit, and are now available to all other modules built on the Slicer3 framework.

*Outreach and Technology Transfer
Cores 4-5-6 continue to support, train and disseminate to the NA-MIC community, and the broader biomedical computing community.
* NA-MIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NA-MIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair. The training and dissemination will continue with a DTI workshop at the forthcoming Human Brain Mapping meeting and activities at MICCAI 2007, among others.

==Q2==
'''A brief statement - (one page per question, max) addressing each of the questions listed below. These are the questions that we have been asked to address in our report. Our goal in asking for this information is to be able to produce a report that reviews the program as a whole. Your view, from the vantage point of the center you direct, is critical to our work. In addition, your answers will provide us with more information that we can use in our discussion with program staff on June 11th. We know that some of this information can be found on your websites, so in those cases a link to the information would be most helpful.'''

===Q2.1 To what extent does the vision and direction of the NCBC initiative promote biomedical computing?===
(Eric/Polina)

(From Ross/edits Marty--THIS SECTION I THINK NEEDS MORE WORK AS I DON"T UNDERSTAND HOW IT ANSWERS THE QUESTION BEING ASKED) The fields of biological and medical imaging are exploding. Moreover, the combination of new acquisition and reconstruction techniques, new computing resources, and diverse biological and clinical applications have resulted in a massive proliferation of imaging data. This imaging data has the potential for having an important impact on basic biological science, in the development of new drugs and medical technologies, and in direct clinical practice - in both diagnosis and treatment. Thus, biomedical imaging analysis is one of the most important applications of biomedical computing.

To realize this potential will require new computational tools for image analysis. These tools will rely on a diverse set of technical knowledge including physics, systems, computer science, mathmatics, and statistics. The development of these tools will also incorporate in-depth knowledge of clinical and biological applications in diverse areas such as neuroscience, psychiatry, encology, cardiology, and biochemistry.

These computational tools must be scalable in several ways. First, they must be computationally scalable. Tools for image analysis must be suitable for quantative analyses on large sets of 3D data, and many current software packages for image processing are not appropriate for this. In order to serve computational needs across NIH, image analysis tools must also scale across application domains and address a variety of clinical and biomedical problems. In the spirit of the National Center, these tools must also scale across institutions and research groups; that is, they must be usable and sustainable outside the context of the Center itself.

I DONT REALLY UNDERSTAND WHAT POINT IS BEING MADE IN THE PARAGRAPH BELOW??? (MARTY)
The NAMIC NCBC addresses these issues through a variety of mechanisms. First it is truly an national center, with researchers from seven (?) institutions across the US that represent expertise in the wide range of disciplines described above. The distributed nature of the project provides technical ald clinical expertise, but it also enforces openness of the resource - the infrastructure must accessible in order for the center to operate effectively. NAMIC also includes a set of industrial partners in the engineering core (Core 2), who provide a set of scalable tools for developing, maintaining, and distributing software. The software is universally managable and maintainable, and it does not belong to any one research group --- it belongs to the community. The tools area also scalable across appliation domains. HUH??? While the initial DBPs focused on neuroscience, the new DBPs include oncology and associated R01s include biomechanics/orthopedics. Users of the software are even more diverse, and even include fields outside of medicine and biology. The strategy of NAMIC is to make sure that the effort scales over time as well. The liscensing agreement which is enforced on all of our development activities does not restrict use, and thus we anticipate (and promote) commerical use of the NAMIC software so that the impact of the project could be realized beyond the lifetime of the center.

===Q2.2 In what ways has the NCBC initiative advanced biomedical computing?===
(Tina)

Answer:http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#4._Impact_and_Value_to_Biocomputing

===Q2.3 Are the NCBCs interfacing appropriately? (recommended by RICC)===
(Will Schroeder)

===Q2.4. What new collaborations have been formed through the NCBC initiative?===
(Jim Miller)
**Answer: http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations
**http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events

*discuss what is new: interactions inside cores and between cores.

===Q2.5. What new training opportunities have the centers provided?===
(Randy Gollub)

**Answer: http://www.na-mic.org/Wiki/index.php/Training:Main

** The entire Slicer tutorial portfolio would not exist without NA-MIC. explain what this portfolio consists of.

===Q2.6. What changes could make the program more effective in the future?===
(Steve Pieper)

===Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing?===
(Martha Shenton)

=='''Q3:A list of publications and/or software tools produced by the Center.''' If this information is provided in your progress report or is available on your website, a link will be sufficient. We are especially interested in your assessment of the maturity of your software tools and the impact they are having on the scientific community.===

(Will Schroeder, Allen Tannenbaum)

A3:
*NA-MIC Publications are available here: http://www.na-mic.org/Wiki/index.php/Publications.
*NA-MIC Software Tools are available here: http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit
The Center has created and extended a number of software tools to handle some of the key problems in medical imaging analysis and to deliver these computational technologies via a suite of applications, toolkits, and infrastructure facilities. A summary description of these tools includes:
* Slicer3 (application) - a application platform for deploying imaging technologies, newly architected with an execution model facilitating integration, work flow, and large scale computing. While Slicer3 is the newest addition to the NAMIC Kit, it is built on pre-existing, mature toolkits so that the application is relatively mature, and is already in use. Because Slicer3 supports plug-in modules, active development is proceeding to create and package various modules for dissemination to the NAMIC community.
* ITK (toolkit) - a mature system for image analysis, registration and segmentation (initially created in 1999). ITK is in use worldwide for medical imaging research and development.
* VTK (toolkit) - a mature system for visualization, graphics, volume rendering, and interaction (initially created in 1993). VTK is used worldwide for research, teaching and commercial development.
* DART (computational infrastructure) - a key component of the NAMIC quality control process, DART is used to coordinate and report the software testing process. It was created in the first year of NAMIC and is in constant use, therefore a mature system.
* CMake/CTest/CPack (computational infrastructure) - CMake and CTest are relatively mature systems used to manage the building and testing of software across diverse computer platforms. CMake is used worldwide by some of the [http://lwn.net/Articles// world's largest open source systems such as KDE]. CPack, a recent addition to the NAMIC kit, is used to simplify the packaging and dissemination of software across platforms. Thus in NAMIC we can easily deploy our software across Windows, Linux, Unix, and Mac platforms.
* Other tools (computational infrastructure) - Many other software tools are used to support the development of advanced imaging applications, and to assist with large scale computing, including
** Teem - image processing tools (mature)
** KWWidgets - Open source, cross platform GUI toolkit (mature, but development continues to support workflow).
** BatchMake - Support large-scale computing, including grid computing, for performing large population studies and statistical analysis (under active development).

These tools address key problems in imaging including segmentation, registration, visualization, and shape analysis; or provide facilities supporting researchers and developers who wish to create advanced software applications. One of key characteristics of NAMIC is that we treat the development of advanced medical image analysis software holistically; that is, the complete cycle of algorithm design, efficient impmentations, quality control and dissemination are needed to effectively address challenges provided by the driving biological problems. Examples of how these tools are being used include the following:

(a) Segmentation: Here there are a variety of tools of varying degrees of maturity ranging from the EM Segmentor (a widely distributed, mature algorithm included in Slicer3 as an application plug-in) to more recent work on DTI segmentation based on directional flows which are Matlab and C++ based. Powerful mature tools such as Bayesian segmentation have been recently included in Slicer (and have been available in ITK for some time now) which can be combined with very recent work on the semi-automated segmentation of the DPFC done in collaboration with Core 3 researchers. Further, tools previously developed by NAMIC researchers which have had a wide distribution have been put into Slicer. For example, geometric based segmentation methods (some of which were included in packages marketed by GE) were tailored for cortical segmentation, and included in the Slicer, and in fact even improved with the inclusion of statistical based approaches.

(b) Registration: Similar remarks can be made for registration in which we have a spectrum ranging from very mature methods to very recent ones which are still being tested. In particular, mature widely distributed methodologies for rigid registration are now included in ITK, as well as spline-based registration methodologies. These are well-tested methods which have been made accessible to the general imaging community. Newer methodologies such as those based on optimal transport for elastic registration are being included in ITK. NAMIC has also pushed for fast implementations of its algorithms to be used on cheap widely available platforms. Taking a cue from the game industry, some algorithms have been ported to GPUs (graphics cards) which are being employed now as computing devices. This has led to a speed-up of almost two orders of magnitude on some of the registration algorithms being tested.

(c) Shape Analysis: Again a number of methodologies have been developed and implemented with varying levels of maturity. Shape methodologies based on spherical harmonics are quite mature, and are available in pipelines developed by NAMIC researchers and have been distributed to the general community. A newer spherical based wavelet shape analysis package has been put into ITK, which also drives a novel shape-based segmentation procedure. More globally based spherical harmonic ideas have been combined with the multi-resolution spherical wavelet approach as a statistical shape based package for schizophrenia. This general technique may be used for other purposes as well, and is presently being ported to some work being done on the prostate. Work has also been accomplished on particle based approaches to this important problem area with the code put into ITK. Many times we work with a Matlab/C++ initial version of our codes, then move to ITK, and finally to Slicer. However, even at the Matlab/C++ stage, algorithms have been distributed and used in a clinical setting (for example, rule-based brain segmentation approaches).

(d) Diffusion Weighted Image (DWI) Analysis: A number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical analysis of fiber bundles have been contributed to the NAMIC toolkit. Some of these tools have been already integrated into diffusion dedicated ITK package with GUI as part of the NAMIC toolkit (e.g., FiberViewer-UNC) and into the Slicer platform (BWH tractography, clustering). The impact of NAMIC DWI analysis activities is best characterized by most recent journal articles and journal articles in print. The application of the NAMIC FiberViewer tool (UNC) in large clinical studies at UNC and Duke are in print (Taylor et al, Gilmore et al., Cascio et al.). Clinical application of the Slicer DTI package by BWH/MIT is reported in O’Donnell et al. Kuroki N. et al. and Nakamura et al.. The research by MGH is found in two journal publications by Tuch et al.. The description of the methodologies also appeared or will appear in peer reviewed journals (Corouge et al., Fletcher et al., Corouge et al.). New methods in development (Finsler metric GT, volumetric PDE based path analysis Utah, stochastic tractography BWH), and path of interest MGH, are currently tested on Core-3 DBP data, with the goal to give recommendations on which type of solution is appropriate to solve specific clinical analysis questions.

(e) Visualization: Core 2 researchers involved with NAMIC now (e.g., the founders of Kitware) were at the forefront of developing VTK (and of course ITK). Thus here we are considering technologies which are at a commercial level of development, and used at thousands of sites. Algorithms developed at NAMIC have driven new directions for these packages. Newer visualization methods, for example, the conformal flattening procedure have been ported to an ITK filter and is in the NAMIC Sandbox. Quasi-isometric methods for brain flattening from the MGH Free Surfer have become part of the NAMIC enterprise as well. These flattening procedures are very easy to use, and may also be employed for registration. Code for the control of distortion of the area in flattening has been incorporated which give area-preservation with minimal distortion. The techniques may be also used for several other purposes included automatic fly-throughs in endoscopy (incorporated into Slicer), and for texture mappings for general visualization purposes.

==Logistics==
When completed, the information should be sent to:

Gwen Jacobs, PhD

Professor of Neuroscience

Asst. CIO and Director of Academic Computing

1 Lewis Hall

Montana State University

Bozeman, MT 59717

406-994-7334 - phone

406-994-7077 - FAX

gwen@cns.montana.edu <mailto:gwen@cns.montana.edu>

2007 Materials for NCBC Program Review

2007-06-06T18:35:12Z

Shenton: /* '''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''= */

==Materials requested for NCBC Program Review==

These are due to Gwen Jacobs by Friday, June 08, 2007.

=='''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''===
(Tina/some edits from Marty --Tina--you had schizophrenia listed as a NEW DBP, I put in VCFS--if this was in the annual report, it needs to be fixed. Also, under highlights--- I see what Core 1 was able to do because of data provided by core 3, but there are no highlights of what core 3 did with what Core 1 gave them --- don't you think this should be fixed?)

Summary: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#1._Introduction

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with respect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs include a focus on Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

Highlights: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#3._Highlights

The third year of the NA-MIC project saw continued development and dissemination of medical image analysis software. The current progress is clearly characterized by a significant increase in the application of Core-1 and Core-2 tools to image data provided by the Core-3 DBP groups. The main reasons for this progress is two-fold, first there are several new methods that are out of the prototype stage and ready to be applied to large sets of imaging datasets, and second there are new high-resolution imaging data from high-field scanners that are more appropriate for the new tools than historical data with often very coarse slice resolution.

With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NA-MIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NA-MIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

*Advanced Algorithms
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NA-MIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NA-MIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The NA-MIC toolkit includes a comprehensive set of modules for analysis of diffusion weighted images (DWI), including improved calculation of tensors, interpolation, nonlinear deformation and statistics on tensor fields, novel methods for tractography and for optimal path finding, and clustering of sets of streamlines.
* A quantitative tractography package for user-guided geometric parametrization and statistical analysis of fiber bundles (FiberViewer) has been contributed to the NAMIC toolbox. This tool used in several ongoing clinical DTI studies.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NA-MIC Sandbox in preparation for formal acceptance into the NA-MIC Kit.
*Technology Deployment Platform: Slicer3
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NA-MIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NA-MIC Kit, and are now available to all other modules built on the Slicer3 framework.

*Outreach and Technology Transfer
Cores 4-5-6 continue to support, train and disseminate to the NA-MIC community, and the broader biomedical computing community.
* NA-MIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NA-MIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair. The training and dissemination will continue with a DTI workshop at the forthcoming Human Brain Mapping meeting and activities at MICCAI 2007, among others.

==Q2==
'''A brief statement - (one page per question, max) addressing each of the questions listed below. These are the questions that we have been asked to address in our report. Our goal in asking for this information is to be able to produce a report that reviews the program as a whole. Your view, from the vantage point of the center you direct, is critical to our work. In addition, your answers will provide us with more information that we can use in our discussion with program staff on June 11th. We know that some of this information can be found on your websites, so in those cases a link to the information would be most helpful.'''

===Q2.1 To what extent does the vision and direction of the NCBC initiative promote biomedical computing?===
(Eric/Polina)

(From Ross/edits Marty--THIS SECTION I THINK NEEDS MORE WORK AS I DON"T UNDERSTAND HOW IT ANSWERS THE QUESTION BEING ASKED) The fields of biological and medical imaging are exploding. Moreover, the combination of new acquisition and reconstruction techniques, new computing resources, and diverse biological and clinical applications have resulted in a massive proliferation of imaging data. This imaging data has the potential for having an important impact on basic biological science, in the development of new drugs and medical technologies, and in direct clinical practice - in both diagnosis and treatment. Thus, biomedical imaging analysis is one of the most important applications of biomedical computing.

To realize this potential will require new computational tools for image analysis. These tools will rely on a diverse set of technical knowledge including physics, systems, computer science, mathmatics, and statistics. The development of these tools will also incorporate in-depth knowledge of clinical and biological applications in diverse areas such as neuroscience, psychiatry, encology, cardiology, and biochemistry.

These computational tools must be scalable in several ways. First, they must be computationally scalable. Tools for image analysis must be suitable for quantative analyses on large sets of 3D data, and many current software packages for image processing are not appropriate for this. In order to serve computational needs across NIH, image analysis tools must also scale across application domains and address a variety of clinical and biomedical problems. In the spirit of the National Center, these tools must also scale across institutions and research groups; that is, they must be usable and sustainable outside the context of the Center itself.

I DONT REALLY UNDERSTAND WHAT POINT IS BEING MADE IN THE PARAGRAPH BELOW??? (MARTY)
The NAMIC NCBC addresses these issues through a variety of mechanisms. First it is truly an national center, with researchers from seven (?) institutions across the US that represent expertise in the wide range of disciplines described above. The distributed nature of the project provides technical ald clinical expertise, but it also enforces openness of the resource - the infrastructure must accessible in order for the center to operate effectively. NAMIC also includes a set of industrial partners in the engineering core (Core 2), who provide a set of scalable tools for developing, maintaining, and distributing software. The software is universally managable and maintainable, and it does not belong to any one research group --- it belongs to the community. The tools area also scalable across appliation domains. HUH??? While the initial DBPs focused on neuroscience, the new DBPs include oncology and associated R01s include biomechanics/orthopedics. Users of the software are even more diverse, and even include fields outside of medicine and biology. The strategy of NAMIC is to make sure that the effort scales over time as well. The liscensing agreement which is enforced on all of our development activities does not restrict use, and thus we anticipate (and promote) commerical use of the NAMIC software so that the impact of the project could be realized beyond the lifetime of the center.

===Q2.2 In what ways has the NCBC initiative advanced biomedical computing?===
(Tina)

Answer:http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report#4._Impact_and_Value_to_Biocomputing

===Q2.3 Are the NCBCs interfacing appropriately? (recommended by RICC)===
(Will Schroeder)

===Q2.4. What new collaborations have been formed through the NCBC initiative?===
(Jim Miller)
**Answer: http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations
**http://www.na-mic.org/Wiki/index.php/Engineering:Programming_Events

*discuss what is new: interactions inside cores and between cores.

===Q2.5. What new training opportunities have the centers provided?===
(Randy Gollub)

**Answer: http://www.na-mic.org/Wiki/index.php/Training:Main

** The entire Slicer tutorial portfolio would not exist without NA-MIC. explain what this portfolio consists of.

===Q2.6. What changes could make the program more effective in the future?===
(Steve Pieper)

===Q2.7. What lessons have been learned from the NCBC initiative that can guide future NIH efforts in biomedical computing?===
(Martha Shenton)

=='''Q3:A list of publications and/or software tools produced by the Center.''' If this information is provided in your progress report or is available on your website, a link will be sufficient. We are especially interested in your assessment of the maturity of your software tools and the impact they are having on the scientific community.===

(Will Schroeder, Allen Tannenbaum)

A3:
*NA-MIC Publications are available here: http://www.na-mic.org/Wiki/index.php/Publications.
*NA-MIC Software Tools are available here: http://www.na-mic.org/Wiki/index.php/NA-MIC-Kit
The Center has created and extended a number of software tools to handle some of the key problems in medical imaging analysis and to deliver these computational technologies via a suite of applications, toolkits, and infrastructure facilities. A summary description of these tools includes:
* Slicer3 (application) - a application platform for deploying imaging technologies, newly architected with an execution model facilitating integration, work flow, and large scale computing. While Slicer3 is the newest addition to the NAMIC Kit, it is built on pre-existing, mature toolkits so that the application is relatively mature, and is already in use. Because Slicer3 supports plug-in modules, active development is proceeding to create and package various modules for dissemination to the NAMIC community.
* ITK (toolkit) - a mature system for image analysis, registration and segmentation (initially created in 1999). ITK is in use worldwide for medical imaging research and development.
* VTK (toolkit) - a mature system for visualization, graphics, volume rendering, and interaction (initially created in 1993). VTK is used worldwide for research, teaching and commercial development.
* DART (computational infrastructure) - a key component of the NAMIC quality control process, DART is used to coordinate and report the software testing process. It was created in the first year of NAMIC and is in constant use, therefore a mature system.
* CMake/CTest/CPack (computational infrastructure) - CMake and CTest are relatively mature systems used to manage the building and testing of software across diverse computer platforms. CMake is used worldwide by some of the [http://lwn.net/Articles// world's largest open source systems such as KDE]. CPack, a recent addition to the NAMIC kit, is used to simplify the packaging and dissemination of software across platforms. Thus in NAMIC we can easily deploy our software across Windows, Linux, Unix, and Mac platforms.
* Other tools (computational infrastructure) - Many other software tools are used to support the development of advanced imaging applications, and to assist with large scale computing, including
** Teem - image processing tools (mature)
** KWWidgets - Open source, cross platform GUI toolkit (mature, but development continues to support workflow).
** BatchMake - Support large-scale computing, including grid computing, for performing large population studies and statistical analysis (under active development).

These tools address key problems in imaging including segmentation, registration, visualization, and shape analysis; or provide facilities supporting researchers and developers who wish to create advanced software applications. One of key characteristics of NAMIC is that we treat the development of advanced medical image analysis software holistically; that is, the complete cycle of algorithm design, efficient impmentations, quality control and dissemination are needed to effectively address challenges provided by the driving biological problems. Examples of how these tools are being used include the following:

(a) Segmentation: Here there are a variety of tools of varying degrees of maturity ranging from the EM Segmentor (a widely distributed, mature algorithm included in Slicer3 as an application plug-in) to more recent work on DTI segmentation based on directional flows which are Matlab and C++ based. Powerful mature tools such as Bayesian segmentation have been recently included in Slicer (and have been available in ITK for some time now) which can be combined with very recent work on the semi-automated segmentation of the DPFC done in collaboration with Core 3 researchers. Further, tools previously developed by NAMIC researchers which have had a wide distribution have been put into Slicer. For example, geometric based segmentation methods (some of which were included in packages marketed by GE) were tailored for cortical segmentation, and included in the Slicer, and in fact even improved with the inclusion of statistical based approaches.

(b) Registration: Similar remarks can be made for registration in which we have a spectrum ranging from very mature methods to very recent ones which are still being tested. In particular, mature widely distributed methodologies for rigid registration are now included in ITK, as well as spline-based registration methodologies. These are well-tested methods which have been made accessible to the general imaging community. Newer methodologies such as those based on optimal transport for elastic registration are being included in ITK. NAMIC has also pushed for fast implementations of its algorithms to be used on cheap widely available platforms. Taking a cue from the game industry, some algorithms have been ported to GPUs (graphics cards) which are being employed now as computing devices. This has led to a speed-up of almost two orders of magnitude on some of the registration algorithms being tested.

(c) Shape Analysis: Again a number of methodologies have been developed and implemented with varying levels of maturity. Shape methodologies based on spherical harmonics are quite mature, and are available in pipelines developed by NAMIC researchers and have been distributed to the general community. A newer spherical based wavelet shape analysis package has been put into ITK, which also drives a novel shape-based segmentation procedure. More globally based spherical harmonic ideas have been combined with the multi-resolution spherical wavelet approach as a statistical shape based package for schizophrenia. This general technique may be used for other purposes as well, and is presently being ported to some work being done on the prostate. Work has also been accomplished on particle based approaches to this important problem area with the code put into ITK. Many times we work with a Matlab/C++ initial version of our codes, then move to ITK, and finally to Slicer. However, even at the Matlab/C++ stage, algorithms have been distributed and used in a clinical setting (for example, rule-based brain segmentation approaches).

(d) Diffusion Weighted Image (DWI) Analysis: A number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical analysis of fiber bundles have been contributed to the NAMIC toolkit. Some of these tools have been already integrated into diffusion dedicated ITK package with GUI as part of the NAMIC toolkit (e.g., FiberViewer-UNC) and into the Slicer platform (BWH tractography, clustering). The impact of NAMIC DWI analysis activities is best characterized by most recent journal articles and journal articles in print. The application of the NAMIC FiberViewer tool (UNC) in large clinical studies at UNC and Duke are in print (Taylor et al, Gilmore et al., Cascio et al.). Clinical application of the Slicer DTI package by BWH/MIT is reported in O’Donnell et al. Kuroki N. et al. and Nakamura et al.. The research by MGH is found in two journal publications by Tuch et al.. The description of the methodologies also appeared or will appear in peer reviewed journals (Corouge et al., Fletcher et al., Corouge et al.). New methods in development (Finsler metric GT, volumetric PDE based path analysis Utah, stochastic tractography BWH), and path of interest MGH, are currently tested on Core-3 DBP data, with the goal to give recommendations on which type of solution is appropriate to solve specific clinical analysis questions.

(e) Visualization: Core 2 researchers involved with NAMIC now (e.g., the founders of Kitware) were at the forefront of developing VTK (and of course ITK). Thus here we are considering technologies which are at a commercial level of development, and used at thousands of sites. Algorithms developed at NAMIC have driven new directions for these packages. Newer visualization methods, for example, the conformal flattening procedure have been ported to an ITK filter and is in the NAMIC Sandbox. Quasi-isometric methods for brain flattening from the MGH Free Surfer have become part of the NAMIC enterprise as well. These flattening procedures are very easy to use, and may also be employed for registration. Code for the control of distortion of the area in flattening has been incorporated which give area-preservation with minimal distortion. The techniques may be also used for several other purposes included automatic fly-throughs in endoscopy (incorporated into Slicer), and for texture mappings for general visualization purposes.

==Logistics==
When completed, the information should be sent to:

Gwen Jacobs, PhD

Professor of Neuroscience

Asst. CIO and Director of Academic Computing

1 Lewis Hall

Montana State University

Bozeman, MT 59717

406-994-7334 - phone

406-994-7077 - FAX

gwen@cns.montana.edu <mailto:gwen@cns.montana.edu>

2007 Materials for NCBC Program Review

2007-06-06T18:33:29Z

Shenton: /* '''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''= */

2007 Materials for NCBC Program Review

2007-06-06T18:30:44Z

Shenton: /* '''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''= */

2007 Materials for NCBC Program Review

2007-06-06T18:27:24Z

Shenton: /* Q2.1 To what extent does the vision and direction of the NCBC initiative promote biomedical computing? */

2007 Materials for NCBC Program Review

2007-06-06T18:21:26Z

Shenton: /* '''Q1: A copy of two parts of your most recent progress report: the summary section and the highlights section.'''= */

2007 Annual Scientific Report

2007-05-20T17:06:52Z

Shenton: /* Response to EAB Report */

Back to [[2007_Progress_Report]]

For reference:

*[[2006_Progress_Report]]

*[[Media:2005_NAMIC_Specialized_ProgReport.pdf|2005 Annual Scientific Report]]

== 1. Introduction ==

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with espect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs continue to include neuropsychiatric disorders such as Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Schizophrenia (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a adopting a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

(Please note that this report is available on the NA-MIC wiki at the following url: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report)

== 2. Four Main Themes ==

This year's activities focus on four main themes: Diffusion Image Analysis, Structural Analysis, Functional MRI Analysis, and the NA-MIC Kit. Each of the following sections begins with an overview of the theme, provides a progress update and list of key investigators, and concludes with a set of links to additional information for individual projects in that theme.

These thematic activities involve scientists from each of the 7 NA-MIC cores (Appendix).

* [http://www.na-mic.org/Wiki/index.php/Algorithm:Main Core 1 Algorithms]-Ross Whitaker PI
* [http://www.na-mic.org/Wiki/index.php/Engineering:Main Core 2 Engineering]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/DBP:Main Core 3 Driving Biological Problems] DBP1-Martha Shenton PI / DBP2-Andy Saykin PI / DBP3-Steven Potkin PI
* [http://www.na-mic.org/Wiki/index.php/Service:Main Core 4 Service]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/Training:Main Core 5 Training]-Randy Gollub PI
* [http://www.na-mic.org/Wiki/index.php/Dissemination:Main Core 6 Dissemination]-Tina Kapur Co-PI; Steve Pieper Co-PI
* [http://www.na-mic.org/Wiki/index.php/Leadership:Main Core 7 Leadership]-Ron Kikinis

=== 2.1 Diffusion Image Analysis Theme ===

==== Progress ====
Over the past year, we have continued to develop a number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical diffusion tensor analysis. These tools have been already integrated into diffusion dedicated software (e.g., Fiber Viewer-UNC). In addition, we started developing tools for group data analysis, such as white matter atlases, and tools for data integration, that would combine and interface among different imaging modalities (such as structural, fMRI and DTI), to better estimate anatomical and functional connectivity. This software has continued to be used in multiple clinical projects involving several psychiatric populations. Below we provide detailed progress in the area of diffusion image analysis.

==== Fiber Tract Extraction and Analysis ====
* Since last year, all of the algorithms for fiber tractography and anisotropy estimation have been implemented in both the “Fiber Viewer” and “Slicer” packages, and now the resulting methods are being applied to clinical studies. Fiber Tract integrity has been investigated and results have been presented at several international conferences for four key fiber tracts in schizophrenia (corpus callosum, fornix, inferior occipito-frontal fasciculus, and uncinate fasciculus). These tracts have been extracted, and fractional anisotropy has been compared between groups, demonstrating fronto-temporal connectivity abnormalities in schizophrenia.

* In addition to clinical studies, teams have been working on other methods to define and estimate brain connectivity. The most important developments in this regard included volumentric connectivity and stochastic tractography measures. For volumentric connectivity, a PDE-based approach to white matter connectivity from DTI has been developed, that is founded on the principal of minimal paths through the tensor volume. This method computes a volumetric representation of a white matter tract given two endpoint regions. In addition, statistical methods for quantifying the full tensor data along these pathways have been also developed. Directional PDE-based flows have been proposed and implemented for a similar purpose. Further, an approach called "stochastic tractography," has been developed to calculate probability of two regions being connected by the fiber tract. This should be especialy useful when tract has to go through the regions characterized by low diffusion anisotropy.

* Some initial attempts have been made to population based analysis of DT-MRI. One method is based on the unbiased non-rigid registration of a population to a common coordinate system. The registration jointly produces an average DTI atlas, which is unbiased with respect to the choice of a template image, along with a diffeomorphic correspondence for each image. The anatomically significant correspondence provides a basis for comparison of tensor features and fiber tract geometry in clinical studies.

==== Fractional Anisotropy Analysis ====
* We have used tools developed last year, and applied them to our population of chronic schizophrenia subjects in order to investigate two fiber tracts: the cingulum bundle and the corpus callosum. In the case of cingulum bundle, manually drawn regions of interest and Finsler geometry were used to extract entire cingulum bundle fiber tract, and FA was estimated along the tract, and compared between groups. Corpus Callosum cross sectional area and its probabilistic subdivisions were determined automatically from the structural MRI scans using a model based deformable contour segmentation. The structural scan was then co-registered with the DTI scan and the anatomical corpus callosum subdivisions were propagated to the associated FA map, and compared between groups, demonstrating deficient interhemispheric communication in schizophrenia.

==== Integration of fMRI and DTI, Path-of-Interest Analysis ====
* Progress has been also made in the development of a tool that would successfully combine and integrate functional and anatomical information. The Optimal Path Analysis method has been applied to the Harvard- BWH fMRI dataset, and anatomical connections between regions active during the fMRI experiment have been extracted for each subject. The connectivity has calculated and compared between groups. This tool is now being ported to Slicer3.

==== Key Investigators ====

* BWH: Martha Shenton, Marek Kubicki, Marc Niethammer, Sylvain Bouix, Jennifer Fitzsimmons, Katarina Quintis, Doug Markant, Kate Smith, Georgia Bushell, Mark Dreusicke, Carl-Fredrik Westin, Raul San Jose, Gordon Kindlmann
* MGH: Bruce Fischl, Denis Jen, David Kennedy
* MIT: Lauren O'Donnell, Polina Golland, Tri Ngo
* UCI: James Fallon, Martina Panzenboeck
* UNC: Guido Gerig, Isabelle Corouge, Casey Goodlett, Martin Styner
* Utah: Tom Fletcher, Ross Whitaker, Saurav Basu, Davis McKay
* GA Tech: Eric Pichon, John Melonakos, Xavier LaFaucheur, Vandana Mohan, Allen Tannenbaum
* Dartmouth: John West, Andrew Saykin, Laura Flashman, Paul Wang, Heather Pixley, Robert Roth
* Isomics: Steve Pieper
* Kitware: Luis Ibanez

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Diffusion_Image_Analysis NA-MIC Projects on Diffusion Image Analysis].

=== 2.2 Structural Analysis Theme ===

==== Progress ====

Within the NAMIC's structural analysis theme, the main topics of interest are structural segmentation, registration and shape analysis. These topics are of course intertwined, e.g., segmentation or registration can directly deliver structural correspondence used in shape analysis, or in turn shape modeling is necessary for good structural segmentations. Here is a selection of progress highlights in the structural analysis theme

====='''Segmentation'''=====

* Wavelet based structural segmentation: We have developed a spherical wavelet based framework for the segmentation of selected brain structures, such as the hippocampus or the caudate nucleus. An automated segmentation of such structures must be highly accurate and include high frequency variations in the surface. Since shape representation is a key component of the segmentation, it must be rich enough to express shape variations at various frequency levels, from low harmonics to sharp edges. Medical object segmentation with deformable models and statistical shape modeling may be combined to obtain a more robust and accurate segmentation. To address this issue, a decomposable spherical wavelet based shape representation targeted to the population seems natural, where the shape parameters are separated into groups that describe independent global and/or local biological variations in the population, and a prior induced over each group explicitly encodes these variations. This work presents three novel contributions for shape representation, multiscale prior probability estimation and segmentation.

* Directional based segmentation: We have proposed an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. In the classical case, the Euclidean metric is locally multiplied by a scalar conformal factor (based on image information) such that the weighted length of curves lying on points of interest (typically edges) is small. We propose to add directionality to the factor, and show that one gets a well-defined minimization problem in the case that the factor defines a Finsler metric. Optimal curves may be obtained using the calculus of variations or dynamic programming. This methodology also makes connections to the important technique of graph-cuts.

* Statistical PDE methods for segmentation in shape space: This past year, we have proposed another method to perform shape-driven segmentation. In our approach, shapes are represented using binary maps, and linear PCA is utilized to provide shape priors for segmentation. Intensity based probability distributions are then employed to convert the given volume into a binary map representation, and a new energy functional is formulated whose minimization is performed using a parametric model for surface evolution in the shape space. Our algorithm is then applied to the segmentation of brain caudate nucleus and hippocampus from MRI data. Our validation shows that the proposed algorithm outperforms the log-likelihood based energy, converges in less than 5 iterations and is very obust to initialization. The overall algorithm is illustrates the potential for segmentation in shape space.

* Rule-based segmentation methods: We have continued this past year to develop segmentation methods based on heuristic rules provided to us by our Core 3 partners for segmenting various brain regions of interest in schizophrenia, e.g. the DLPFC and the striatum. The idea is to try to semi-automate these rules in order to forge an interactive tool for segmentation which can greatly shorten the time necessary for manual segmentation. Typically, these methods are used in conjunction with some Bayesian classifier which further aids to automating and in speeding up the given segmentation methodology.

* Tissue and structural segmentation via EM Segmenter: Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases segmentation is mostly performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We have developed an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior knowledge. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. The mapping between atlas and image space, structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the Maximum A posteriori Probability (MAP) estimation problem. These results are then fed into an Active Mean Field approach, which views the results as priors to a Mean Field approximation with a curve length prior. This segmentation framework has been ported into the NAMIC-toolkit and a first version of the tool is distributed with Slicer 3.

====='''Shape Analysis'''=====

* Wavelet based shape analysis: A shape representation that encodes variations at multiple scales can be useful as a rich feature set for shape analysis and classification. Combining tools from the existing shape analysis toolset, we extended it to include spherical wavelet coefficients (SWC) as features and compared the results obtained to shape analysis using a SPHARM-PDM representation. The rich SWC feature set allows the differentiation of shape differences at various scales as well as highly correlates with the existing SPHARM-PDM analysis but with increased statistical sensitivity. Wavelet coefficient shrinkage and dimension reduction are well-understood and have been widely researched for traditional types of wavelet decompositions but not much explored for the second generation wavelets. During the past year, we have developed a Bayesian model on our specific wavelet structure based on a population of surfaces. For each shape, the deviation from the mean is computed and is modeled as the sum of an unknown signal and a noise. This deviation is encoded by the wavelet transform and our goal is to estimate the wavelet coefficients belonging to the noiseless signal.

* Surface flattening for shape analysis: Flattened representations of undulated surfaces constitute an active area of research in the field of medical imaging and visualization, due to their extensive use for registration and shape analysis of various structures of interest. We have presented a method for flattening anatomical surfaces in an area preserving manner, while minimizing the geometrical distortion. This method is based on the theory of optimal mass transport and conformal mapping of surfaces. The key idea here is the use of a multiresolution scheme for the solution of optimal mass transport gradient descent equations which allows a fast and stable solution for optimal transport. The method has been implemented on a GPU, allowing us to flatten a 128 by 128 by 128 volume in about 5 seconds on a standard workstation.

* Curvature based population correspondence: We have extended the Minimum Description Length population based correspondence framework to include curvature based measurements, such as the Koenderink Shape Index S and Curvedness C in combination with the standard location information. Current methodology in population based correspondence is based mainly on minimizing distribution properties of surface point locations and are thus not invariant to alignment. We have favorably compared our combined "Curvature + Location" MDL to the standard MDL, as well as to the SPHARM approach, in complex structures, such as the striatal brain structure (composed of caudate, nucleus accumbens and putamen).

* Shape analysis toolset: A considerable amount of work was spent on the development aspect of the shape analysis tools. The distributed set of tools is continuously enhanced and the population based correspondence framework has been released as open source. All the tools including the visualization tool, KWMeshVisu, can be called directly from Slicer 3. The visualization tool allows the overlay of scalar, vector and ellipsoid data onto surfaces via versatile colormaps. The attributed surfaces are then visible within Slicer 3. This lean visualization tool fills a niche and is also used in our cortical thickness analysis tool. Also, while it is entirely possible to run all shape analysis steps by calling the individual modules, this is highly inefficient in a larger study. As a result we are developing a separate shape pipeline Slicer module that uses Batchmake to run the shape analysis pipeline as a distributed, background process. The whole shape analysis pipeline will thus become entirely encapsulated and accessible to the trained clinical collaborator.

* Particle based correspondence: We have developed a method for a multi-object correspondence optimization, and have applied it successfully to a proof-of-concept application for the analysis of brain structure complexes from a longitudinal study of pediatric autism. This new method for constructing compact statistical point-based models of ensembles of similar shapes does not rely on any specific surface parameterization, requires little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including non-manifold surfaces and objects of arbitrary topology. The method uses a dynamic particle system to optimize correspondence point positions across all structures by simultaneously maximizing both the geometric accuracy and the statistical simplicity of the model.

==== Key Investigators ====

* MIT: Kilian Pohl, Sandy Wells, Eric Grimson
* UNC: Martin Styner, Ipek Oguz, Guido Gerig, Xavier Barbero
* Utah: Ross Whitaker, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer
* GaTech: Allen Tannenbaum, John Melonakos, Tauseef ur Rehman, Shawn Lankton, Ramsey Al-Hakim, Eric Pichon, Delphine Nain, Oleg Michailovich, Yogesh Rathi, James Malcolm
* Isomics: Steve Pieper
* GE: Bill Lorensen, Jim Miller
* Kitware: Luis Ibanez, Karthik Krishnan,
* UCLA: Michael J. Pan, Jagadeeswaran Rajendiran
* BWH: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt
* Dartmouth: Andrew Saykin
* UCI: James Fallon

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Structural_Image_Analysis NA-MIC Projects on Structural Image Analysis].

=== 2.3 Functional MRI Analysis Theme ===

====Progress====

During this year, the focus of the algorithms and the engineering
cores has been on the structural and DTI analysis. While we continued
to expand the methods and the infrastructure in NAMIC-kit to support
fMRI analysis, as well as using the analysis tools to perform clinical
studies, the emphasis of the work this year has been on integrating
the fMRI analysis with other modalities and supporting other
modalities.

===== Clinical Studies =====

We would like to highlight several clinical studies within NAMIC that
focused on fMRI data and its relationship with other imaging
modalities:

* Imaging Phenotypes in Schizophrenics and Controls: Functional connectivity of the DLPFC by genotype was investigated emplying partial least squares (PLS) correlation analysis. PLS is a multivariate analytical technique used to summarize large neuroimaging data sets in such a way as to correlate patterns of activation with a variable(s) of interest (i.e., DLPFC activity). In the most recent analysis, the DRD1 genotype was used as a grouping variable. This analysis has been submitted for publication (Tura, Turner, Fallon, Kennedy, and Potkin. Genetic Impact on Functional Connectivity in Schizophrenics During a Working Memory Task). Working memory performance did not differ significantly between the two cohorts. However, imaging-genetic analysis showed a significant difference (P< 0.05) between the circuitry engaged by each group. Significance and reliability of the resulting imaging-behavioral patterns within each genotype were assessed by 200 bootstrap and 500 permutation tests, respectively. In one group, the circuitry included the temporal pole, the insula, the dorsolateral prefrontal cortex, and the Brodmann Areas (BA) 1,2,3,4,6,11 and 21, while the other group showed a network comprising the tectum, precuneus retroplenial, vermis, substantia nigra, BA 22,39,8, and 9. The DRD1 polymorphic site may characterize circuitry differences in schizophrenic patients.

*Path-Of-Interest Analysis (joint DTI/fMRI modeling): We collected preliminary data using an application of the “optimal path analysis”. In this analysis, we extracted group fMRI activation due to the Stroop effect (an attentional experiment where incongruent color, in which the word is written, competes with name of the color itself, activating areas responding to conflict monitoring and selection) separately for controls and schizophrenics. This resulted in three clusters of activation, one in the right Dorsolateral Prefrontal Cortex, a second in the Anterior Cingulate Gyrus, and a third in the Medial Parietal Lobe. In the next step, we placed activation clusters in each individual space, by reversing normalization parameters used during fMRI analysis. Finally, EPI fMRI scans were co-registered to DTI scans, and the same registration parameters were applied to activation maps (fMRI results). Regions of activation were used as start and destination points for optimal path analysis, which resulted in three separate paths of optimal connectivity for each subject. The probability of the connections were then calculated for each path and each subject, and compared between groups. In our preliminary analysis, we included 10 control subjects and 10 chronic schizophrenics. Our results demonstrated a relationship between Stroop Effect fMRI activation in the medial parietal area and optimal path connectivity between parietal and cingulate activation sites in schizophrenics (rho=-0.56; p=0.047), which was not observed in controls. These findings suggest that decreased connectivity may result in schizophrenics relying more on posterior parts of the executive attentional network during performance of the Stroop task.

*Hippocampal and frontal memory circuitry abnormalities in schizophrenia: Relation of diffusion, morphometric and fMRI markers: We performed a combined DTI, fMRI, and morphometric study on 13 patients with schizophrenia (SZ) and 14 HC. We identified areas of increased trace diffusivity (TD) in the hippocampal and insular regions as well as areas of reduced fractional anisotropy (FA) in left frontal white matter in SZ relative to HC (p<.01). Voxel based morphometry analyses in a subset of these subjects showed corresponding reductions in gray matter density in hippocampal and insular regions in patients relative to controls (p<.01). Analysis of fMRI results from the novel vs. repeated word contrast from the event-related auditory verbal episodic memory encoding/retrieval task in a subset of the subjects indicated reduced activation in frontal and temporal regions, as well as increased activation in posterior cingulate, retrosplenial, and thalamic regions in SZ relative to HC (p<.05). Further analysis showed that left frontal white matter FA was associated with activation in the left and right hippocampi as well as other frontal and temporal regions, but inversely related to activation in the retrosplenial/posterior cingulate region (p<.05). These initial findings indicate a pattern of relationships between of structural and functional brain abnormalities in schizophrenia and demonstrate the feasibility of integrated quantitative analyses across modalities.

===== Methods =====

During this year, we continued methodological development along two
directions:

*Improving fMRI detectors by incorporating Markov priors on the activation state. We integrated the improved detector into Slicer and performed substantial validation of the methods using fBIRN data provided by the UC Irvine group. Journal paper on the method has been submitted to IEEE TMI.

*Improving registration of EPI images to anatomical scans through modeling of the EPI distortions. We demonstrated an initial model that uses segmentation of the structural scan to predict the distortions in the EPI images. The preliminary results are quite encouraging.

==== Key Investigators ====

* MIT: Polina Golland, Sandy Wells, Wanmei Ou, Claire Poynton
* BWH: Martha Shenton, Marek Kubicki
* Dartmouth: Andy Saykin
* UCI: Jessica Turner, Stephen Potkin
* Toronto: Jim Kennedy

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#fMRI_Analysis NA-MIC Projects on fMRI Analysis].

=== 2.4 [[NA-MIC-Kit|NA-MIC Kit]] Theme ===

==== Progress ====
The continuing vision of the NA-MIC Kit is to provide an open source set of software tools and methodologies that will serve as the foundation for medical image computing projects for both academic and commercial use. Key elements of this vision are:

# ''Unrestrictive License.'' Users of the Kit are free to distribute their derived works under any license suitable to their needs.
# ''Cross Platform.'' This software set can be adapted to the best available price-performance computer systems for any particular use.
# ''Extensible Application Framework.'' New techniques and algorithms can be quickly integrated into a working system. Sophisticated user interfaces can be generated easily through automated processes. Sophisticated toolsets such as ITK, VTK, and KWWidgets are available to create and deploy applications quickly.
# ''Quality Software Process.'' Developers and users can rely on accurate and well documented behavior from all the parts of the Kit.
# ''Sustainable Community.'' Users are actively involved in the design process of the Kit. Documentation, training materials, and hands-on sessions are available and well publicized to the community.

====='''Slicer3'''=====

A major focus of the third year was the implementation of the Slicer3 in the NAMIC Kit. This effort addressed Item #3 above ''Extensible Application Framework.'' The previous two years of the NAMIC project, which entailed gathering requirements from Cores 1 and 3, and developing the computational foundation, toolsets, and software process, came together in the Slicer3 application platform.

Core 2 worked hard to insure that the Slicer 3 application serves, and will continue to serve, as a productive technology deployment platform. The application framework was designed carefully to provide ease-of-use, both in terms of interaction and software integration. Advanced capabilities, including the ability to launch large-scale grid computing, was designed into the application. Some of the key features of the Slicer3 application completed in the third year include the following.
* Advanced application framework including a tuned GUI for ease of use, undo/redo capabilities, 2D/3D view windows, and support for advanced interaction techniques such as 3D widgets. The application provides viewers for displaying slices, volumes, and models including the ability to edit properties. A built in transformation pipeline enables users to confidently import, edit and display data in a consistent coordinate system.
* The application is data-driven based on the next generation MRML scene description file format. Backward compatibility to Slicer 2.x MRML files is preserved.
* A module plug-in architecture and execution model that enables researchers to package and integrate their software into the Slicer3 framework. Plug-in modules can be implemented in a variety of programming languages, and are described using a simple XML description. These modules, when located and loaded into Slicer3, have the capability to automatically generate their user interface, which is seamlessly integrated into the Slicer3 GUI.
* Support for editing and marking data including support for fudicials, paint and draw editors.
* The creation of several simple plug-in modules, including the conversion of previous Slicer 2.x modules to the new Slicer3 architecture.

====='''EM Segment'''=====

As an application framework, Slicer3 provides tools for loading, viewing, measuring, editing, and saving data. To support advanced medical image analysis, plug-in modules are required in conjunction with the Slicer3 core. To demonstrate the capabilities of the framework we implemented the [http://www.na-mic.org/Wiki/index.php/Slicer3:EM EM Segment] module, a sophisticated and proven method for automatically segmenting complex anatomical structures.

To use this module, users specify parameters defining the image protocol and the anatomical structures of interests. This process results in a template that the module uses to automatically segment large data sets. The template is composed of atlas data and a non-trivial collection of parameters for the EM Segment algorithm ([[Media:Pohl-miccai-short-2005.pdf|Pohl et al.]]). Once the parameters are specified, the target images are segmented using the algorithm; if the results are satisfactory, the template is saved and can be used later to segment new images (via the GUI or batch processing). If the results are unsatisfactory, the parameters can be modified and the segmentation re-run.

Besides successfully demonstrating the use of complex algorithms in the Slicer3 framework, this effort also led us to develop tools, including modifications to the underlying KWWidgets GUI toolkit, to support module workflow. With these tools, it is possible to simplify complex modules by dividing the complicated template specification task into a number of smaller, intuitive steps. These steps are enforced by the GUI and reduce the potential for user error, while improving the overall user interface.

====='''Quality Software Process'''=====

Building on [http://lwn.net/Articles/188693/ last year's success with the KDE community], the NAMIC community continued to extend its world-class, open source software process tools CMake, DART, CTest and DART. These tools, which form the core of a quality-oriented, test-driven development (TDD) software process. In particular, the CPack system is now able to automatically package and distibute code, libraries, executables and installers across all of NAMIC's supported platforms (i.e., Linux, Windows, Mac). This enables the NAMIC developer community to rapidly deploy software tools to the user community.

==== Key Investigators ====

* GE: Bill Lorensen, Jim Miller, Xiaodong Tao, Dan Blezek
* Isomics: Steve Pieper, Alex Yarmarkovich
* Kitware: Will Schroeder, Luis Ibanez, Brad Davis, Andy Cedilnik, Sebastien Barre, Bill Hoffman
* UCLA: Mike Pan, Jagadeeswaran Rajendiran
* UCSD: Neil Jones, Jeffrey Grethe, Mark Ellisman
* BWH: Nicole Aucoin, Katie Hayes, Wendy Plesniak, Mike Halle, Gordon Kindlmann, Raul San Jose Estepar, Haiying Liu, Ron Kikinis
* MIT: Lauren O'Donnell, Kilian Pohl

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#NA-MIC_Kit NA-MIC Kit Projects].

== 3. Highlights ==
The third year of the NAMIC project saw continued development and dissemination of medical image analysis software. With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NAMIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NAMIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

=== 3.1 Advanced Algorithms ===
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NAMIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NAMIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NAMIC Sandbox in preparation for formal acceptance into the NAMIC Kit.

=== 3.2 Technology Deployment Platform: Slicer3 ===
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NAMIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NAMIC Kit, and are now available to all other modules built on the Slicer3 framework.

=== 3.3 Outreach and Technology Transfer ===
Cores 4-5-6 continue to support, train and dissemniate to the NAMIC community, and the broader biomedical computing community.
* NAMIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NAMIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair.

== 4. Impact and Value to Biocomputing ==

In NA-MIC's third year, it is evident that NA-MIC is developing a
culture, environment, and resources to foster and incite collaborative
research in medical image analysis that draws together mathematicians,
computer scientists, software engineers, and clinical
researchers. These artefacts of NA-MIC impact how NA-MIC
operates, make NA-MIC a fulcrum for NIH funded research,
and draws new collaborators from across the country
and around the world to NA-MIC.

=== 4.1 Impact within the Center ===

Within the center, the NA-MIC organization, NA-MIC processes, and the
NA-MIC calendar has permeated the research. The organization is
nimble, forming ad hoc distributed teams within and between cores to
address specific biocomputing tasks. Information is shared freely on
the NA-MIC Wiki, on the weekly Engineering telephone conferences, and
in the NA-MIC Subversion source code repository. The software
engineering tools of CMake, Dart 2 and CTest, CPack, and KWWidgets
facilitate a cross platform software environment for medical image
analysis that be easily built, tested, and distributed to
end-users. Core 2 has provided a platform, Slicer 3, that allows Core
1 to easily integrate new technology and deliver this technology in an
end user application to Core 3. Core 1 has developed a host of
techniques to apply to structural and diffusion analysis which are
under evaluation by Core 3. Major NA-MIC events, such as the annual
All Hands Meeting, the Summer Project Week, the Spring Algorithms
meeting, and Engineering Teleconferences are avidly attended by NA-MIC
researchers as opportunities to foster collaborations.

=== 4.2 Impact within NIH Funded Research ===

Within NIH funded research, NA-MIC continues to forge relationships
with other large NIH funded projects such as BIRN, caBIG, NAC, and
IGT. Here, we are sharing the NA-MIC culture, engineering practices,
and tools. BIRN hosts data for the NA-MIC researchers and NA-MIC hosts
BIRN wikis. caBIG lists the 3D Slicer among the applications available
on the National Cancer Imaging Archive. NAC and IGT use the NA-MIC
infrastructure and are involved in the development of the 3D
Slicer. BIRN recently held an event modeled after the NA-MIC Project
Week. NA-MIC has become a resource on open source licensing to the medical image analysis community.

NA-MIC is also attracting NIH funded collaborations. Two grants have
been funded under PAR-05-063 to collaborate with NA-MIC: ''Automated FE Mesh Development'' and ''Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI''. Five additional
applications to collaborate with NA-MIC via the NCBC collaborative
grant mechanism are under consideration. Additional grant applications
submitted under other calls are planning to use and extend the NA-MIC tools.

=== 4.3 National and International Impact ===

NA-MIC events and tools garner national and international interest.
There were nearly 100 participants at the NA-MIC All Hands Meeting in
January 2007, with many of these participants from outside of NA-MIC.
Several researchers from outside the NA-MIC community have attended
the Summer Project Weeks and the Winter Project Half-Weeks to gain
access to the NA-MIC tools and people. These external researchers are
contributing ideas and technology back into NA-MIC.

Components of the NA-MIC kit are used globally. The software
engineering tools of CMake, Dart 2 and CTest are used by many open
source projects and commercial applications. For example, the K Desktop Environment (KDE) for
Linux and Unix workstations uses CMake and Dart. KDE is one of the
largest open source projects in the world. Many open source projects and
commercial products are benefiting from the NA-MIC related
contributions to ITK and VTK. Finally, Slicer 3 is being used as an
image analysis platform in several fields outside of medical image
analysis, in particular, biological image analysis, astronomy, and industrial inspection.

NA-MIC co-sponsored the ''Workshop on Open Science'' at the Medical
Image Computing and Computer-Assisted Intervention (MICCAI) 2006
conference. The proceedings of the workshop are published on the
electronic Insight Journal, another NIH-funded activity.

Over 50 NA-MIC related publications have been produced since the inception of the center.

== 5.NA-MIC Timeline ==

This section provides a table of NAMIC timelines from the original proposal that graphically depicts completed tasks/goals in years 1, 2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines have also been described.

[[2007_Scientific_Report_Timeline|2007 Scientific Report Timeline]]

== Appendix A Publications ==

http://www.na-mic.org/Wiki/index.php/Publications

== Appendix B EAB Report ==

===EAB Report===
The NA-MIC External Advisory Board (EAB), chaired by Professor Chris Johnson of the University of Utah, met at the annual All-Hands Meeting. After individual presentations by NA-MIC investigators and open as well as closed-door EAB discussion, the Board provided its independent expert assessment of the Center.

[https://share.spl.harvard.edu/grants/u54/2007/APR%202007/NAMIC-EAB-Report07-ver5.doc This is a password protected link to the EAB report.]

===Response to EAB Report===

The NA-MIC leadership discussed the excellent suggestions made by the EAB, and in the following section we respond to each of them.

''EAB: The Center should actively pursue methods to distribute their software and other resources to a broader community. As we noted in last year’s EAB Report, the Center is doing a good job at distributing their software toolkit, however, largely the distribution has been focused internally to NA-MIC collaborators. The EAB recommends that the Center work to more broadly distribute its software to the larger community of neuroimaging researchers. In addition, because the Center’s software tools will also be useful to biomedical researchers outside of neuroimaging, the EAB recommends the Center consider additional methods to “get the word out” about their resources.''

NA-MIC: The focus in the last two years of the Center has been to broaden the outreach effort to the neuroimaging community and to other biomedical researchers outside of this community. Many algorithms have been developed in ITK/VTK, and then ported to Slicer to try to make them as accessible as possible. We believe that the ITK/VTK platforms have been quite widely disseminated. However, as the EAB has noted, the current training and dissemination funds are limited, and the Center leadership will continue to look for creative solutions to get the word out within these resource constraints.

''EAB: The Center is well suited to assess and compare algorithms. An important question in image analysis involves understanding which image-processing algorithm is best suited for a particular task. Given the large number of different image analysis algorithms being created by NA-MIC, the Center is in a good position to evaluate the effectiveness of different algorithms and to make appropriate recommendations.''

NA-MIC: This topic has been discussed extensively within the Center, and we have addressed this issue in a manner that is aligned with our core expertise in open source software. More specifically, we will continue to build upon our strength and provide infrastructure and tools needed for open science, as well as to train researchers in the use of these tools. It is our hope that by providing easy access to these tools, we will be facilitating the process of comparison between algorithms for various biomedical tasks. This being said, many times there is no optimal algorithm for every imaging purpose. A certain segmentation routine may be excellent for one purpose, but not for another. This is a well recognized problem in image processing and computer vision, and not a fundamental limitation of medical imaging. Comparisons, however, among different algorithms to determine which algorithms are best for which questions being addressed is nonetheless important and we have made some inroads here. Sylvain Bouix, for example, has a paper in press in Neuroimage on evaluating brain tissue classifiers without ground truth, which compares several different segmentation algorithms. Also, in the shape analysis field, every NA-MIC related publication performs the necessary comparisons. Furthermore, Martin Styner and coworkers (Core 1 and 3 investigators) authored a paper on shape analysis comparisons at ISBI 2006. In addition, Martin is co-organizing a caudate and liver segmentation comparison workshop at MICCAI 2007, with currently over 35 registered participating methods. Our philosophy is to make a large menu of algorithmic methodologies available in as easy to use form as possible, so as to encourage the kind of comparisons recently done by Drs. Bouix and Styner.

''EAB: The Center should consider additional techniques to validate and verify techniques and software. The EAB was pleased to see the progress the Center made in quantifying and validating their algorithms and implementations within the NA-MIC Kit. However, because this is such an important issue, the EAB recommends the Center continue to make progress in this area.
''

NA-MIC: The Center intends to continue its effort in validation and verification of its software implementations. Center researchers have also come up with original techniques for validation, e.g., the Laplacian method for comparing the accuracy of segmentations. These validation techniques have been used to drive new original algorithmic as well. In addition, work within the Center has continued to compare manual measures of brain structure, i.e., the caudate, as a gold standard for automated measures of caudate in the same subjects. This kind of validation will continue.

''EAB: The Center should generate a set of success criteria. The EAB recommends that the Center create a set of benchmarks of success that it will use in future years to measure the Center’s success and as ways to evaluate the Center’s continuing progress and at the time of the Center’s first renewal.''

NA-MIC: We note that we have established benchmarks for success that are similar to criteria used by study sections for reviewing the success of other grant mechanisms, although we also include criteria for success that is specific to NA-MIC. These criteria include:

• Papers in peer reviewed publications

• Technology transfers from Core 1 to Core 3 researchers

• Software released as part of the NA-MIC Kit

• Training outside researchers in the use of the NA-MIC Kit

= Logistics =

== Schedule and process for preparation of this report ==

* March 30 - Assign section/theme leads (Ron). Last year: introduction (marty), structural analysis (allen, martin), dti (guido, marty/marek), fmri (polina, andy), namic kit(bill), timeline (ross), highlights (will), impact (bill).
*April 7,10 - tcons with Marty, Ross, Will, Tina to finalize the layout, process, and timeline of report.
* April 13 - update projects list using last year's [[NA-MIC_Collaborations]] and [[2007_Project_Half_Week#Projects|projects pursued at the half week in SLC]]. Remind investigators to update individual pages. (Tina)
* April 23- complete project description pages in updated list: [[NA-MIC_Collaborations]] (all project owners).
* April 30 - complete section summaries, introduction, highlights, impact, timeline (owners of these topics)
* May 8 - submit wiki report to NA-MIC editor, Ann (Tina)
* May 17 - submit Edited report to Rachana in pdf format (Ann)
* May 25 - submit final report, if changes are needed (Ann)
* May 29 - ship final package to NIH (Rachana)

== Guidelines from NIH Program Officer ==
===NIH Reviewer Comments on Yr2 APR (July 2006)===
The following additional guidelines were provided by the NIH progress review committee after they reviewed the 2006 report:

1. Comment on obstacles and challenges encountered during the year.

2. Comment on outcomes of DBPs.

3. Create a context for the report in the Introduction.

4. Comment on how all the themes as a whole are moving forward.

5. Full citations should be given within the report as footnotes or in a bibliography, not just as hyperlinks.

6. Explain time-lines with text including reasons for delays. Link timelines to papers and citations.

7. Define terms used in the timeline to describe the amount of progress being made, especially with respect to software development.

8. Put priority weights on completed tasks within timeline. Note if a task is a stepping stone or a final goal.

9. Make sure the projects presented in the annual report are also listed in the project pages of the NAMIC Wiki and vice versa.

10. In the budget pages, please elaborate on the number and locations for travel.

===Feb 2006===
The following guidelines were provided by Grace Peng, NA-MIC program officer, in Feb 2006.

The key is to synthesize all the individual elements into bigger picture stories that really speak of each area’s impact to the community.

The specialized scientific report should have the following format:

# Introductory page describing the new grouping of NAMIC project themes.
# A description of progress in each NAMIC project theme (not to exceed 2 pages each), tying together relevant activities from participating subcomponents and referencing cores in parentheses.
# A table of NAMIC timelines (from original proposal), graphically depicting completed tasks/goals in years 1,2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines should be described.
# A description of 3 highlights selected from all NAMIC projects to showcase NAMIC.
# A discussion of NAMIC’s impact and value to the biocomputing community this year.

2007 Annual Scientific Report

2007-05-20T17:06:32Z

Shenton: /* Response to EAB Report */

Back to [[2007_Progress_Report]]

For reference:

*[[2006_Progress_Report]]

*[[Media:2005_NAMIC_Specialized_ProgReport.pdf|2005 Annual Scientific Report]]

== 1. Introduction ==

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with espect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs continue to include neuropsychiatric disorders such as Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Schizophrenia (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a adopting a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

(Please note that this report is available on the NA-MIC wiki at the following url: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report)

== 2. Four Main Themes ==

This year's activities focus on four main themes: Diffusion Image Analysis, Structural Analysis, Functional MRI Analysis, and the NA-MIC Kit. Each of the following sections begins with an overview of the theme, provides a progress update and list of key investigators, and concludes with a set of links to additional information for individual projects in that theme.

These thematic activities involve scientists from each of the 7 NA-MIC cores (Appendix).

* [http://www.na-mic.org/Wiki/index.php/Algorithm:Main Core 1 Algorithms]-Ross Whitaker PI
* [http://www.na-mic.org/Wiki/index.php/Engineering:Main Core 2 Engineering]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/DBP:Main Core 3 Driving Biological Problems] DBP1-Martha Shenton PI / DBP2-Andy Saykin PI / DBP3-Steven Potkin PI
* [http://www.na-mic.org/Wiki/index.php/Service:Main Core 4 Service]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/Training:Main Core 5 Training]-Randy Gollub PI
* [http://www.na-mic.org/Wiki/index.php/Dissemination:Main Core 6 Dissemination]-Tina Kapur Co-PI; Steve Pieper Co-PI
* [http://www.na-mic.org/Wiki/index.php/Leadership:Main Core 7 Leadership]-Ron Kikinis

=== 2.1 Diffusion Image Analysis Theme ===

==== Progress ====
Over the past year, we have continued to develop a number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical diffusion tensor analysis. These tools have been already integrated into diffusion dedicated software (e.g., Fiber Viewer-UNC). In addition, we started developing tools for group data analysis, such as white matter atlases, and tools for data integration, that would combine and interface among different imaging modalities (such as structural, fMRI and DTI), to better estimate anatomical and functional connectivity. This software has continued to be used in multiple clinical projects involving several psychiatric populations. Below we provide detailed progress in the area of diffusion image analysis.

==== Fiber Tract Extraction and Analysis ====
* Since last year, all of the algorithms for fiber tractography and anisotropy estimation have been implemented in both the “Fiber Viewer” and “Slicer” packages, and now the resulting methods are being applied to clinical studies. Fiber Tract integrity has been investigated and results have been presented at several international conferences for four key fiber tracts in schizophrenia (corpus callosum, fornix, inferior occipito-frontal fasciculus, and uncinate fasciculus). These tracts have been extracted, and fractional anisotropy has been compared between groups, demonstrating fronto-temporal connectivity abnormalities in schizophrenia.

* In addition to clinical studies, teams have been working on other methods to define and estimate brain connectivity. The most important developments in this regard included volumentric connectivity and stochastic tractography measures. For volumentric connectivity, a PDE-based approach to white matter connectivity from DTI has been developed, that is founded on the principal of minimal paths through the tensor volume. This method computes a volumetric representation of a white matter tract given two endpoint regions. In addition, statistical methods for quantifying the full tensor data along these pathways have been also developed. Directional PDE-based flows have been proposed and implemented for a similar purpose. Further, an approach called "stochastic tractography," has been developed to calculate probability of two regions being connected by the fiber tract. This should be especialy useful when tract has to go through the regions characterized by low diffusion anisotropy.

* Some initial attempts have been made to population based analysis of DT-MRI. One method is based on the unbiased non-rigid registration of a population to a common coordinate system. The registration jointly produces an average DTI atlas, which is unbiased with respect to the choice of a template image, along with a diffeomorphic correspondence for each image. The anatomically significant correspondence provides a basis for comparison of tensor features and fiber tract geometry in clinical studies.

==== Fractional Anisotropy Analysis ====
* We have used tools developed last year, and applied them to our population of chronic schizophrenia subjects in order to investigate two fiber tracts: the cingulum bundle and the corpus callosum. In the case of cingulum bundle, manually drawn regions of interest and Finsler geometry were used to extract entire cingulum bundle fiber tract, and FA was estimated along the tract, and compared between groups. Corpus Callosum cross sectional area and its probabilistic subdivisions were determined automatically from the structural MRI scans using a model based deformable contour segmentation. The structural scan was then co-registered with the DTI scan and the anatomical corpus callosum subdivisions were propagated to the associated FA map, and compared between groups, demonstrating deficient interhemispheric communication in schizophrenia.

==== Integration of fMRI and DTI, Path-of-Interest Analysis ====
* Progress has been also made in the development of a tool that would successfully combine and integrate functional and anatomical information. The Optimal Path Analysis method has been applied to the Harvard- BWH fMRI dataset, and anatomical connections between regions active during the fMRI experiment have been extracted for each subject. The connectivity has calculated and compared between groups. This tool is now being ported to Slicer3.

==== Key Investigators ====

* BWH: Martha Shenton, Marek Kubicki, Marc Niethammer, Sylvain Bouix, Jennifer Fitzsimmons, Katarina Quintis, Doug Markant, Kate Smith, Georgia Bushell, Mark Dreusicke, Carl-Fredrik Westin, Raul San Jose, Gordon Kindlmann
* MGH: Bruce Fischl, Denis Jen, David Kennedy
* MIT: Lauren O'Donnell, Polina Golland, Tri Ngo
* UCI: James Fallon, Martina Panzenboeck
* UNC: Guido Gerig, Isabelle Corouge, Casey Goodlett, Martin Styner
* Utah: Tom Fletcher, Ross Whitaker, Saurav Basu, Davis McKay
* GA Tech: Eric Pichon, John Melonakos, Xavier LaFaucheur, Vandana Mohan, Allen Tannenbaum
* Dartmouth: John West, Andrew Saykin, Laura Flashman, Paul Wang, Heather Pixley, Robert Roth
* Isomics: Steve Pieper
* Kitware: Luis Ibanez

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Diffusion_Image_Analysis NA-MIC Projects on Diffusion Image Analysis].

=== 2.2 Structural Analysis Theme ===

==== Progress ====

Within the NAMIC's structural analysis theme, the main topics of interest are structural segmentation, registration and shape analysis. These topics are of course intertwined, e.g., segmentation or registration can directly deliver structural correspondence used in shape analysis, or in turn shape modeling is necessary for good structural segmentations. Here is a selection of progress highlights in the structural analysis theme

====='''Segmentation'''=====

* Wavelet based structural segmentation: We have developed a spherical wavelet based framework for the segmentation of selected brain structures, such as the hippocampus or the caudate nucleus. An automated segmentation of such structures must be highly accurate and include high frequency variations in the surface. Since shape representation is a key component of the segmentation, it must be rich enough to express shape variations at various frequency levels, from low harmonics to sharp edges. Medical object segmentation with deformable models and statistical shape modeling may be combined to obtain a more robust and accurate segmentation. To address this issue, a decomposable spherical wavelet based shape representation targeted to the population seems natural, where the shape parameters are separated into groups that describe independent global and/or local biological variations in the population, and a prior induced over each group explicitly encodes these variations. This work presents three novel contributions for shape representation, multiscale prior probability estimation and segmentation.

* Directional based segmentation: We have proposed an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. In the classical case, the Euclidean metric is locally multiplied by a scalar conformal factor (based on image information) such that the weighted length of curves lying on points of interest (typically edges) is small. We propose to add directionality to the factor, and show that one gets a well-defined minimization problem in the case that the factor defines a Finsler metric. Optimal curves may be obtained using the calculus of variations or dynamic programming. This methodology also makes connections to the important technique of graph-cuts.

* Statistical PDE methods for segmentation in shape space: This past year, we have proposed another method to perform shape-driven segmentation. In our approach, shapes are represented using binary maps, and linear PCA is utilized to provide shape priors for segmentation. Intensity based probability distributions are then employed to convert the given volume into a binary map representation, and a new energy functional is formulated whose minimization is performed using a parametric model for surface evolution in the shape space. Our algorithm is then applied to the segmentation of brain caudate nucleus and hippocampus from MRI data. Our validation shows that the proposed algorithm outperforms the log-likelihood based energy, converges in less than 5 iterations and is very obust to initialization. The overall algorithm is illustrates the potential for segmentation in shape space.

* Rule-based segmentation methods: We have continued this past year to develop segmentation methods based on heuristic rules provided to us by our Core 3 partners for segmenting various brain regions of interest in schizophrenia, e.g. the DLPFC and the striatum. The idea is to try to semi-automate these rules in order to forge an interactive tool for segmentation which can greatly shorten the time necessary for manual segmentation. Typically, these methods are used in conjunction with some Bayesian classifier which further aids to automating and in speeding up the given segmentation methodology.

* Tissue and structural segmentation via EM Segmenter: Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases segmentation is mostly performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We have developed an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior knowledge. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. The mapping between atlas and image space, structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the Maximum A posteriori Probability (MAP) estimation problem. These results are then fed into an Active Mean Field approach, which views the results as priors to a Mean Field approximation with a curve length prior. This segmentation framework has been ported into the NAMIC-toolkit and a first version of the tool is distributed with Slicer 3.

====='''Shape Analysis'''=====

* Wavelet based shape analysis: A shape representation that encodes variations at multiple scales can be useful as a rich feature set for shape analysis and classification. Combining tools from the existing shape analysis toolset, we extended it to include spherical wavelet coefficients (SWC) as features and compared the results obtained to shape analysis using a SPHARM-PDM representation. The rich SWC feature set allows the differentiation of shape differences at various scales as well as highly correlates with the existing SPHARM-PDM analysis but with increased statistical sensitivity. Wavelet coefficient shrinkage and dimension reduction are well-understood and have been widely researched for traditional types of wavelet decompositions but not much explored for the second generation wavelets. During the past year, we have developed a Bayesian model on our specific wavelet structure based on a population of surfaces. For each shape, the deviation from the mean is computed and is modeled as the sum of an unknown signal and a noise. This deviation is encoded by the wavelet transform and our goal is to estimate the wavelet coefficients belonging to the noiseless signal.

* Surface flattening for shape analysis: Flattened representations of undulated surfaces constitute an active area of research in the field of medical imaging and visualization, due to their extensive use for registration and shape analysis of various structures of interest. We have presented a method for flattening anatomical surfaces in an area preserving manner, while minimizing the geometrical distortion. This method is based on the theory of optimal mass transport and conformal mapping of surfaces. The key idea here is the use of a multiresolution scheme for the solution of optimal mass transport gradient descent equations which allows a fast and stable solution for optimal transport. The method has been implemented on a GPU, allowing us to flatten a 128 by 128 by 128 volume in about 5 seconds on a standard workstation.

* Curvature based population correspondence: We have extended the Minimum Description Length population based correspondence framework to include curvature based measurements, such as the Koenderink Shape Index S and Curvedness C in combination with the standard location information. Current methodology in population based correspondence is based mainly on minimizing distribution properties of surface point locations and are thus not invariant to alignment. We have favorably compared our combined "Curvature + Location" MDL to the standard MDL, as well as to the SPHARM approach, in complex structures, such as the striatal brain structure (composed of caudate, nucleus accumbens and putamen).

* Shape analysis toolset: A considerable amount of work was spent on the development aspect of the shape analysis tools. The distributed set of tools is continuously enhanced and the population based correspondence framework has been released as open source. All the tools including the visualization tool, KWMeshVisu, can be called directly from Slicer 3. The visualization tool allows the overlay of scalar, vector and ellipsoid data onto surfaces via versatile colormaps. The attributed surfaces are then visible within Slicer 3. This lean visualization tool fills a niche and is also used in our cortical thickness analysis tool. Also, while it is entirely possible to run all shape analysis steps by calling the individual modules, this is highly inefficient in a larger study. As a result we are developing a separate shape pipeline Slicer module that uses Batchmake to run the shape analysis pipeline as a distributed, background process. The whole shape analysis pipeline will thus become entirely encapsulated and accessible to the trained clinical collaborator.

* Particle based correspondence: We have developed a method for a multi-object correspondence optimization, and have applied it successfully to a proof-of-concept application for the analysis of brain structure complexes from a longitudinal study of pediatric autism. This new method for constructing compact statistical point-based models of ensembles of similar shapes does not rely on any specific surface parameterization, requires little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including non-manifold surfaces and objects of arbitrary topology. The method uses a dynamic particle system to optimize correspondence point positions across all structures by simultaneously maximizing both the geometric accuracy and the statistical simplicity of the model.

==== Key Investigators ====

* MIT: Kilian Pohl, Sandy Wells, Eric Grimson
* UNC: Martin Styner, Ipek Oguz, Guido Gerig, Xavier Barbero
* Utah: Ross Whitaker, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer
* GaTech: Allen Tannenbaum, John Melonakos, Tauseef ur Rehman, Shawn Lankton, Ramsey Al-Hakim, Eric Pichon, Delphine Nain, Oleg Michailovich, Yogesh Rathi, James Malcolm
* Isomics: Steve Pieper
* GE: Bill Lorensen, Jim Miller
* Kitware: Luis Ibanez, Karthik Krishnan,
* UCLA: Michael J. Pan, Jagadeeswaran Rajendiran
* BWH: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt
* Dartmouth: Andrew Saykin
* UCI: James Fallon

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Structural_Image_Analysis NA-MIC Projects on Structural Image Analysis].

=== 2.3 Functional MRI Analysis Theme ===

====Progress====

During this year, the focus of the algorithms and the engineering
cores has been on the structural and DTI analysis. While we continued
to expand the methods and the infrastructure in NAMIC-kit to support
fMRI analysis, as well as using the analysis tools to perform clinical
studies, the emphasis of the work this year has been on integrating
the fMRI analysis with other modalities and supporting other
modalities.

===== Clinical Studies =====

We would like to highlight several clinical studies within NAMIC that
focused on fMRI data and its relationship with other imaging
modalities:

* Imaging Phenotypes in Schizophrenics and Controls: Functional connectivity of the DLPFC by genotype was investigated emplying partial least squares (PLS) correlation analysis. PLS is a multivariate analytical technique used to summarize large neuroimaging data sets in such a way as to correlate patterns of activation with a variable(s) of interest (i.e., DLPFC activity). In the most recent analysis, the DRD1 genotype was used as a grouping variable. This analysis has been submitted for publication (Tura, Turner, Fallon, Kennedy, and Potkin. Genetic Impact on Functional Connectivity in Schizophrenics During a Working Memory Task). Working memory performance did not differ significantly between the two cohorts. However, imaging-genetic analysis showed a significant difference (P< 0.05) between the circuitry engaged by each group. Significance and reliability of the resulting imaging-behavioral patterns within each genotype were assessed by 200 bootstrap and 500 permutation tests, respectively. In one group, the circuitry included the temporal pole, the insula, the dorsolateral prefrontal cortex, and the Brodmann Areas (BA) 1,2,3,4,6,11 and 21, while the other group showed a network comprising the tectum, precuneus retroplenial, vermis, substantia nigra, BA 22,39,8, and 9. The DRD1 polymorphic site may characterize circuitry differences in schizophrenic patients.

*Path-Of-Interest Analysis (joint DTI/fMRI modeling): We collected preliminary data using an application of the “optimal path analysis”. In this analysis, we extracted group fMRI activation due to the Stroop effect (an attentional experiment where incongruent color, in which the word is written, competes with name of the color itself, activating areas responding to conflict monitoring and selection) separately for controls and schizophrenics. This resulted in three clusters of activation, one in the right Dorsolateral Prefrontal Cortex, a second in the Anterior Cingulate Gyrus, and a third in the Medial Parietal Lobe. In the next step, we placed activation clusters in each individual space, by reversing normalization parameters used during fMRI analysis. Finally, EPI fMRI scans were co-registered to DTI scans, and the same registration parameters were applied to activation maps (fMRI results). Regions of activation were used as start and destination points for optimal path analysis, which resulted in three separate paths of optimal connectivity for each subject. The probability of the connections were then calculated for each path and each subject, and compared between groups. In our preliminary analysis, we included 10 control subjects and 10 chronic schizophrenics. Our results demonstrated a relationship between Stroop Effect fMRI activation in the medial parietal area and optimal path connectivity between parietal and cingulate activation sites in schizophrenics (rho=-0.56; p=0.047), which was not observed in controls. These findings suggest that decreased connectivity may result in schizophrenics relying more on posterior parts of the executive attentional network during performance of the Stroop task.

*Hippocampal and frontal memory circuitry abnormalities in schizophrenia: Relation of diffusion, morphometric and fMRI markers: We performed a combined DTI, fMRI, and morphometric study on 13 patients with schizophrenia (SZ) and 14 HC. We identified areas of increased trace diffusivity (TD) in the hippocampal and insular regions as well as areas of reduced fractional anisotropy (FA) in left frontal white matter in SZ relative to HC (p<.01). Voxel based morphometry analyses in a subset of these subjects showed corresponding reductions in gray matter density in hippocampal and insular regions in patients relative to controls (p<.01). Analysis of fMRI results from the novel vs. repeated word contrast from the event-related auditory verbal episodic memory encoding/retrieval task in a subset of the subjects indicated reduced activation in frontal and temporal regions, as well as increased activation in posterior cingulate, retrosplenial, and thalamic regions in SZ relative to HC (p<.05). Further analysis showed that left frontal white matter FA was associated with activation in the left and right hippocampi as well as other frontal and temporal regions, but inversely related to activation in the retrosplenial/posterior cingulate region (p<.05). These initial findings indicate a pattern of relationships between of structural and functional brain abnormalities in schizophrenia and demonstrate the feasibility of integrated quantitative analyses across modalities.

===== Methods =====

During this year, we continued methodological development along two
directions:

*Improving fMRI detectors by incorporating Markov priors on the activation state. We integrated the improved detector into Slicer and performed substantial validation of the methods using fBIRN data provided by the UC Irvine group. Journal paper on the method has been submitted to IEEE TMI.

*Improving registration of EPI images to anatomical scans through modeling of the EPI distortions. We demonstrated an initial model that uses segmentation of the structural scan to predict the distortions in the EPI images. The preliminary results are quite encouraging.

==== Key Investigators ====

* MIT: Polina Golland, Sandy Wells, Wanmei Ou, Claire Poynton
* BWH: Martha Shenton, Marek Kubicki
* Dartmouth: Andy Saykin
* UCI: Jessica Turner, Stephen Potkin
* Toronto: Jim Kennedy

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#fMRI_Analysis NA-MIC Projects on fMRI Analysis].

=== 2.4 [[NA-MIC-Kit|NA-MIC Kit]] Theme ===

==== Progress ====
The continuing vision of the NA-MIC Kit is to provide an open source set of software tools and methodologies that will serve as the foundation for medical image computing projects for both academic and commercial use. Key elements of this vision are:

# ''Unrestrictive License.'' Users of the Kit are free to distribute their derived works under any license suitable to their needs.
# ''Cross Platform.'' This software set can be adapted to the best available price-performance computer systems for any particular use.
# ''Extensible Application Framework.'' New techniques and algorithms can be quickly integrated into a working system. Sophisticated user interfaces can be generated easily through automated processes. Sophisticated toolsets such as ITK, VTK, and KWWidgets are available to create and deploy applications quickly.
# ''Quality Software Process.'' Developers and users can rely on accurate and well documented behavior from all the parts of the Kit.
# ''Sustainable Community.'' Users are actively involved in the design process of the Kit. Documentation, training materials, and hands-on sessions are available and well publicized to the community.

====='''Slicer3'''=====

A major focus of the third year was the implementation of the Slicer3 in the NAMIC Kit. This effort addressed Item #3 above ''Extensible Application Framework.'' The previous two years of the NAMIC project, which entailed gathering requirements from Cores 1 and 3, and developing the computational foundation, toolsets, and software process, came together in the Slicer3 application platform.

Core 2 worked hard to insure that the Slicer 3 application serves, and will continue to serve, as a productive technology deployment platform. The application framework was designed carefully to provide ease-of-use, both in terms of interaction and software integration. Advanced capabilities, including the ability to launch large-scale grid computing, was designed into the application. Some of the key features of the Slicer3 application completed in the third year include the following.
* Advanced application framework including a tuned GUI for ease of use, undo/redo capabilities, 2D/3D view windows, and support for advanced interaction techniques such as 3D widgets. The application provides viewers for displaying slices, volumes, and models including the ability to edit properties. A built in transformation pipeline enables users to confidently import, edit and display data in a consistent coordinate system.
* The application is data-driven based on the next generation MRML scene description file format. Backward compatibility to Slicer 2.x MRML files is preserved.
* A module plug-in architecture and execution model that enables researchers to package and integrate their software into the Slicer3 framework. Plug-in modules can be implemented in a variety of programming languages, and are described using a simple XML description. These modules, when located and loaded into Slicer3, have the capability to automatically generate their user interface, which is seamlessly integrated into the Slicer3 GUI.
* Support for editing and marking data including support for fudicials, paint and draw editors.
* The creation of several simple plug-in modules, including the conversion of previous Slicer 2.x modules to the new Slicer3 architecture.

====='''EM Segment'''=====

As an application framework, Slicer3 provides tools for loading, viewing, measuring, editing, and saving data. To support advanced medical image analysis, plug-in modules are required in conjunction with the Slicer3 core. To demonstrate the capabilities of the framework we implemented the [http://www.na-mic.org/Wiki/index.php/Slicer3:EM EM Segment] module, a sophisticated and proven method for automatically segmenting complex anatomical structures.

To use this module, users specify parameters defining the image protocol and the anatomical structures of interests. This process results in a template that the module uses to automatically segment large data sets. The template is composed of atlas data and a non-trivial collection of parameters for the EM Segment algorithm ([[Media:Pohl-miccai-short-2005.pdf|Pohl et al.]]). Once the parameters are specified, the target images are segmented using the algorithm; if the results are satisfactory, the template is saved and can be used later to segment new images (via the GUI or batch processing). If the results are unsatisfactory, the parameters can be modified and the segmentation re-run.

Besides successfully demonstrating the use of complex algorithms in the Slicer3 framework, this effort also led us to develop tools, including modifications to the underlying KWWidgets GUI toolkit, to support module workflow. With these tools, it is possible to simplify complex modules by dividing the complicated template specification task into a number of smaller, intuitive steps. These steps are enforced by the GUI and reduce the potential for user error, while improving the overall user interface.

====='''Quality Software Process'''=====

Building on [http://lwn.net/Articles/188693/ last year's success with the KDE community], the NAMIC community continued to extend its world-class, open source software process tools CMake, DART, CTest and DART. These tools, which form the core of a quality-oriented, test-driven development (TDD) software process. In particular, the CPack system is now able to automatically package and distibute code, libraries, executables and installers across all of NAMIC's supported platforms (i.e., Linux, Windows, Mac). This enables the NAMIC developer community to rapidly deploy software tools to the user community.

==== Key Investigators ====

* GE: Bill Lorensen, Jim Miller, Xiaodong Tao, Dan Blezek
* Isomics: Steve Pieper, Alex Yarmarkovich
* Kitware: Will Schroeder, Luis Ibanez, Brad Davis, Andy Cedilnik, Sebastien Barre, Bill Hoffman
* UCLA: Mike Pan, Jagadeeswaran Rajendiran
* UCSD: Neil Jones, Jeffrey Grethe, Mark Ellisman
* BWH: Nicole Aucoin, Katie Hayes, Wendy Plesniak, Mike Halle, Gordon Kindlmann, Raul San Jose Estepar, Haiying Liu, Ron Kikinis
* MIT: Lauren O'Donnell, Kilian Pohl

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#NA-MIC_Kit NA-MIC Kit Projects].

== 3. Highlights ==
The third year of the NAMIC project saw continued development and dissemination of medical image analysis software. With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NAMIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NAMIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

=== 3.1 Advanced Algorithms ===
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NAMIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NAMIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NAMIC Sandbox in preparation for formal acceptance into the NAMIC Kit.

=== 3.2 Technology Deployment Platform: Slicer3 ===
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NAMIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NAMIC Kit, and are now available to all other modules built on the Slicer3 framework.

=== 3.3 Outreach and Technology Transfer ===
Cores 4-5-6 continue to support, train and dissemniate to the NAMIC community, and the broader biomedical computing community.
* NAMIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NAMIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair.

== 4. Impact and Value to Biocomputing ==

In NA-MIC's third year, it is evident that NA-MIC is developing a
culture, environment, and resources to foster and incite collaborative
research in medical image analysis that draws together mathematicians,
computer scientists, software engineers, and clinical
researchers. These artefacts of NA-MIC impact how NA-MIC
operates, make NA-MIC a fulcrum for NIH funded research,
and draws new collaborators from across the country
and around the world to NA-MIC.

=== 4.1 Impact within the Center ===

Within the center, the NA-MIC organization, NA-MIC processes, and the
NA-MIC calendar has permeated the research. The organization is
nimble, forming ad hoc distributed teams within and between cores to
address specific biocomputing tasks. Information is shared freely on
the NA-MIC Wiki, on the weekly Engineering telephone conferences, and
in the NA-MIC Subversion source code repository. The software
engineering tools of CMake, Dart 2 and CTest, CPack, and KWWidgets
facilitate a cross platform software environment for medical image
analysis that be easily built, tested, and distributed to
end-users. Core 2 has provided a platform, Slicer 3, that allows Core
1 to easily integrate new technology and deliver this technology in an
end user application to Core 3. Core 1 has developed a host of
techniques to apply to structural and diffusion analysis which are
under evaluation by Core 3. Major NA-MIC events, such as the annual
All Hands Meeting, the Summer Project Week, the Spring Algorithms
meeting, and Engineering Teleconferences are avidly attended by NA-MIC
researchers as opportunities to foster collaborations.

=== 4.2 Impact within NIH Funded Research ===

Within NIH funded research, NA-MIC continues to forge relationships
with other large NIH funded projects such as BIRN, caBIG, NAC, and
IGT. Here, we are sharing the NA-MIC culture, engineering practices,
and tools. BIRN hosts data for the NA-MIC researchers and NA-MIC hosts
BIRN wikis. caBIG lists the 3D Slicer among the applications available
on the National Cancer Imaging Archive. NAC and IGT use the NA-MIC
infrastructure and are involved in the development of the 3D
Slicer. BIRN recently held an event modeled after the NA-MIC Project
Week. NA-MIC has become a resource on open source licensing to the medical image analysis community.

NA-MIC is also attracting NIH funded collaborations. Two grants have
been funded under PAR-05-063 to collaborate with NA-MIC: ''Automated FE Mesh Development'' and ''Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI''. Five additional
applications to collaborate with NA-MIC via the NCBC collaborative
grant mechanism are under consideration. Additional grant applications
submitted under other calls are planning to use and extend the NA-MIC tools.

=== 4.3 National and International Impact ===

NA-MIC events and tools garner national and international interest.
There were nearly 100 participants at the NA-MIC All Hands Meeting in
January 2007, with many of these participants from outside of NA-MIC.
Several researchers from outside the NA-MIC community have attended
the Summer Project Weeks and the Winter Project Half-Weeks to gain
access to the NA-MIC tools and people. These external researchers are
contributing ideas and technology back into NA-MIC.

Components of the NA-MIC kit are used globally. The software
engineering tools of CMake, Dart 2 and CTest are used by many open
source projects and commercial applications. For example, the K Desktop Environment (KDE) for
Linux and Unix workstations uses CMake and Dart. KDE is one of the
largest open source projects in the world. Many open source projects and
commercial products are benefiting from the NA-MIC related
contributions to ITK and VTK. Finally, Slicer 3 is being used as an
image analysis platform in several fields outside of medical image
analysis, in particular, biological image analysis, astronomy, and industrial inspection.

NA-MIC co-sponsored the ''Workshop on Open Science'' at the Medical
Image Computing and Computer-Assisted Intervention (MICCAI) 2006
conference. The proceedings of the workshop are published on the
electronic Insight Journal, another NIH-funded activity.

Over 50 NA-MIC related publications have been produced since the inception of the center.

== 5.NA-MIC Timeline ==

This section provides a table of NAMIC timelines from the original proposal that graphically depicts completed tasks/goals in years 1, 2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines have also been described.

[[2007_Scientific_Report_Timeline|2007 Scientific Report Timeline]]

== Appendix A Publications ==

http://www.na-mic.org/Wiki/index.php/Publications

== Appendix B EAB Report ==

===EAB Report===
The NA-MIC External Advisory Board (EAB), chaired by Professor Chris Johnson of the University of Utah, met at the annual All-Hands Meeting. After individual presentations by NA-MIC investigators and open as well as closed-door EAB discussion, the Board provided its independent expert assessment of the Center.

[https://share.spl.harvard.edu/grants/u54/2007/APR%202007/NAMIC-EAB-Report07-ver5.doc This is a password protected link to the EAB report.]

===Response to EAB Report===

The NA-MIC leadership discussed the excellent suggestions made by the EAB, and in the following section we respond to each of them.

''EAB: The Center should actively pursue methods to distribute their software and other resources to a broader community. As we noted in last year’s EAB Report, the Center is doing a good job at distributing their software toolkit, however, largely the distribution has been focused internally to NA-MIC collaborators. The EAB recommends that the Center work to more broadly distribute its software to the larger community of neuroimaging researchers. In addition, because the Center’s software tools will also be useful to biomedical researchers outside of neuroimaging, the EAB recommends the Center consider additional methods to “get the word out” about their resources.''

NA-MIC: The focus in the last two years of the Center has been to broaden the outreach effort to the neuroimaging community and to other biomedical researchers outside of this community. Many algorithms have been developed in ITK/VTK, and then ported to Slicer to try to make them as accessible as possible. We believe that the ITK/VTK platforms have been quite widely disseminated. However, as the EAB has noted, the current training and dissemination funds are limited, and the Center leadership will continue to look for creative solutions to get the word out within these resource constraints.

''EAB: The Center is well suited to assess and compare algorithms. An important question in image analysis involves understanding which image-processing algorithm is best suited for a particular task. Given the large number of different image analysis algorithms being created by NA-MIC, the Center is in a good position to evaluate the effectiveness of different algorithms and to make appropriate recommendations.''

NA-MIC: This topic has been discussed extensively within the Center, and we have addressed this issue in a manner that is aligned with our core expertise in open source software. More specifically, we will continue to build upon our strength and provide infrastructure and tools needed for open science, as well as to train researchers in the use of these tools. It is our hope that by providing easy access to these tools, we will be facilitating the process of comparison between algorithms for various biomedical tasks. This being said, many times there is no optimal algorithm for every imaging purpose. A certain segmentation routine may be excellent for one purpose, but not for another. This is a well recognized problem in image processing and computer vision, and not a fundamental limitation of medical imaging. Comparisons, however, among different algorithms to determine which algorithms are best for which questions being addressed is nonetheless important and we have made some inroads here. Sylvain Bouix, for example, has a paper in press in Neuroimage on evaluating brain tissue classifiers without ground truth, which compares several different segmentation algorithms. Also, in the shape analysis field, every NA-MIC related publication performs the necessary comparisons. Furthermore, Martin Styner and coworkers (Core 1 and 3 investigators) authored a paper on shape analysis comparisons at ISBI 2006. In addition, Martin is co-organizing a caudate and liver segmentation comparison workshop at MICCAI 2007, with currently over 35 registered participating methods. Our philosophy is to make a large menu of algorithmic methodologies available in as easy to use form as possible, so as to encourage the kind of comparisons recently done by Drs. Bouix and Styner.

''EAB: The Center should consider additional techniques to validate and verify techniques and software. The EAB was pleased to see the progress the Center made in quantifying and validating their algorithms and implementations within the NA-MIC Kit. However, because this is such an important issue, the EAB recommends the Center continue to make progress in this area.
''

NA-MIC: The Center intends to continue its effort in validation and verification of its software implementations. Center researchers have also come up with original techniques for validation, e.g., the Laplacian method for comparing the accuracy of segmentations. These validation techniques have been used to drive new original algorithmic as well. In addition, work within the Center has continued to compare manual measures of brain structure, i.e., the caudate, as a gold standard for automated measures of caudate in the same subjects. This kind of validation will continue.

''EAB: The Center should generate a set of success criteria. The EAB recommends that the Center create a set of benchmarks of success that it will use in future years to measure the Center’s success and as ways to evaluate the Center’s continuing progress and at the time of the Center’s first renewal.''

NA-MIC: We note that we have established benchmarks for success that are similar to criteria used by study sections for reviewing the success of other grant mechanisms, although we also include criteria for success that is specific to NA-MIC, including:

• Papers in peer reviewed publications

• Technology transfers from Core 1 to Core 3 researchers

• Software released as part of the NA-MIC Kit

• Training outside researchers in the use of the NA-MIC Kit

= Logistics =

== Schedule and process for preparation of this report ==

* March 30 - Assign section/theme leads (Ron). Last year: introduction (marty), structural analysis (allen, martin), dti (guido, marty/marek), fmri (polina, andy), namic kit(bill), timeline (ross), highlights (will), impact (bill).
*April 7,10 - tcons with Marty, Ross, Will, Tina to finalize the layout, process, and timeline of report.
* April 13 - update projects list using last year's [[NA-MIC_Collaborations]] and [[2007_Project_Half_Week#Projects|projects pursued at the half week in SLC]]. Remind investigators to update individual pages. (Tina)
* April 23- complete project description pages in updated list: [[NA-MIC_Collaborations]] (all project owners).
* April 30 - complete section summaries, introduction, highlights, impact, timeline (owners of these topics)
* May 8 - submit wiki report to NA-MIC editor, Ann (Tina)
* May 17 - submit Edited report to Rachana in pdf format (Ann)
* May 25 - submit final report, if changes are needed (Ann)
* May 29 - ship final package to NIH (Rachana)

== Guidelines from NIH Program Officer ==
===NIH Reviewer Comments on Yr2 APR (July 2006)===
The following additional guidelines were provided by the NIH progress review committee after they reviewed the 2006 report:

1. Comment on obstacles and challenges encountered during the year.

2. Comment on outcomes of DBPs.

3. Create a context for the report in the Introduction.

4. Comment on how all the themes as a whole are moving forward.

5. Full citations should be given within the report as footnotes or in a bibliography, not just as hyperlinks.

6. Explain time-lines with text including reasons for delays. Link timelines to papers and citations.

7. Define terms used in the timeline to describe the amount of progress being made, especially with respect to software development.

8. Put priority weights on completed tasks within timeline. Note if a task is a stepping stone or a final goal.

9. Make sure the projects presented in the annual report are also listed in the project pages of the NAMIC Wiki and vice versa.

10. In the budget pages, please elaborate on the number and locations for travel.

===Feb 2006===
The following guidelines were provided by Grace Peng, NA-MIC program officer, in Feb 2006.

The key is to synthesize all the individual elements into bigger picture stories that really speak of each area’s impact to the community.

The specialized scientific report should have the following format:

# Introductory page describing the new grouping of NAMIC project themes.
# A description of progress in each NAMIC project theme (not to exceed 2 pages each), tying together relevant activities from participating subcomponents and referencing cores in parentheses.
# A table of NAMIC timelines (from original proposal), graphically depicting completed tasks/goals in years 1,2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines should be described.
# A description of 3 highlights selected from all NAMIC projects to showcase NAMIC.
# A discussion of NAMIC’s impact and value to the biocomputing community this year.

2007 Annual Scientific Report

2007-05-20T17:05:35Z

Shenton: /* Response to EAB Report */

Back to [[2007_Progress_Report]]

For reference:

*[[2006_Progress_Report]]

*[[Media:2005_NAMIC_Specialized_ProgReport.pdf|2005 Annual Scientific Report]]

== 1. Introduction ==

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with espect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs continue to include neuropsychiatric disorders such as Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Schizophrenia (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a adopting a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

(Please note that this report is available on the NA-MIC wiki at the following url: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report)

== 2. Four Main Themes ==

This year's activities focus on four main themes: Diffusion Image Analysis, Structural Analysis, Functional MRI Analysis, and the NA-MIC Kit. Each of the following sections begins with an overview of the theme, provides a progress update and list of key investigators, and concludes with a set of links to additional information for individual projects in that theme.

These thematic activities involve scientists from each of the 7 NA-MIC cores (Appendix).

* [http://www.na-mic.org/Wiki/index.php/Algorithm:Main Core 1 Algorithms]-Ross Whitaker PI
* [http://www.na-mic.org/Wiki/index.php/Engineering:Main Core 2 Engineering]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/DBP:Main Core 3 Driving Biological Problems] DBP1-Martha Shenton PI / DBP2-Andy Saykin PI / DBP3-Steven Potkin PI
* [http://www.na-mic.org/Wiki/index.php/Service:Main Core 4 Service]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/Training:Main Core 5 Training]-Randy Gollub PI
* [http://www.na-mic.org/Wiki/index.php/Dissemination:Main Core 6 Dissemination]-Tina Kapur Co-PI; Steve Pieper Co-PI
* [http://www.na-mic.org/Wiki/index.php/Leadership:Main Core 7 Leadership]-Ron Kikinis

=== 2.1 Diffusion Image Analysis Theme ===

==== Progress ====
Over the past year, we have continued to develop a number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical diffusion tensor analysis. These tools have been already integrated into diffusion dedicated software (e.g., Fiber Viewer-UNC). In addition, we started developing tools for group data analysis, such as white matter atlases, and tools for data integration, that would combine and interface among different imaging modalities (such as structural, fMRI and DTI), to better estimate anatomical and functional connectivity. This software has continued to be used in multiple clinical projects involving several psychiatric populations. Below we provide detailed progress in the area of diffusion image analysis.

==== Fiber Tract Extraction and Analysis ====
* Since last year, all of the algorithms for fiber tractography and anisotropy estimation have been implemented in both the “Fiber Viewer” and “Slicer” packages, and now the resulting methods are being applied to clinical studies. Fiber Tract integrity has been investigated and results have been presented at several international conferences for four key fiber tracts in schizophrenia (corpus callosum, fornix, inferior occipito-frontal fasciculus, and uncinate fasciculus). These tracts have been extracted, and fractional anisotropy has been compared between groups, demonstrating fronto-temporal connectivity abnormalities in schizophrenia.

* In addition to clinical studies, teams have been working on other methods to define and estimate brain connectivity. The most important developments in this regard included volumentric connectivity and stochastic tractography measures. For volumentric connectivity, a PDE-based approach to white matter connectivity from DTI has been developed, that is founded on the principal of minimal paths through the tensor volume. This method computes a volumetric representation of a white matter tract given two endpoint regions. In addition, statistical methods for quantifying the full tensor data along these pathways have been also developed. Directional PDE-based flows have been proposed and implemented for a similar purpose. Further, an approach called "stochastic tractography," has been developed to calculate probability of two regions being connected by the fiber tract. This should be especialy useful when tract has to go through the regions characterized by low diffusion anisotropy.

* Some initial attempts have been made to population based analysis of DT-MRI. One method is based on the unbiased non-rigid registration of a population to a common coordinate system. The registration jointly produces an average DTI atlas, which is unbiased with respect to the choice of a template image, along with a diffeomorphic correspondence for each image. The anatomically significant correspondence provides a basis for comparison of tensor features and fiber tract geometry in clinical studies.

==== Fractional Anisotropy Analysis ====
* We have used tools developed last year, and applied them to our population of chronic schizophrenia subjects in order to investigate two fiber tracts: the cingulum bundle and the corpus callosum. In the case of cingulum bundle, manually drawn regions of interest and Finsler geometry were used to extract entire cingulum bundle fiber tract, and FA was estimated along the tract, and compared between groups. Corpus Callosum cross sectional area and its probabilistic subdivisions were determined automatically from the structural MRI scans using a model based deformable contour segmentation. The structural scan was then co-registered with the DTI scan and the anatomical corpus callosum subdivisions were propagated to the associated FA map, and compared between groups, demonstrating deficient interhemispheric communication in schizophrenia.

==== Integration of fMRI and DTI, Path-of-Interest Analysis ====
* Progress has been also made in the development of a tool that would successfully combine and integrate functional and anatomical information. The Optimal Path Analysis method has been applied to the Harvard- BWH fMRI dataset, and anatomical connections between regions active during the fMRI experiment have been extracted for each subject. The connectivity has calculated and compared between groups. This tool is now being ported to Slicer3.

==== Key Investigators ====

* BWH: Martha Shenton, Marek Kubicki, Marc Niethammer, Sylvain Bouix, Jennifer Fitzsimmons, Katarina Quintis, Doug Markant, Kate Smith, Georgia Bushell, Mark Dreusicke, Carl-Fredrik Westin, Raul San Jose, Gordon Kindlmann
* MGH: Bruce Fischl, Denis Jen, David Kennedy
* MIT: Lauren O'Donnell, Polina Golland, Tri Ngo
* UCI: James Fallon, Martina Panzenboeck
* UNC: Guido Gerig, Isabelle Corouge, Casey Goodlett, Martin Styner
* Utah: Tom Fletcher, Ross Whitaker, Saurav Basu, Davis McKay
* GA Tech: Eric Pichon, John Melonakos, Xavier LaFaucheur, Vandana Mohan, Allen Tannenbaum
* Dartmouth: John West, Andrew Saykin, Laura Flashman, Paul Wang, Heather Pixley, Robert Roth
* Isomics: Steve Pieper
* Kitware: Luis Ibanez

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Diffusion_Image_Analysis NA-MIC Projects on Diffusion Image Analysis].

=== 2.2 Structural Analysis Theme ===

==== Progress ====

Within the NAMIC's structural analysis theme, the main topics of interest are structural segmentation, registration and shape analysis. These topics are of course intertwined, e.g., segmentation or registration can directly deliver structural correspondence used in shape analysis, or in turn shape modeling is necessary for good structural segmentations. Here is a selection of progress highlights in the structural analysis theme

====='''Segmentation'''=====

* Wavelet based structural segmentation: We have developed a spherical wavelet based framework for the segmentation of selected brain structures, such as the hippocampus or the caudate nucleus. An automated segmentation of such structures must be highly accurate and include high frequency variations in the surface. Since shape representation is a key component of the segmentation, it must be rich enough to express shape variations at various frequency levels, from low harmonics to sharp edges. Medical object segmentation with deformable models and statistical shape modeling may be combined to obtain a more robust and accurate segmentation. To address this issue, a decomposable spherical wavelet based shape representation targeted to the population seems natural, where the shape parameters are separated into groups that describe independent global and/or local biological variations in the population, and a prior induced over each group explicitly encodes these variations. This work presents three novel contributions for shape representation, multiscale prior probability estimation and segmentation.

* Directional based segmentation: We have proposed an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. In the classical case, the Euclidean metric is locally multiplied by a scalar conformal factor (based on image information) such that the weighted length of curves lying on points of interest (typically edges) is small. We propose to add directionality to the factor, and show that one gets a well-defined minimization problem in the case that the factor defines a Finsler metric. Optimal curves may be obtained using the calculus of variations or dynamic programming. This methodology also makes connections to the important technique of graph-cuts.

* Statistical PDE methods for segmentation in shape space: This past year, we have proposed another method to perform shape-driven segmentation. In our approach, shapes are represented using binary maps, and linear PCA is utilized to provide shape priors for segmentation. Intensity based probability distributions are then employed to convert the given volume into a binary map representation, and a new energy functional is formulated whose minimization is performed using a parametric model for surface evolution in the shape space. Our algorithm is then applied to the segmentation of brain caudate nucleus and hippocampus from MRI data. Our validation shows that the proposed algorithm outperforms the log-likelihood based energy, converges in less than 5 iterations and is very obust to initialization. The overall algorithm is illustrates the potential for segmentation in shape space.

* Rule-based segmentation methods: We have continued this past year to develop segmentation methods based on heuristic rules provided to us by our Core 3 partners for segmenting various brain regions of interest in schizophrenia, e.g. the DLPFC and the striatum. The idea is to try to semi-automate these rules in order to forge an interactive tool for segmentation which can greatly shorten the time necessary for manual segmentation. Typically, these methods are used in conjunction with some Bayesian classifier which further aids to automating and in speeding up the given segmentation methodology.

* Tissue and structural segmentation via EM Segmenter: Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases segmentation is mostly performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We have developed an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior knowledge. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. The mapping between atlas and image space, structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the Maximum A posteriori Probability (MAP) estimation problem. These results are then fed into an Active Mean Field approach, which views the results as priors to a Mean Field approximation with a curve length prior. This segmentation framework has been ported into the NAMIC-toolkit and a first version of the tool is distributed with Slicer 3.

====='''Shape Analysis'''=====

* Wavelet based shape analysis: A shape representation that encodes variations at multiple scales can be useful as a rich feature set for shape analysis and classification. Combining tools from the existing shape analysis toolset, we extended it to include spherical wavelet coefficients (SWC) as features and compared the results obtained to shape analysis using a SPHARM-PDM representation. The rich SWC feature set allows the differentiation of shape differences at various scales as well as highly correlates with the existing SPHARM-PDM analysis but with increased statistical sensitivity. Wavelet coefficient shrinkage and dimension reduction are well-understood and have been widely researched for traditional types of wavelet decompositions but not much explored for the second generation wavelets. During the past year, we have developed a Bayesian model on our specific wavelet structure based on a population of surfaces. For each shape, the deviation from the mean is computed and is modeled as the sum of an unknown signal and a noise. This deviation is encoded by the wavelet transform and our goal is to estimate the wavelet coefficients belonging to the noiseless signal.

* Surface flattening for shape analysis: Flattened representations of undulated surfaces constitute an active area of research in the field of medical imaging and visualization, due to their extensive use for registration and shape analysis of various structures of interest. We have presented a method for flattening anatomical surfaces in an area preserving manner, while minimizing the geometrical distortion. This method is based on the theory of optimal mass transport and conformal mapping of surfaces. The key idea here is the use of a multiresolution scheme for the solution of optimal mass transport gradient descent equations which allows a fast and stable solution for optimal transport. The method has been implemented on a GPU, allowing us to flatten a 128 by 128 by 128 volume in about 5 seconds on a standard workstation.

* Curvature based population correspondence: We have extended the Minimum Description Length population based correspondence framework to include curvature based measurements, such as the Koenderink Shape Index S and Curvedness C in combination with the standard location information. Current methodology in population based correspondence is based mainly on minimizing distribution properties of surface point locations and are thus not invariant to alignment. We have favorably compared our combined "Curvature + Location" MDL to the standard MDL, as well as to the SPHARM approach, in complex structures, such as the striatal brain structure (composed of caudate, nucleus accumbens and putamen).

* Shape analysis toolset: A considerable amount of work was spent on the development aspect of the shape analysis tools. The distributed set of tools is continuously enhanced and the population based correspondence framework has been released as open source. All the tools including the visualization tool, KWMeshVisu, can be called directly from Slicer 3. The visualization tool allows the overlay of scalar, vector and ellipsoid data onto surfaces via versatile colormaps. The attributed surfaces are then visible within Slicer 3. This lean visualization tool fills a niche and is also used in our cortical thickness analysis tool. Also, while it is entirely possible to run all shape analysis steps by calling the individual modules, this is highly inefficient in a larger study. As a result we are developing a separate shape pipeline Slicer module that uses Batchmake to run the shape analysis pipeline as a distributed, background process. The whole shape analysis pipeline will thus become entirely encapsulated and accessible to the trained clinical collaborator.

* Particle based correspondence: We have developed a method for a multi-object correspondence optimization, and have applied it successfully to a proof-of-concept application for the analysis of brain structure complexes from a longitudinal study of pediatric autism. This new method for constructing compact statistical point-based models of ensembles of similar shapes does not rely on any specific surface parameterization, requires little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including non-manifold surfaces and objects of arbitrary topology. The method uses a dynamic particle system to optimize correspondence point positions across all structures by simultaneously maximizing both the geometric accuracy and the statistical simplicity of the model.

==== Key Investigators ====

* MIT: Kilian Pohl, Sandy Wells, Eric Grimson
* UNC: Martin Styner, Ipek Oguz, Guido Gerig, Xavier Barbero
* Utah: Ross Whitaker, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer
* GaTech: Allen Tannenbaum, John Melonakos, Tauseef ur Rehman, Shawn Lankton, Ramsey Al-Hakim, Eric Pichon, Delphine Nain, Oleg Michailovich, Yogesh Rathi, James Malcolm
* Isomics: Steve Pieper
* GE: Bill Lorensen, Jim Miller
* Kitware: Luis Ibanez, Karthik Krishnan,
* UCLA: Michael J. Pan, Jagadeeswaran Rajendiran
* BWH: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt
* Dartmouth: Andrew Saykin
* UCI: James Fallon

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Structural_Image_Analysis NA-MIC Projects on Structural Image Analysis].

=== 2.3 Functional MRI Analysis Theme ===

====Progress====

During this year, the focus of the algorithms and the engineering
cores has been on the structural and DTI analysis. While we continued
to expand the methods and the infrastructure in NAMIC-kit to support
fMRI analysis, as well as using the analysis tools to perform clinical
studies, the emphasis of the work this year has been on integrating
the fMRI analysis with other modalities and supporting other
modalities.

===== Clinical Studies =====

We would like to highlight several clinical studies within NAMIC that
focused on fMRI data and its relationship with other imaging
modalities:

* Imaging Phenotypes in Schizophrenics and Controls: Functional connectivity of the DLPFC by genotype was investigated emplying partial least squares (PLS) correlation analysis. PLS is a multivariate analytical technique used to summarize large neuroimaging data sets in such a way as to correlate patterns of activation with a variable(s) of interest (i.e., DLPFC activity). In the most recent analysis, the DRD1 genotype was used as a grouping variable. This analysis has been submitted for publication (Tura, Turner, Fallon, Kennedy, and Potkin. Genetic Impact on Functional Connectivity in Schizophrenics During a Working Memory Task). Working memory performance did not differ significantly between the two cohorts. However, imaging-genetic analysis showed a significant difference (P< 0.05) between the circuitry engaged by each group. Significance and reliability of the resulting imaging-behavioral patterns within each genotype were assessed by 200 bootstrap and 500 permutation tests, respectively. In one group, the circuitry included the temporal pole, the insula, the dorsolateral prefrontal cortex, and the Brodmann Areas (BA) 1,2,3,4,6,11 and 21, while the other group showed a network comprising the tectum, precuneus retroplenial, vermis, substantia nigra, BA 22,39,8, and 9. The DRD1 polymorphic site may characterize circuitry differences in schizophrenic patients.

*Path-Of-Interest Analysis (joint DTI/fMRI modeling): We collected preliminary data using an application of the “optimal path analysis”. In this analysis, we extracted group fMRI activation due to the Stroop effect (an attentional experiment where incongruent color, in which the word is written, competes with name of the color itself, activating areas responding to conflict monitoring and selection) separately for controls and schizophrenics. This resulted in three clusters of activation, one in the right Dorsolateral Prefrontal Cortex, a second in the Anterior Cingulate Gyrus, and a third in the Medial Parietal Lobe. In the next step, we placed activation clusters in each individual space, by reversing normalization parameters used during fMRI analysis. Finally, EPI fMRI scans were co-registered to DTI scans, and the same registration parameters were applied to activation maps (fMRI results). Regions of activation were used as start and destination points for optimal path analysis, which resulted in three separate paths of optimal connectivity for each subject. The probability of the connections were then calculated for each path and each subject, and compared between groups. In our preliminary analysis, we included 10 control subjects and 10 chronic schizophrenics. Our results demonstrated a relationship between Stroop Effect fMRI activation in the medial parietal area and optimal path connectivity between parietal and cingulate activation sites in schizophrenics (rho=-0.56; p=0.047), which was not observed in controls. These findings suggest that decreased connectivity may result in schizophrenics relying more on posterior parts of the executive attentional network during performance of the Stroop task.

*Hippocampal and frontal memory circuitry abnormalities in schizophrenia: Relation of diffusion, morphometric and fMRI markers: We performed a combined DTI, fMRI, and morphometric study on 13 patients with schizophrenia (SZ) and 14 HC. We identified areas of increased trace diffusivity (TD) in the hippocampal and insular regions as well as areas of reduced fractional anisotropy (FA) in left frontal white matter in SZ relative to HC (p<.01). Voxel based morphometry analyses in a subset of these subjects showed corresponding reductions in gray matter density in hippocampal and insular regions in patients relative to controls (p<.01). Analysis of fMRI results from the novel vs. repeated word contrast from the event-related auditory verbal episodic memory encoding/retrieval task in a subset of the subjects indicated reduced activation in frontal and temporal regions, as well as increased activation in posterior cingulate, retrosplenial, and thalamic regions in SZ relative to HC (p<.05). Further analysis showed that left frontal white matter FA was associated with activation in the left and right hippocampi as well as other frontal and temporal regions, but inversely related to activation in the retrosplenial/posterior cingulate region (p<.05). These initial findings indicate a pattern of relationships between of structural and functional brain abnormalities in schizophrenia and demonstrate the feasibility of integrated quantitative analyses across modalities.

===== Methods =====

During this year, we continued methodological development along two
directions:

*Improving fMRI detectors by incorporating Markov priors on the activation state. We integrated the improved detector into Slicer and performed substantial validation of the methods using fBIRN data provided by the UC Irvine group. Journal paper on the method has been submitted to IEEE TMI.

*Improving registration of EPI images to anatomical scans through modeling of the EPI distortions. We demonstrated an initial model that uses segmentation of the structural scan to predict the distortions in the EPI images. The preliminary results are quite encouraging.

==== Key Investigators ====

* MIT: Polina Golland, Sandy Wells, Wanmei Ou, Claire Poynton
* BWH: Martha Shenton, Marek Kubicki
* Dartmouth: Andy Saykin
* UCI: Jessica Turner, Stephen Potkin
* Toronto: Jim Kennedy

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#fMRI_Analysis NA-MIC Projects on fMRI Analysis].

=== 2.4 [[NA-MIC-Kit|NA-MIC Kit]] Theme ===

==== Progress ====
The continuing vision of the NA-MIC Kit is to provide an open source set of software tools and methodologies that will serve as the foundation for medical image computing projects for both academic and commercial use. Key elements of this vision are:

# ''Unrestrictive License.'' Users of the Kit are free to distribute their derived works under any license suitable to their needs.
# ''Cross Platform.'' This software set can be adapted to the best available price-performance computer systems for any particular use.
# ''Extensible Application Framework.'' New techniques and algorithms can be quickly integrated into a working system. Sophisticated user interfaces can be generated easily through automated processes. Sophisticated toolsets such as ITK, VTK, and KWWidgets are available to create and deploy applications quickly.
# ''Quality Software Process.'' Developers and users can rely on accurate and well documented behavior from all the parts of the Kit.
# ''Sustainable Community.'' Users are actively involved in the design process of the Kit. Documentation, training materials, and hands-on sessions are available and well publicized to the community.

====='''Slicer3'''=====

A major focus of the third year was the implementation of the Slicer3 in the NAMIC Kit. This effort addressed Item #3 above ''Extensible Application Framework.'' The previous two years of the NAMIC project, which entailed gathering requirements from Cores 1 and 3, and developing the computational foundation, toolsets, and software process, came together in the Slicer3 application platform.

Core 2 worked hard to insure that the Slicer 3 application serves, and will continue to serve, as a productive technology deployment platform. The application framework was designed carefully to provide ease-of-use, both in terms of interaction and software integration. Advanced capabilities, including the ability to launch large-scale grid computing, was designed into the application. Some of the key features of the Slicer3 application completed in the third year include the following.
* Advanced application framework including a tuned GUI for ease of use, undo/redo capabilities, 2D/3D view windows, and support for advanced interaction techniques such as 3D widgets. The application provides viewers for displaying slices, volumes, and models including the ability to edit properties. A built in transformation pipeline enables users to confidently import, edit and display data in a consistent coordinate system.
* The application is data-driven based on the next generation MRML scene description file format. Backward compatibility to Slicer 2.x MRML files is preserved.
* A module plug-in architecture and execution model that enables researchers to package and integrate their software into the Slicer3 framework. Plug-in modules can be implemented in a variety of programming languages, and are described using a simple XML description. These modules, when located and loaded into Slicer3, have the capability to automatically generate their user interface, which is seamlessly integrated into the Slicer3 GUI.
* Support for editing and marking data including support for fudicials, paint and draw editors.
* The creation of several simple plug-in modules, including the conversion of previous Slicer 2.x modules to the new Slicer3 architecture.

====='''EM Segment'''=====

As an application framework, Slicer3 provides tools for loading, viewing, measuring, editing, and saving data. To support advanced medical image analysis, plug-in modules are required in conjunction with the Slicer3 core. To demonstrate the capabilities of the framework we implemented the [http://www.na-mic.org/Wiki/index.php/Slicer3:EM EM Segment] module, a sophisticated and proven method for automatically segmenting complex anatomical structures.

To use this module, users specify parameters defining the image protocol and the anatomical structures of interests. This process results in a template that the module uses to automatically segment large data sets. The template is composed of atlas data and a non-trivial collection of parameters for the EM Segment algorithm ([[Media:Pohl-miccai-short-2005.pdf|Pohl et al.]]). Once the parameters are specified, the target images are segmented using the algorithm; if the results are satisfactory, the template is saved and can be used later to segment new images (via the GUI or batch processing). If the results are unsatisfactory, the parameters can be modified and the segmentation re-run.

Besides successfully demonstrating the use of complex algorithms in the Slicer3 framework, this effort also led us to develop tools, including modifications to the underlying KWWidgets GUI toolkit, to support module workflow. With these tools, it is possible to simplify complex modules by dividing the complicated template specification task into a number of smaller, intuitive steps. These steps are enforced by the GUI and reduce the potential for user error, while improving the overall user interface.

====='''Quality Software Process'''=====

Building on [http://lwn.net/Articles/188693/ last year's success with the KDE community], the NAMIC community continued to extend its world-class, open source software process tools CMake, DART, CTest and DART. These tools, which form the core of a quality-oriented, test-driven development (TDD) software process. In particular, the CPack system is now able to automatically package and distibute code, libraries, executables and installers across all of NAMIC's supported platforms (i.e., Linux, Windows, Mac). This enables the NAMIC developer community to rapidly deploy software tools to the user community.

==== Key Investigators ====

* GE: Bill Lorensen, Jim Miller, Xiaodong Tao, Dan Blezek
* Isomics: Steve Pieper, Alex Yarmarkovich
* Kitware: Will Schroeder, Luis Ibanez, Brad Davis, Andy Cedilnik, Sebastien Barre, Bill Hoffman
* UCLA: Mike Pan, Jagadeeswaran Rajendiran
* UCSD: Neil Jones, Jeffrey Grethe, Mark Ellisman
* BWH: Nicole Aucoin, Katie Hayes, Wendy Plesniak, Mike Halle, Gordon Kindlmann, Raul San Jose Estepar, Haiying Liu, Ron Kikinis
* MIT: Lauren O'Donnell, Kilian Pohl

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#NA-MIC_Kit NA-MIC Kit Projects].

== 3. Highlights ==
The third year of the NAMIC project saw continued development and dissemination of medical image analysis software. With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NAMIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NAMIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

=== 3.1 Advanced Algorithms ===
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NAMIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NAMIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NAMIC Sandbox in preparation for formal acceptance into the NAMIC Kit.

=== 3.2 Technology Deployment Platform: Slicer3 ===
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NAMIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NAMIC Kit, and are now available to all other modules built on the Slicer3 framework.

=== 3.3 Outreach and Technology Transfer ===
Cores 4-5-6 continue to support, train and dissemniate to the NAMIC community, and the broader biomedical computing community.
* NAMIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NAMIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair.

== 4. Impact and Value to Biocomputing ==

In NA-MIC's third year, it is evident that NA-MIC is developing a
culture, environment, and resources to foster and incite collaborative
research in medical image analysis that draws together mathematicians,
computer scientists, software engineers, and clinical
researchers. These artefacts of NA-MIC impact how NA-MIC
operates, make NA-MIC a fulcrum for NIH funded research,
and draws new collaborators from across the country
and around the world to NA-MIC.

=== 4.1 Impact within the Center ===

Within the center, the NA-MIC organization, NA-MIC processes, and the
NA-MIC calendar has permeated the research. The organization is
nimble, forming ad hoc distributed teams within and between cores to
address specific biocomputing tasks. Information is shared freely on
the NA-MIC Wiki, on the weekly Engineering telephone conferences, and
in the NA-MIC Subversion source code repository. The software
engineering tools of CMake, Dart 2 and CTest, CPack, and KWWidgets
facilitate a cross platform software environment for medical image
analysis that be easily built, tested, and distributed to
end-users. Core 2 has provided a platform, Slicer 3, that allows Core
1 to easily integrate new technology and deliver this technology in an
end user application to Core 3. Core 1 has developed a host of
techniques to apply to structural and diffusion analysis which are
under evaluation by Core 3. Major NA-MIC events, such as the annual
All Hands Meeting, the Summer Project Week, the Spring Algorithms
meeting, and Engineering Teleconferences are avidly attended by NA-MIC
researchers as opportunities to foster collaborations.

=== 4.2 Impact within NIH Funded Research ===

Within NIH funded research, NA-MIC continues to forge relationships
with other large NIH funded projects such as BIRN, caBIG, NAC, and
IGT. Here, we are sharing the NA-MIC culture, engineering practices,
and tools. BIRN hosts data for the NA-MIC researchers and NA-MIC hosts
BIRN wikis. caBIG lists the 3D Slicer among the applications available
on the National Cancer Imaging Archive. NAC and IGT use the NA-MIC
infrastructure and are involved in the development of the 3D
Slicer. BIRN recently held an event modeled after the NA-MIC Project
Week. NA-MIC has become a resource on open source licensing to the medical image analysis community.

NA-MIC is also attracting NIH funded collaborations. Two grants have
been funded under PAR-05-063 to collaborate with NA-MIC: ''Automated FE Mesh Development'' and ''Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI''. Five additional
applications to collaborate with NA-MIC via the NCBC collaborative
grant mechanism are under consideration. Additional grant applications
submitted under other calls are planning to use and extend the NA-MIC tools.

=== 4.3 National and International Impact ===

NA-MIC events and tools garner national and international interest.
There were nearly 100 participants at the NA-MIC All Hands Meeting in
January 2007, with many of these participants from outside of NA-MIC.
Several researchers from outside the NA-MIC community have attended
the Summer Project Weeks and the Winter Project Half-Weeks to gain
access to the NA-MIC tools and people. These external researchers are
contributing ideas and technology back into NA-MIC.

Components of the NA-MIC kit are used globally. The software
engineering tools of CMake, Dart 2 and CTest are used by many open
source projects and commercial applications. For example, the K Desktop Environment (KDE) for
Linux and Unix workstations uses CMake and Dart. KDE is one of the
largest open source projects in the world. Many open source projects and
commercial products are benefiting from the NA-MIC related
contributions to ITK and VTK. Finally, Slicer 3 is being used as an
image analysis platform in several fields outside of medical image
analysis, in particular, biological image analysis, astronomy, and industrial inspection.

NA-MIC co-sponsored the ''Workshop on Open Science'' at the Medical
Image Computing and Computer-Assisted Intervention (MICCAI) 2006
conference. The proceedings of the workshop are published on the
electronic Insight Journal, another NIH-funded activity.

Over 50 NA-MIC related publications have been produced since the inception of the center.

== 5.NA-MIC Timeline ==

This section provides a table of NAMIC timelines from the original proposal that graphically depicts completed tasks/goals in years 1, 2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines have also been described.

[[2007_Scientific_Report_Timeline|2007 Scientific Report Timeline]]

== Appendix A Publications ==

http://www.na-mic.org/Wiki/index.php/Publications

== Appendix B EAB Report ==

===EAB Report===
The NA-MIC External Advisory Board (EAB), chaired by Professor Chris Johnson of the University of Utah, met at the annual All-Hands Meeting. After individual presentations by NA-MIC investigators and open as well as closed-door EAB discussion, the Board provided its independent expert assessment of the Center.

[https://share.spl.harvard.edu/grants/u54/2007/APR%202007/NAMIC-EAB-Report07-ver5.doc This is a password protected link to the EAB report.]

===Response to EAB Report===

The NA-MIC leadership discussed the excellent suggestions made by the EAB, and in the following section we respond to each of them.

''EAB: The Center should actively pursue methods to distribute their software and other resources to a broader community. As we noted in last year’s EAB Report, the Center is doing a good job at distributing their software toolkit, however, largely the distribution has been focused internally to NA-MIC collaborators. The EAB recommends that the Center work to more broadly distribute its software to the larger community of neuroimaging researchers. In addition, because the Center’s software tools will also be useful to biomedical researchers outside of neuroimaging, the EAB recommends the Center consider additional methods to “get the word out” about their resources.''

NA-MIC: The focus in the last two years of the Center has been to broaden the outreach effort to the neuroimaging community and to other biomedical researchers outside of this community. Many algorithms have been developed in ITK/VTK, and then ported to Slicer to try to make them as accessible as possible. We believe that the ITK/VTK platforms have been quite widely disseminated. However, as the EAB has noted, the current training and dissemination funds are limited, and the Center leadership will continue to look for creative solutions to get the word out within these resource constraints.

''EAB: The Center is well suited to assess and compare algorithms. An important question in image analysis involves understanding which image-processing algorithm is best suited for a particular task. Given the large number of different image analysis algorithms being created by NA-MIC, the Center is in a good position to evaluate the effectiveness of different algorithms and to make appropriate recommendations.''

NA-MIC: This topic has been discussed extensively within the Center, and we have addressed this issue in a manner that is aligned with our core expertise in open source software. More specifically, we will continue to build upon our strength and provide infrastructure and tools needed for open science, as well as to train researchers in the use of these tools. It is our hope that by providing easy access to these tools, we will be facilitating the process of comparison between algorithms for various biomedical tasks. This being said, many times there is no optimal algorithm for every imaging purpose. A certain segmentation routine may be excellent for one purpose, but not for another. This is a well recognized problem in image processing and computer vision, and not a fundamental limitation of medical imaging. Comparisons, however, among different algorithms to determine which algorithms are best for which questions being addressed is nonetheless important and we have made some inroads here. Sylvain Bouix, for example, has a paper in press in Neuroimage on evaluating brain tissue classifiers without ground truth, which compares several different segmentation algorithms. Also, in the shape analysis field, every NA-MIC related publication performs the necessary comparisons. Furthermore, Martin Styner and coworkers (Core 1 and 3 investigators) authored a paper on shape analysis comparisons at ISBI 2006. In addition, Martin is co-organizing a caudate and liver segmentation comparison workshop at MICCAI 2007, with currently over 35 registered participating methods. Our philosophy is to make a large menu of algorithmic methodologies available in as easy to use form as possible, so as to encourage the kind of comparisons recently done by Drs. Bouix and Styner.

''EAB: The Center should consider additional techniques to validate and verify techniques and software. The EAB was pleased to see the progress the Center made in quantifying and validating their algorithms and implementations within the NA-MIC Kit. However, because this is such an important issue, the EAB recommends the Center continue to make progress in this area.
''

NA-MIC: The Center intends to continue its effort in validation and verification of its software implementations. Center researchers have also come up with original techniques for validation, e.g., the Laplacian method for comparing the accuracy of segmentations. These validation techniques have been used to drive new original algorithmic as well. In addition, work within the Center has continued to compare manual measures of brain structure, i.e., the caudate, as a gold standard for automated measures of caudate in the same subjects. This kind of validation will continue.

''EAB: The Center should generate a set of success criteria. The EAB recommends that the Center create a set of benchmarks of success that it will use in future years to measure the Center’s success and as ways to evaluate the Center’s continuing progress and at the time of the Center’s first renewal.''

NA-MIC: We note that we have established benchmarks for success that are similar to criteria used by study sections for reviewing the success of other grant mechanisms, although also includes specific criteria for NA-MIC, and includes:

• Papers in peer reviewed publications

• Technology transfers from Core 1 to Core 3 researchers

• Software released as part of the NA-MIC Kit

• Training outside researchers in the use of the NA-MIC Kit

= Logistics =

== Schedule and process for preparation of this report ==

* March 30 - Assign section/theme leads (Ron). Last year: introduction (marty), structural analysis (allen, martin), dti (guido, marty/marek), fmri (polina, andy), namic kit(bill), timeline (ross), highlights (will), impact (bill).
*April 7,10 - tcons with Marty, Ross, Will, Tina to finalize the layout, process, and timeline of report.
* April 13 - update projects list using last year's [[NA-MIC_Collaborations]] and [[2007_Project_Half_Week#Projects|projects pursued at the half week in SLC]]. Remind investigators to update individual pages. (Tina)
* April 23- complete project description pages in updated list: [[NA-MIC_Collaborations]] (all project owners).
* April 30 - complete section summaries, introduction, highlights, impact, timeline (owners of these topics)
* May 8 - submit wiki report to NA-MIC editor, Ann (Tina)
* May 17 - submit Edited report to Rachana in pdf format (Ann)
* May 25 - submit final report, if changes are needed (Ann)
* May 29 - ship final package to NIH (Rachana)

== Guidelines from NIH Program Officer ==
===NIH Reviewer Comments on Yr2 APR (July 2006)===
The following additional guidelines were provided by the NIH progress review committee after they reviewed the 2006 report:

1. Comment on obstacles and challenges encountered during the year.

2. Comment on outcomes of DBPs.

3. Create a context for the report in the Introduction.

4. Comment on how all the themes as a whole are moving forward.

5. Full citations should be given within the report as footnotes or in a bibliography, not just as hyperlinks.

6. Explain time-lines with text including reasons for delays. Link timelines to papers and citations.

7. Define terms used in the timeline to describe the amount of progress being made, especially with respect to software development.

8. Put priority weights on completed tasks within timeline. Note if a task is a stepping stone or a final goal.

9. Make sure the projects presented in the annual report are also listed in the project pages of the NAMIC Wiki and vice versa.

10. In the budget pages, please elaborate on the number and locations for travel.

===Feb 2006===
The following guidelines were provided by Grace Peng, NA-MIC program officer, in Feb 2006.

The key is to synthesize all the individual elements into bigger picture stories that really speak of each area’s impact to the community.

The specialized scientific report should have the following format:

# Introductory page describing the new grouping of NAMIC project themes.
# A description of progress in each NAMIC project theme (not to exceed 2 pages each), tying together relevant activities from participating subcomponents and referencing cores in parentheses.
# A table of NAMIC timelines (from original proposal), graphically depicting completed tasks/goals in years 1,2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines should be described.
# A description of 3 highlights selected from all NAMIC projects to showcase NAMIC.
# A discussion of NAMIC’s impact and value to the biocomputing community this year.

2007 Annual Scientific Report

2007-05-20T17:04:38Z

Shenton: /* Response to EAB Report */

Back to [[2007_Progress_Report]]

For reference:

*[[2006_Progress_Report]]

*[[Media:2005_NAMIC_Specialized_ProgReport.pdf|2005 Annual Scientific Report]]

== 1. Introduction ==

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with espect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs continue to include neuropsychiatric disorders such as Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Schizophrenia (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a adopting a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

(Please note that this report is available on the NA-MIC wiki at the following url: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report)

== 2. Four Main Themes ==

This year's activities focus on four main themes: Diffusion Image Analysis, Structural Analysis, Functional MRI Analysis, and the NA-MIC Kit. Each of the following sections begins with an overview of the theme, provides a progress update and list of key investigators, and concludes with a set of links to additional information for individual projects in that theme.

These thematic activities involve scientists from each of the 7 NA-MIC cores (Appendix).

* [http://www.na-mic.org/Wiki/index.php/Algorithm:Main Core 1 Algorithms]-Ross Whitaker PI
* [http://www.na-mic.org/Wiki/index.php/Engineering:Main Core 2 Engineering]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/DBP:Main Core 3 Driving Biological Problems] DBP1-Martha Shenton PI / DBP2-Andy Saykin PI / DBP3-Steven Potkin PI
* [http://www.na-mic.org/Wiki/index.php/Service:Main Core 4 Service]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/Training:Main Core 5 Training]-Randy Gollub PI
* [http://www.na-mic.org/Wiki/index.php/Dissemination:Main Core 6 Dissemination]-Tina Kapur Co-PI; Steve Pieper Co-PI
* [http://www.na-mic.org/Wiki/index.php/Leadership:Main Core 7 Leadership]-Ron Kikinis

=== 2.1 Diffusion Image Analysis Theme ===

==== Progress ====
Over the past year, we have continued to develop a number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical diffusion tensor analysis. These tools have been already integrated into diffusion dedicated software (e.g., Fiber Viewer-UNC). In addition, we started developing tools for group data analysis, such as white matter atlases, and tools for data integration, that would combine and interface among different imaging modalities (such as structural, fMRI and DTI), to better estimate anatomical and functional connectivity. This software has continued to be used in multiple clinical projects involving several psychiatric populations. Below we provide detailed progress in the area of diffusion image analysis.

==== Fiber Tract Extraction and Analysis ====
* Since last year, all of the algorithms for fiber tractography and anisotropy estimation have been implemented in both the “Fiber Viewer” and “Slicer” packages, and now the resulting methods are being applied to clinical studies. Fiber Tract integrity has been investigated and results have been presented at several international conferences for four key fiber tracts in schizophrenia (corpus callosum, fornix, inferior occipito-frontal fasciculus, and uncinate fasciculus). These tracts have been extracted, and fractional anisotropy has been compared between groups, demonstrating fronto-temporal connectivity abnormalities in schizophrenia.

* In addition to clinical studies, teams have been working on other methods to define and estimate brain connectivity. The most important developments in this regard included volumentric connectivity and stochastic tractography measures. For volumentric connectivity, a PDE-based approach to white matter connectivity from DTI has been developed, that is founded on the principal of minimal paths through the tensor volume. This method computes a volumetric representation of a white matter tract given two endpoint regions. In addition, statistical methods for quantifying the full tensor data along these pathways have been also developed. Directional PDE-based flows have been proposed and implemented for a similar purpose. Further, an approach called "stochastic tractography," has been developed to calculate probability of two regions being connected by the fiber tract. This should be especialy useful when tract has to go through the regions characterized by low diffusion anisotropy.

* Some initial attempts have been made to population based analysis of DT-MRI. One method is based on the unbiased non-rigid registration of a population to a common coordinate system. The registration jointly produces an average DTI atlas, which is unbiased with respect to the choice of a template image, along with a diffeomorphic correspondence for each image. The anatomically significant correspondence provides a basis for comparison of tensor features and fiber tract geometry in clinical studies.

==== Fractional Anisotropy Analysis ====
* We have used tools developed last year, and applied them to our population of chronic schizophrenia subjects in order to investigate two fiber tracts: the cingulum bundle and the corpus callosum. In the case of cingulum bundle, manually drawn regions of interest and Finsler geometry were used to extract entire cingulum bundle fiber tract, and FA was estimated along the tract, and compared between groups. Corpus Callosum cross sectional area and its probabilistic subdivisions were determined automatically from the structural MRI scans using a model based deformable contour segmentation. The structural scan was then co-registered with the DTI scan and the anatomical corpus callosum subdivisions were propagated to the associated FA map, and compared between groups, demonstrating deficient interhemispheric communication in schizophrenia.

==== Integration of fMRI and DTI, Path-of-Interest Analysis ====
* Progress has been also made in the development of a tool that would successfully combine and integrate functional and anatomical information. The Optimal Path Analysis method has been applied to the Harvard- BWH fMRI dataset, and anatomical connections between regions active during the fMRI experiment have been extracted for each subject. The connectivity has calculated and compared between groups. This tool is now being ported to Slicer3.

==== Key Investigators ====

* BWH: Martha Shenton, Marek Kubicki, Marc Niethammer, Sylvain Bouix, Jennifer Fitzsimmons, Katarina Quintis, Doug Markant, Kate Smith, Georgia Bushell, Mark Dreusicke, Carl-Fredrik Westin, Raul San Jose, Gordon Kindlmann
* MGH: Bruce Fischl, Denis Jen, David Kennedy
* MIT: Lauren O'Donnell, Polina Golland, Tri Ngo
* UCI: James Fallon, Martina Panzenboeck
* UNC: Guido Gerig, Isabelle Corouge, Casey Goodlett, Martin Styner
* Utah: Tom Fletcher, Ross Whitaker, Saurav Basu, Davis McKay
* GA Tech: Eric Pichon, John Melonakos, Xavier LaFaucheur, Vandana Mohan, Allen Tannenbaum
* Dartmouth: John West, Andrew Saykin, Laura Flashman, Paul Wang, Heather Pixley, Robert Roth
* Isomics: Steve Pieper
* Kitware: Luis Ibanez

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Diffusion_Image_Analysis NA-MIC Projects on Diffusion Image Analysis].

=== 2.2 Structural Analysis Theme ===

==== Progress ====

Within the NAMIC's structural analysis theme, the main topics of interest are structural segmentation, registration and shape analysis. These topics are of course intertwined, e.g., segmentation or registration can directly deliver structural correspondence used in shape analysis, or in turn shape modeling is necessary for good structural segmentations. Here is a selection of progress highlights in the structural analysis theme

====='''Segmentation'''=====

* Wavelet based structural segmentation: We have developed a spherical wavelet based framework for the segmentation of selected brain structures, such as the hippocampus or the caudate nucleus. An automated segmentation of such structures must be highly accurate and include high frequency variations in the surface. Since shape representation is a key component of the segmentation, it must be rich enough to express shape variations at various frequency levels, from low harmonics to sharp edges. Medical object segmentation with deformable models and statistical shape modeling may be combined to obtain a more robust and accurate segmentation. To address this issue, a decomposable spherical wavelet based shape representation targeted to the population seems natural, where the shape parameters are separated into groups that describe independent global and/or local biological variations in the population, and a prior induced over each group explicitly encodes these variations. This work presents three novel contributions for shape representation, multiscale prior probability estimation and segmentation.

* Directional based segmentation: We have proposed an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. In the classical case, the Euclidean metric is locally multiplied by a scalar conformal factor (based on image information) such that the weighted length of curves lying on points of interest (typically edges) is small. We propose to add directionality to the factor, and show that one gets a well-defined minimization problem in the case that the factor defines a Finsler metric. Optimal curves may be obtained using the calculus of variations or dynamic programming. This methodology also makes connections to the important technique of graph-cuts.

* Statistical PDE methods for segmentation in shape space: This past year, we have proposed another method to perform shape-driven segmentation. In our approach, shapes are represented using binary maps, and linear PCA is utilized to provide shape priors for segmentation. Intensity based probability distributions are then employed to convert the given volume into a binary map representation, and a new energy functional is formulated whose minimization is performed using a parametric model for surface evolution in the shape space. Our algorithm is then applied to the segmentation of brain caudate nucleus and hippocampus from MRI data. Our validation shows that the proposed algorithm outperforms the log-likelihood based energy, converges in less than 5 iterations and is very obust to initialization. The overall algorithm is illustrates the potential for segmentation in shape space.

* Rule-based segmentation methods: We have continued this past year to develop segmentation methods based on heuristic rules provided to us by our Core 3 partners for segmenting various brain regions of interest in schizophrenia, e.g. the DLPFC and the striatum. The idea is to try to semi-automate these rules in order to forge an interactive tool for segmentation which can greatly shorten the time necessary for manual segmentation. Typically, these methods are used in conjunction with some Bayesian classifier which further aids to automating and in speeding up the given segmentation methodology.

* Tissue and structural segmentation via EM Segmenter: Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases segmentation is mostly performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We have developed an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior knowledge. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. The mapping between atlas and image space, structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the Maximum A posteriori Probability (MAP) estimation problem. These results are then fed into an Active Mean Field approach, which views the results as priors to a Mean Field approximation with a curve length prior. This segmentation framework has been ported into the NAMIC-toolkit and a first version of the tool is distributed with Slicer 3.

====='''Shape Analysis'''=====

* Wavelet based shape analysis: A shape representation that encodes variations at multiple scales can be useful as a rich feature set for shape analysis and classification. Combining tools from the existing shape analysis toolset, we extended it to include spherical wavelet coefficients (SWC) as features and compared the results obtained to shape analysis using a SPHARM-PDM representation. The rich SWC feature set allows the differentiation of shape differences at various scales as well as highly correlates with the existing SPHARM-PDM analysis but with increased statistical sensitivity. Wavelet coefficient shrinkage and dimension reduction are well-understood and have been widely researched for traditional types of wavelet decompositions but not much explored for the second generation wavelets. During the past year, we have developed a Bayesian model on our specific wavelet structure based on a population of surfaces. For each shape, the deviation from the mean is computed and is modeled as the sum of an unknown signal and a noise. This deviation is encoded by the wavelet transform and our goal is to estimate the wavelet coefficients belonging to the noiseless signal.

* Surface flattening for shape analysis: Flattened representations of undulated surfaces constitute an active area of research in the field of medical imaging and visualization, due to their extensive use for registration and shape analysis of various structures of interest. We have presented a method for flattening anatomical surfaces in an area preserving manner, while minimizing the geometrical distortion. This method is based on the theory of optimal mass transport and conformal mapping of surfaces. The key idea here is the use of a multiresolution scheme for the solution of optimal mass transport gradient descent equations which allows a fast and stable solution for optimal transport. The method has been implemented on a GPU, allowing us to flatten a 128 by 128 by 128 volume in about 5 seconds on a standard workstation.

* Curvature based population correspondence: We have extended the Minimum Description Length population based correspondence framework to include curvature based measurements, such as the Koenderink Shape Index S and Curvedness C in combination with the standard location information. Current methodology in population based correspondence is based mainly on minimizing distribution properties of surface point locations and are thus not invariant to alignment. We have favorably compared our combined "Curvature + Location" MDL to the standard MDL, as well as to the SPHARM approach, in complex structures, such as the striatal brain structure (composed of caudate, nucleus accumbens and putamen).

* Shape analysis toolset: A considerable amount of work was spent on the development aspect of the shape analysis tools. The distributed set of tools is continuously enhanced and the population based correspondence framework has been released as open source. All the tools including the visualization tool, KWMeshVisu, can be called directly from Slicer 3. The visualization tool allows the overlay of scalar, vector and ellipsoid data onto surfaces via versatile colormaps. The attributed surfaces are then visible within Slicer 3. This lean visualization tool fills a niche and is also used in our cortical thickness analysis tool. Also, while it is entirely possible to run all shape analysis steps by calling the individual modules, this is highly inefficient in a larger study. As a result we are developing a separate shape pipeline Slicer module that uses Batchmake to run the shape analysis pipeline as a distributed, background process. The whole shape analysis pipeline will thus become entirely encapsulated and accessible to the trained clinical collaborator.

* Particle based correspondence: We have developed a method for a multi-object correspondence optimization, and have applied it successfully to a proof-of-concept application for the analysis of brain structure complexes from a longitudinal study of pediatric autism. This new method for constructing compact statistical point-based models of ensembles of similar shapes does not rely on any specific surface parameterization, requires little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including non-manifold surfaces and objects of arbitrary topology. The method uses a dynamic particle system to optimize correspondence point positions across all structures by simultaneously maximizing both the geometric accuracy and the statistical simplicity of the model.

==== Key Investigators ====

* MIT: Kilian Pohl, Sandy Wells, Eric Grimson
* UNC: Martin Styner, Ipek Oguz, Guido Gerig, Xavier Barbero
* Utah: Ross Whitaker, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer
* GaTech: Allen Tannenbaum, John Melonakos, Tauseef ur Rehman, Shawn Lankton, Ramsey Al-Hakim, Eric Pichon, Delphine Nain, Oleg Michailovich, Yogesh Rathi, James Malcolm
* Isomics: Steve Pieper
* GE: Bill Lorensen, Jim Miller
* Kitware: Luis Ibanez, Karthik Krishnan,
* UCLA: Michael J. Pan, Jagadeeswaran Rajendiran
* BWH: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt
* Dartmouth: Andrew Saykin
* UCI: James Fallon

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Structural_Image_Analysis NA-MIC Projects on Structural Image Analysis].

=== 2.3 Functional MRI Analysis Theme ===

====Progress====

During this year, the focus of the algorithms and the engineering
cores has been on the structural and DTI analysis. While we continued
to expand the methods and the infrastructure in NAMIC-kit to support
fMRI analysis, as well as using the analysis tools to perform clinical
studies, the emphasis of the work this year has been on integrating
the fMRI analysis with other modalities and supporting other
modalities.

===== Clinical Studies =====

We would like to highlight several clinical studies within NAMIC that
focused on fMRI data and its relationship with other imaging
modalities:

* Imaging Phenotypes in Schizophrenics and Controls: Functional connectivity of the DLPFC by genotype was investigated emplying partial least squares (PLS) correlation analysis. PLS is a multivariate analytical technique used to summarize large neuroimaging data sets in such a way as to correlate patterns of activation with a variable(s) of interest (i.e., DLPFC activity). In the most recent analysis, the DRD1 genotype was used as a grouping variable. This analysis has been submitted for publication (Tura, Turner, Fallon, Kennedy, and Potkin. Genetic Impact on Functional Connectivity in Schizophrenics During a Working Memory Task). Working memory performance did not differ significantly between the two cohorts. However, imaging-genetic analysis showed a significant difference (P< 0.05) between the circuitry engaged by each group. Significance and reliability of the resulting imaging-behavioral patterns within each genotype were assessed by 200 bootstrap and 500 permutation tests, respectively. In one group, the circuitry included the temporal pole, the insula, the dorsolateral prefrontal cortex, and the Brodmann Areas (BA) 1,2,3,4,6,11 and 21, while the other group showed a network comprising the tectum, precuneus retroplenial, vermis, substantia nigra, BA 22,39,8, and 9. The DRD1 polymorphic site may characterize circuitry differences in schizophrenic patients.

*Path-Of-Interest Analysis (joint DTI/fMRI modeling): We collected preliminary data using an application of the “optimal path analysis”. In this analysis, we extracted group fMRI activation due to the Stroop effect (an attentional experiment where incongruent color, in which the word is written, competes with name of the color itself, activating areas responding to conflict monitoring and selection) separately for controls and schizophrenics. This resulted in three clusters of activation, one in the right Dorsolateral Prefrontal Cortex, a second in the Anterior Cingulate Gyrus, and a third in the Medial Parietal Lobe. In the next step, we placed activation clusters in each individual space, by reversing normalization parameters used during fMRI analysis. Finally, EPI fMRI scans were co-registered to DTI scans, and the same registration parameters were applied to activation maps (fMRI results). Regions of activation were used as start and destination points for optimal path analysis, which resulted in three separate paths of optimal connectivity for each subject. The probability of the connections were then calculated for each path and each subject, and compared between groups. In our preliminary analysis, we included 10 control subjects and 10 chronic schizophrenics. Our results demonstrated a relationship between Stroop Effect fMRI activation in the medial parietal area and optimal path connectivity between parietal and cingulate activation sites in schizophrenics (rho=-0.56; p=0.047), which was not observed in controls. These findings suggest that decreased connectivity may result in schizophrenics relying more on posterior parts of the executive attentional network during performance of the Stroop task.

*Hippocampal and frontal memory circuitry abnormalities in schizophrenia: Relation of diffusion, morphometric and fMRI markers: We performed a combined DTI, fMRI, and morphometric study on 13 patients with schizophrenia (SZ) and 14 HC. We identified areas of increased trace diffusivity (TD) in the hippocampal and insular regions as well as areas of reduced fractional anisotropy (FA) in left frontal white matter in SZ relative to HC (p<.01). Voxel based morphometry analyses in a subset of these subjects showed corresponding reductions in gray matter density in hippocampal and insular regions in patients relative to controls (p<.01). Analysis of fMRI results from the novel vs. repeated word contrast from the event-related auditory verbal episodic memory encoding/retrieval task in a subset of the subjects indicated reduced activation in frontal and temporal regions, as well as increased activation in posterior cingulate, retrosplenial, and thalamic regions in SZ relative to HC (p<.05). Further analysis showed that left frontal white matter FA was associated with activation in the left and right hippocampi as well as other frontal and temporal regions, but inversely related to activation in the retrosplenial/posterior cingulate region (p<.05). These initial findings indicate a pattern of relationships between of structural and functional brain abnormalities in schizophrenia and demonstrate the feasibility of integrated quantitative analyses across modalities.

===== Methods =====

During this year, we continued methodological development along two
directions:

*Improving fMRI detectors by incorporating Markov priors on the activation state. We integrated the improved detector into Slicer and performed substantial validation of the methods using fBIRN data provided by the UC Irvine group. Journal paper on the method has been submitted to IEEE TMI.

*Improving registration of EPI images to anatomical scans through modeling of the EPI distortions. We demonstrated an initial model that uses segmentation of the structural scan to predict the distortions in the EPI images. The preliminary results are quite encouraging.

==== Key Investigators ====

* MIT: Polina Golland, Sandy Wells, Wanmei Ou, Claire Poynton
* BWH: Martha Shenton, Marek Kubicki
* Dartmouth: Andy Saykin
* UCI: Jessica Turner, Stephen Potkin
* Toronto: Jim Kennedy

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#fMRI_Analysis NA-MIC Projects on fMRI Analysis].

=== 2.4 [[NA-MIC-Kit|NA-MIC Kit]] Theme ===

==== Progress ====
The continuing vision of the NA-MIC Kit is to provide an open source set of software tools and methodologies that will serve as the foundation for medical image computing projects for both academic and commercial use. Key elements of this vision are:

# ''Unrestrictive License.'' Users of the Kit are free to distribute their derived works under any license suitable to their needs.
# ''Cross Platform.'' This software set can be adapted to the best available price-performance computer systems for any particular use.
# ''Extensible Application Framework.'' New techniques and algorithms can be quickly integrated into a working system. Sophisticated user interfaces can be generated easily through automated processes. Sophisticated toolsets such as ITK, VTK, and KWWidgets are available to create and deploy applications quickly.
# ''Quality Software Process.'' Developers and users can rely on accurate and well documented behavior from all the parts of the Kit.
# ''Sustainable Community.'' Users are actively involved in the design process of the Kit. Documentation, training materials, and hands-on sessions are available and well publicized to the community.

====='''Slicer3'''=====

A major focus of the third year was the implementation of the Slicer3 in the NAMIC Kit. This effort addressed Item #3 above ''Extensible Application Framework.'' The previous two years of the NAMIC project, which entailed gathering requirements from Cores 1 and 3, and developing the computational foundation, toolsets, and software process, came together in the Slicer3 application platform.

Core 2 worked hard to insure that the Slicer 3 application serves, and will continue to serve, as a productive technology deployment platform. The application framework was designed carefully to provide ease-of-use, both in terms of interaction and software integration. Advanced capabilities, including the ability to launch large-scale grid computing, was designed into the application. Some of the key features of the Slicer3 application completed in the third year include the following.
* Advanced application framework including a tuned GUI for ease of use, undo/redo capabilities, 2D/3D view windows, and support for advanced interaction techniques such as 3D widgets. The application provides viewers for displaying slices, volumes, and models including the ability to edit properties. A built in transformation pipeline enables users to confidently import, edit and display data in a consistent coordinate system.
* The application is data-driven based on the next generation MRML scene description file format. Backward compatibility to Slicer 2.x MRML files is preserved.
* A module plug-in architecture and execution model that enables researchers to package and integrate their software into the Slicer3 framework. Plug-in modules can be implemented in a variety of programming languages, and are described using a simple XML description. These modules, when located and loaded into Slicer3, have the capability to automatically generate their user interface, which is seamlessly integrated into the Slicer3 GUI.
* Support for editing and marking data including support for fudicials, paint and draw editors.
* The creation of several simple plug-in modules, including the conversion of previous Slicer 2.x modules to the new Slicer3 architecture.

====='''EM Segment'''=====

As an application framework, Slicer3 provides tools for loading, viewing, measuring, editing, and saving data. To support advanced medical image analysis, plug-in modules are required in conjunction with the Slicer3 core. To demonstrate the capabilities of the framework we implemented the [http://www.na-mic.org/Wiki/index.php/Slicer3:EM EM Segment] module, a sophisticated and proven method for automatically segmenting complex anatomical structures.

To use this module, users specify parameters defining the image protocol and the anatomical structures of interests. This process results in a template that the module uses to automatically segment large data sets. The template is composed of atlas data and a non-trivial collection of parameters for the EM Segment algorithm ([[Media:Pohl-miccai-short-2005.pdf|Pohl et al.]]). Once the parameters are specified, the target images are segmented using the algorithm; if the results are satisfactory, the template is saved and can be used later to segment new images (via the GUI or batch processing). If the results are unsatisfactory, the parameters can be modified and the segmentation re-run.

Besides successfully demonstrating the use of complex algorithms in the Slicer3 framework, this effort also led us to develop tools, including modifications to the underlying KWWidgets GUI toolkit, to support module workflow. With these tools, it is possible to simplify complex modules by dividing the complicated template specification task into a number of smaller, intuitive steps. These steps are enforced by the GUI and reduce the potential for user error, while improving the overall user interface.

====='''Quality Software Process'''=====

Building on [http://lwn.net/Articles/188693/ last year's success with the KDE community], the NAMIC community continued to extend its world-class, open source software process tools CMake, DART, CTest and DART. These tools, which form the core of a quality-oriented, test-driven development (TDD) software process. In particular, the CPack system is now able to automatically package and distibute code, libraries, executables and installers across all of NAMIC's supported platforms (i.e., Linux, Windows, Mac). This enables the NAMIC developer community to rapidly deploy software tools to the user community.

==== Key Investigators ====

* GE: Bill Lorensen, Jim Miller, Xiaodong Tao, Dan Blezek
* Isomics: Steve Pieper, Alex Yarmarkovich
* Kitware: Will Schroeder, Luis Ibanez, Brad Davis, Andy Cedilnik, Sebastien Barre, Bill Hoffman
* UCLA: Mike Pan, Jagadeeswaran Rajendiran
* UCSD: Neil Jones, Jeffrey Grethe, Mark Ellisman
* BWH: Nicole Aucoin, Katie Hayes, Wendy Plesniak, Mike Halle, Gordon Kindlmann, Raul San Jose Estepar, Haiying Liu, Ron Kikinis
* MIT: Lauren O'Donnell, Kilian Pohl

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#NA-MIC_Kit NA-MIC Kit Projects].

== 3. Highlights ==
The third year of the NAMIC project saw continued development and dissemination of medical image analysis software. With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NAMIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NAMIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

=== 3.1 Advanced Algorithms ===
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NAMIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NAMIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NAMIC Sandbox in preparation for formal acceptance into the NAMIC Kit.

=== 3.2 Technology Deployment Platform: Slicer3 ===
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NAMIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NAMIC Kit, and are now available to all other modules built on the Slicer3 framework.

=== 3.3 Outreach and Technology Transfer ===
Cores 4-5-6 continue to support, train and dissemniate to the NAMIC community, and the broader biomedical computing community.
* NAMIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NAMIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair.

== 4. Impact and Value to Biocomputing ==

In NA-MIC's third year, it is evident that NA-MIC is developing a
culture, environment, and resources to foster and incite collaborative
research in medical image analysis that draws together mathematicians,
computer scientists, software engineers, and clinical
researchers. These artefacts of NA-MIC impact how NA-MIC
operates, make NA-MIC a fulcrum for NIH funded research,
and draws new collaborators from across the country
and around the world to NA-MIC.

=== 4.1 Impact within the Center ===

Within the center, the NA-MIC organization, NA-MIC processes, and the
NA-MIC calendar has permeated the research. The organization is
nimble, forming ad hoc distributed teams within and between cores to
address specific biocomputing tasks. Information is shared freely on
the NA-MIC Wiki, on the weekly Engineering telephone conferences, and
in the NA-MIC Subversion source code repository. The software
engineering tools of CMake, Dart 2 and CTest, CPack, and KWWidgets
facilitate a cross platform software environment for medical image
analysis that be easily built, tested, and distributed to
end-users. Core 2 has provided a platform, Slicer 3, that allows Core
1 to easily integrate new technology and deliver this technology in an
end user application to Core 3. Core 1 has developed a host of
techniques to apply to structural and diffusion analysis which are
under evaluation by Core 3. Major NA-MIC events, such as the annual
All Hands Meeting, the Summer Project Week, the Spring Algorithms
meeting, and Engineering Teleconferences are avidly attended by NA-MIC
researchers as opportunities to foster collaborations.

=== 4.2 Impact within NIH Funded Research ===

Within NIH funded research, NA-MIC continues to forge relationships
with other large NIH funded projects such as BIRN, caBIG, NAC, and
IGT. Here, we are sharing the NA-MIC culture, engineering practices,
and tools. BIRN hosts data for the NA-MIC researchers and NA-MIC hosts
BIRN wikis. caBIG lists the 3D Slicer among the applications available
on the National Cancer Imaging Archive. NAC and IGT use the NA-MIC
infrastructure and are involved in the development of the 3D
Slicer. BIRN recently held an event modeled after the NA-MIC Project
Week. NA-MIC has become a resource on open source licensing to the medical image analysis community.

NA-MIC is also attracting NIH funded collaborations. Two grants have
been funded under PAR-05-063 to collaborate with NA-MIC: ''Automated FE Mesh Development'' and ''Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI''. Five additional
applications to collaborate with NA-MIC via the NCBC collaborative
grant mechanism are under consideration. Additional grant applications
submitted under other calls are planning to use and extend the NA-MIC tools.

=== 4.3 National and International Impact ===

NA-MIC events and tools garner national and international interest.
There were nearly 100 participants at the NA-MIC All Hands Meeting in
January 2007, with many of these participants from outside of NA-MIC.
Several researchers from outside the NA-MIC community have attended
the Summer Project Weeks and the Winter Project Half-Weeks to gain
access to the NA-MIC tools and people. These external researchers are
contributing ideas and technology back into NA-MIC.

Components of the NA-MIC kit are used globally. The software
engineering tools of CMake, Dart 2 and CTest are used by many open
source projects and commercial applications. For example, the K Desktop Environment (KDE) for
Linux and Unix workstations uses CMake and Dart. KDE is one of the
largest open source projects in the world. Many open source projects and
commercial products are benefiting from the NA-MIC related
contributions to ITK and VTK. Finally, Slicer 3 is being used as an
image analysis platform in several fields outside of medical image
analysis, in particular, biological image analysis, astronomy, and industrial inspection.

NA-MIC co-sponsored the ''Workshop on Open Science'' at the Medical
Image Computing and Computer-Assisted Intervention (MICCAI) 2006
conference. The proceedings of the workshop are published on the
electronic Insight Journal, another NIH-funded activity.

Over 50 NA-MIC related publications have been produced since the inception of the center.

== 5.NA-MIC Timeline ==

This section provides a table of NAMIC timelines from the original proposal that graphically depicts completed tasks/goals in years 1, 2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines have also been described.

[[2007_Scientific_Report_Timeline|2007 Scientific Report Timeline]]

== Appendix A Publications ==

http://www.na-mic.org/Wiki/index.php/Publications

== Appendix B EAB Report ==

===EAB Report===
The NA-MIC External Advisory Board (EAB), chaired by Professor Chris Johnson of the University of Utah, met at the annual All-Hands Meeting. After individual presentations by NA-MIC investigators and open as well as closed-door EAB discussion, the Board provided its independent expert assessment of the Center.

[https://share.spl.harvard.edu/grants/u54/2007/APR%202007/NAMIC-EAB-Report07-ver5.doc This is a password protected link to the EAB report.]

===Response to EAB Report===

The NA-MIC leadership discussed the excellent suggestions made by the EAB, and in the following section we respond to each of them.

''EAB: The Center should actively pursue methods to distribute their software and other resources to a broader community. As we noted in last year’s EAB Report, the Center is doing a good job at distributing their software toolkit, however, largely the distribution has been focused internally to NA-MIC collaborators. The EAB recommends that the Center work to more broadly distribute its software to the larger community of neuroimaging researchers. In addition, because the Center’s software tools will also be useful to biomedical researchers outside of neuroimaging, the EAB recommends the Center consider additional methods to “get the word out” about their resources.''

NA-MIC: The focus in the last two years of the Center has been to broaden the outreach effort to the neuroimaging community and to other biomedical researchers outside of this community. Many algorithms have been developed in ITK/VTK, and then ported to Slicer to try to make them as accessible as possible. We believe that the ITK/VTK platforms have been quite widely disseminated. However, as the EAB has noted, the current training and dissemination funds are limited, and the Center leadership will continue to look for creative solutions to get the word out within these resource constraints.

''EAB: The Center is well suited to assess and compare algorithms. An important question in image analysis involves understanding which image-processing algorithm is best suited for a particular task. Given the large number of different image analysis algorithms being created by NA-MIC, the Center is in a good position to evaluate the effectiveness of different algorithms and to make appropriate recommendations.''

NA-MIC: This topic has been discussed extensively within the Center, and we have addressed this issue in a manner that is aligned with our core expertise in open source software. More specifically, we will continue to build upon our strength and provide infrastructure and tools needed for open science, as well as to train researchers in the use of these tools. It is our hope that by providing easy access to these tools, we will be facilitating the process of comparison between algorithms for various biomedical tasks. This being said, many times there is no optimal algorithm for every imaging purpose. A certain segmentation routine may be excellent for one purpose, but not for another. This is a well recognized problem in image processing and computer vision, and not a fundamental limitation of medical imaging. Comparisons, however, among different algorithms to determine which algorithms are best for which questions being addressed is nonetheless important and we have made some inroads here. Sylvain Bouix, for example, has a paper in press in Neuroimage on evaluating brain tissue classifiers without ground truth, which compares several different segmentation algorithms. Also, in the shape analysis field, every NA-MIC related publication performs the necessary comparisons. Furthermore, Martin Styner and coworkers (Core 1 and 3 investigators) authored a paper on shape analysis comparisons at ISBI 2006. In addition, Martin is co-organizing a caudate and liver segmentation comparison workshop at MICCAI 2007, with currently over 35 registered participating methods. Our philosophy is to make a large menu of algorithmic methodologies available in as easy to use form as possible, so as to encourage the kind of comparisons recently done by Drs. Bouix and Styner.

''EAB: The Center should consider additional techniques to validate and verify techniques and software. The EAB was pleased to see the progress the Center made in quantifying and validating their algorithms and implementations within the NA-MIC Kit. However, because this is such an important issue, the EAB recommends the Center continue to make progress in this area.
''

NA-MIC: The Center intends to continue its effort in validation and verification of its software implementations. Center researchers have also come up with original techniques for validation, e.g., the Laplacian method for comparing the accuracy of segmentations. These validation techniques have been used to drive new original algorithmic as well. In addition, work within the Center has continued to compare manual measures of brain structure, i.e., the caudate as a gold standard for automated measures of caudate in the same subjects. This kind of validation will also continue.

''EAB: The Center should generate a set of success criteria. The EAB recommends that the Center create a set of benchmarks of success that it will use in future years to measure the Center’s success and as ways to evaluate the Center’s continuing progress and at the time of the Center’s first renewal.''

NA-MIC: We note that we have established benchmarks for success that are similar to criteria used by study sections for reviewing the success of other grant mechanisms, although also includes specific criteria for NA-MIC, and includes:

• Papers in peer reviewed publications

• Technology transfers from Core 1 to Core 3 researchers

• Software released as part of the NA-MIC Kit

• Training outside researchers in the use of the NA-MIC Kit

= Logistics =

== Schedule and process for preparation of this report ==

* March 30 - Assign section/theme leads (Ron). Last year: introduction (marty), structural analysis (allen, martin), dti (guido, marty/marek), fmri (polina, andy), namic kit(bill), timeline (ross), highlights (will), impact (bill).
*April 7,10 - tcons with Marty, Ross, Will, Tina to finalize the layout, process, and timeline of report.
* April 13 - update projects list using last year's [[NA-MIC_Collaborations]] and [[2007_Project_Half_Week#Projects|projects pursued at the half week in SLC]]. Remind investigators to update individual pages. (Tina)
* April 23- complete project description pages in updated list: [[NA-MIC_Collaborations]] (all project owners).
* April 30 - complete section summaries, introduction, highlights, impact, timeline (owners of these topics)
* May 8 - submit wiki report to NA-MIC editor, Ann (Tina)
* May 17 - submit Edited report to Rachana in pdf format (Ann)
* May 25 - submit final report, if changes are needed (Ann)
* May 29 - ship final package to NIH (Rachana)

== Guidelines from NIH Program Officer ==
===NIH Reviewer Comments on Yr2 APR (July 2006)===
The following additional guidelines were provided by the NIH progress review committee after they reviewed the 2006 report:

1. Comment on obstacles and challenges encountered during the year.

2. Comment on outcomes of DBPs.

3. Create a context for the report in the Introduction.

4. Comment on how all the themes as a whole are moving forward.

5. Full citations should be given within the report as footnotes or in a bibliography, not just as hyperlinks.

6. Explain time-lines with text including reasons for delays. Link timelines to papers and citations.

7. Define terms used in the timeline to describe the amount of progress being made, especially with respect to software development.

8. Put priority weights on completed tasks within timeline. Note if a task is a stepping stone or a final goal.

9. Make sure the projects presented in the annual report are also listed in the project pages of the NAMIC Wiki and vice versa.

10. In the budget pages, please elaborate on the number and locations for travel.

===Feb 2006===
The following guidelines were provided by Grace Peng, NA-MIC program officer, in Feb 2006.

The key is to synthesize all the individual elements into bigger picture stories that really speak of each area’s impact to the community.

The specialized scientific report should have the following format:

# Introductory page describing the new grouping of NAMIC project themes.
# A description of progress in each NAMIC project theme (not to exceed 2 pages each), tying together relevant activities from participating subcomponents and referencing cores in parentheses.
# A table of NAMIC timelines (from original proposal), graphically depicting completed tasks/goals in years 1,2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines should be described.
# A description of 3 highlights selected from all NAMIC projects to showcase NAMIC.
# A discussion of NAMIC’s impact and value to the biocomputing community this year.

2007 Annual Scientific Report

2007-05-20T17:03:45Z

Shenton: /* Response to EAB Report */

Back to [[2007_Progress_Report]]

For reference:

*[[2006_Progress_Report]]

*[[Media:2005_NAMIC_Specialized_ProgReport.pdf|2005 Annual Scientific Report]]

== 1. Introduction ==

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with espect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs continue to include neuropsychiatric disorders such as Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Schizophrenia (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a adopting a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

(Please note that this report is available on the NA-MIC wiki at the following url: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report)

== 2. Four Main Themes ==

This year's activities focus on four main themes: Diffusion Image Analysis, Structural Analysis, Functional MRI Analysis, and the NA-MIC Kit. Each of the following sections begins with an overview of the theme, provides a progress update and list of key investigators, and concludes with a set of links to additional information for individual projects in that theme.

These thematic activities involve scientists from each of the 7 NA-MIC cores (Appendix).

* [http://www.na-mic.org/Wiki/index.php/Algorithm:Main Core 1 Algorithms]-Ross Whitaker PI
* [http://www.na-mic.org/Wiki/index.php/Engineering:Main Core 2 Engineering]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/DBP:Main Core 3 Driving Biological Problems] DBP1-Martha Shenton PI / DBP2-Andy Saykin PI / DBP3-Steven Potkin PI
* [http://www.na-mic.org/Wiki/index.php/Service:Main Core 4 Service]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/Training:Main Core 5 Training]-Randy Gollub PI
* [http://www.na-mic.org/Wiki/index.php/Dissemination:Main Core 6 Dissemination]-Tina Kapur Co-PI; Steve Pieper Co-PI
* [http://www.na-mic.org/Wiki/index.php/Leadership:Main Core 7 Leadership]-Ron Kikinis

=== 2.1 Diffusion Image Analysis Theme ===

==== Progress ====
Over the past year, we have continued to develop a number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical diffusion tensor analysis. These tools have been already integrated into diffusion dedicated software (e.g., Fiber Viewer-UNC). In addition, we started developing tools for group data analysis, such as white matter atlases, and tools for data integration, that would combine and interface among different imaging modalities (such as structural, fMRI and DTI), to better estimate anatomical and functional connectivity. This software has continued to be used in multiple clinical projects involving several psychiatric populations. Below we provide detailed progress in the area of diffusion image analysis.

==== Fiber Tract Extraction and Analysis ====
* Since last year, all of the algorithms for fiber tractography and anisotropy estimation have been implemented in both the “Fiber Viewer” and “Slicer” packages, and now the resulting methods are being applied to clinical studies. Fiber Tract integrity has been investigated and results have been presented at several international conferences for four key fiber tracts in schizophrenia (corpus callosum, fornix, inferior occipito-frontal fasciculus, and uncinate fasciculus). These tracts have been extracted, and fractional anisotropy has been compared between groups, demonstrating fronto-temporal connectivity abnormalities in schizophrenia.

* In addition to clinical studies, teams have been working on other methods to define and estimate brain connectivity. The most important developments in this regard included volumentric connectivity and stochastic tractography measures. For volumentric connectivity, a PDE-based approach to white matter connectivity from DTI has been developed, that is founded on the principal of minimal paths through the tensor volume. This method computes a volumetric representation of a white matter tract given two endpoint regions. In addition, statistical methods for quantifying the full tensor data along these pathways have been also developed. Directional PDE-based flows have been proposed and implemented for a similar purpose. Further, an approach called "stochastic tractography," has been developed to calculate probability of two regions being connected by the fiber tract. This should be especialy useful when tract has to go through the regions characterized by low diffusion anisotropy.

* Some initial attempts have been made to population based analysis of DT-MRI. One method is based on the unbiased non-rigid registration of a population to a common coordinate system. The registration jointly produces an average DTI atlas, which is unbiased with respect to the choice of a template image, along with a diffeomorphic correspondence for each image. The anatomically significant correspondence provides a basis for comparison of tensor features and fiber tract geometry in clinical studies.

==== Fractional Anisotropy Analysis ====
* We have used tools developed last year, and applied them to our population of chronic schizophrenia subjects in order to investigate two fiber tracts: the cingulum bundle and the corpus callosum. In the case of cingulum bundle, manually drawn regions of interest and Finsler geometry were used to extract entire cingulum bundle fiber tract, and FA was estimated along the tract, and compared between groups. Corpus Callosum cross sectional area and its probabilistic subdivisions were determined automatically from the structural MRI scans using a model based deformable contour segmentation. The structural scan was then co-registered with the DTI scan and the anatomical corpus callosum subdivisions were propagated to the associated FA map, and compared between groups, demonstrating deficient interhemispheric communication in schizophrenia.

==== Integration of fMRI and DTI, Path-of-Interest Analysis ====
* Progress has been also made in the development of a tool that would successfully combine and integrate functional and anatomical information. The Optimal Path Analysis method has been applied to the Harvard- BWH fMRI dataset, and anatomical connections between regions active during the fMRI experiment have been extracted for each subject. The connectivity has calculated and compared between groups. This tool is now being ported to Slicer3.

==== Key Investigators ====

* BWH: Martha Shenton, Marek Kubicki, Marc Niethammer, Sylvain Bouix, Jennifer Fitzsimmons, Katarina Quintis, Doug Markant, Kate Smith, Georgia Bushell, Mark Dreusicke, Carl-Fredrik Westin, Raul San Jose, Gordon Kindlmann
* MGH: Bruce Fischl, Denis Jen, David Kennedy
* MIT: Lauren O'Donnell, Polina Golland, Tri Ngo
* UCI: James Fallon, Martina Panzenboeck
* UNC: Guido Gerig, Isabelle Corouge, Casey Goodlett, Martin Styner
* Utah: Tom Fletcher, Ross Whitaker, Saurav Basu, Davis McKay
* GA Tech: Eric Pichon, John Melonakos, Xavier LaFaucheur, Vandana Mohan, Allen Tannenbaum
* Dartmouth: John West, Andrew Saykin, Laura Flashman, Paul Wang, Heather Pixley, Robert Roth
* Isomics: Steve Pieper
* Kitware: Luis Ibanez

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Diffusion_Image_Analysis NA-MIC Projects on Diffusion Image Analysis].

=== 2.2 Structural Analysis Theme ===

==== Progress ====

Within the NAMIC's structural analysis theme, the main topics of interest are structural segmentation, registration and shape analysis. These topics are of course intertwined, e.g., segmentation or registration can directly deliver structural correspondence used in shape analysis, or in turn shape modeling is necessary for good structural segmentations. Here is a selection of progress highlights in the structural analysis theme

====='''Segmentation'''=====

* Wavelet based structural segmentation: We have developed a spherical wavelet based framework for the segmentation of selected brain structures, such as the hippocampus or the caudate nucleus. An automated segmentation of such structures must be highly accurate and include high frequency variations in the surface. Since shape representation is a key component of the segmentation, it must be rich enough to express shape variations at various frequency levels, from low harmonics to sharp edges. Medical object segmentation with deformable models and statistical shape modeling may be combined to obtain a more robust and accurate segmentation. To address this issue, a decomposable spherical wavelet based shape representation targeted to the population seems natural, where the shape parameters are separated into groups that describe independent global and/or local biological variations in the population, and a prior induced over each group explicitly encodes these variations. This work presents three novel contributions for shape representation, multiscale prior probability estimation and segmentation.

* Directional based segmentation: We have proposed an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. In the classical case, the Euclidean metric is locally multiplied by a scalar conformal factor (based on image information) such that the weighted length of curves lying on points of interest (typically edges) is small. We propose to add directionality to the factor, and show that one gets a well-defined minimization problem in the case that the factor defines a Finsler metric. Optimal curves may be obtained using the calculus of variations or dynamic programming. This methodology also makes connections to the important technique of graph-cuts.

* Statistical PDE methods for segmentation in shape space: This past year, we have proposed another method to perform shape-driven segmentation. In our approach, shapes are represented using binary maps, and linear PCA is utilized to provide shape priors for segmentation. Intensity based probability distributions are then employed to convert the given volume into a binary map representation, and a new energy functional is formulated whose minimization is performed using a parametric model for surface evolution in the shape space. Our algorithm is then applied to the segmentation of brain caudate nucleus and hippocampus from MRI data. Our validation shows that the proposed algorithm outperforms the log-likelihood based energy, converges in less than 5 iterations and is very obust to initialization. The overall algorithm is illustrates the potential for segmentation in shape space.

* Rule-based segmentation methods: We have continued this past year to develop segmentation methods based on heuristic rules provided to us by our Core 3 partners for segmenting various brain regions of interest in schizophrenia, e.g. the DLPFC and the striatum. The idea is to try to semi-automate these rules in order to forge an interactive tool for segmentation which can greatly shorten the time necessary for manual segmentation. Typically, these methods are used in conjunction with some Bayesian classifier which further aids to automating and in speeding up the given segmentation methodology.

* Tissue and structural segmentation via EM Segmenter: Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases segmentation is mostly performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We have developed an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior knowledge. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. The mapping between atlas and image space, structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the Maximum A posteriori Probability (MAP) estimation problem. These results are then fed into an Active Mean Field approach, which views the results as priors to a Mean Field approximation with a curve length prior. This segmentation framework has been ported into the NAMIC-toolkit and a first version of the tool is distributed with Slicer 3.

====='''Shape Analysis'''=====

* Wavelet based shape analysis: A shape representation that encodes variations at multiple scales can be useful as a rich feature set for shape analysis and classification. Combining tools from the existing shape analysis toolset, we extended it to include spherical wavelet coefficients (SWC) as features and compared the results obtained to shape analysis using a SPHARM-PDM representation. The rich SWC feature set allows the differentiation of shape differences at various scales as well as highly correlates with the existing SPHARM-PDM analysis but with increased statistical sensitivity. Wavelet coefficient shrinkage and dimension reduction are well-understood and have been widely researched for traditional types of wavelet decompositions but not much explored for the second generation wavelets. During the past year, we have developed a Bayesian model on our specific wavelet structure based on a population of surfaces. For each shape, the deviation from the mean is computed and is modeled as the sum of an unknown signal and a noise. This deviation is encoded by the wavelet transform and our goal is to estimate the wavelet coefficients belonging to the noiseless signal.

* Surface flattening for shape analysis: Flattened representations of undulated surfaces constitute an active area of research in the field of medical imaging and visualization, due to their extensive use for registration and shape analysis of various structures of interest. We have presented a method for flattening anatomical surfaces in an area preserving manner, while minimizing the geometrical distortion. This method is based on the theory of optimal mass transport and conformal mapping of surfaces. The key idea here is the use of a multiresolution scheme for the solution of optimal mass transport gradient descent equations which allows a fast and stable solution for optimal transport. The method has been implemented on a GPU, allowing us to flatten a 128 by 128 by 128 volume in about 5 seconds on a standard workstation.

* Curvature based population correspondence: We have extended the Minimum Description Length population based correspondence framework to include curvature based measurements, such as the Koenderink Shape Index S and Curvedness C in combination with the standard location information. Current methodology in population based correspondence is based mainly on minimizing distribution properties of surface point locations and are thus not invariant to alignment. We have favorably compared our combined "Curvature + Location" MDL to the standard MDL, as well as to the SPHARM approach, in complex structures, such as the striatal brain structure (composed of caudate, nucleus accumbens and putamen).

* Shape analysis toolset: A considerable amount of work was spent on the development aspect of the shape analysis tools. The distributed set of tools is continuously enhanced and the population based correspondence framework has been released as open source. All the tools including the visualization tool, KWMeshVisu, can be called directly from Slicer 3. The visualization tool allows the overlay of scalar, vector and ellipsoid data onto surfaces via versatile colormaps. The attributed surfaces are then visible within Slicer 3. This lean visualization tool fills a niche and is also used in our cortical thickness analysis tool. Also, while it is entirely possible to run all shape analysis steps by calling the individual modules, this is highly inefficient in a larger study. As a result we are developing a separate shape pipeline Slicer module that uses Batchmake to run the shape analysis pipeline as a distributed, background process. The whole shape analysis pipeline will thus become entirely encapsulated and accessible to the trained clinical collaborator.

* Particle based correspondence: We have developed a method for a multi-object correspondence optimization, and have applied it successfully to a proof-of-concept application for the analysis of brain structure complexes from a longitudinal study of pediatric autism. This new method for constructing compact statistical point-based models of ensembles of similar shapes does not rely on any specific surface parameterization, requires little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including non-manifold surfaces and objects of arbitrary topology. The method uses a dynamic particle system to optimize correspondence point positions across all structures by simultaneously maximizing both the geometric accuracy and the statistical simplicity of the model.

==== Key Investigators ====

* MIT: Kilian Pohl, Sandy Wells, Eric Grimson
* UNC: Martin Styner, Ipek Oguz, Guido Gerig, Xavier Barbero
* Utah: Ross Whitaker, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer
* GaTech: Allen Tannenbaum, John Melonakos, Tauseef ur Rehman, Shawn Lankton, Ramsey Al-Hakim, Eric Pichon, Delphine Nain, Oleg Michailovich, Yogesh Rathi, James Malcolm
* Isomics: Steve Pieper
* GE: Bill Lorensen, Jim Miller
* Kitware: Luis Ibanez, Karthik Krishnan,
* UCLA: Michael J. Pan, Jagadeeswaran Rajendiran
* BWH: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt
* Dartmouth: Andrew Saykin
* UCI: James Fallon

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Structural_Image_Analysis NA-MIC Projects on Structural Image Analysis].

=== 2.3 Functional MRI Analysis Theme ===

====Progress====

During this year, the focus of the algorithms and the engineering
cores has been on the structural and DTI analysis. While we continued
to expand the methods and the infrastructure in NAMIC-kit to support
fMRI analysis, as well as using the analysis tools to perform clinical
studies, the emphasis of the work this year has been on integrating
the fMRI analysis with other modalities and supporting other
modalities.

===== Clinical Studies =====

We would like to highlight several clinical studies within NAMIC that
focused on fMRI data and its relationship with other imaging
modalities:

* Imaging Phenotypes in Schizophrenics and Controls: Functional connectivity of the DLPFC by genotype was investigated emplying partial least squares (PLS) correlation analysis. PLS is a multivariate analytical technique used to summarize large neuroimaging data sets in such a way as to correlate patterns of activation with a variable(s) of interest (i.e., DLPFC activity). In the most recent analysis, the DRD1 genotype was used as a grouping variable. This analysis has been submitted for publication (Tura, Turner, Fallon, Kennedy, and Potkin. Genetic Impact on Functional Connectivity in Schizophrenics During a Working Memory Task). Working memory performance did not differ significantly between the two cohorts. However, imaging-genetic analysis showed a significant difference (P< 0.05) between the circuitry engaged by each group. Significance and reliability of the resulting imaging-behavioral patterns within each genotype were assessed by 200 bootstrap and 500 permutation tests, respectively. In one group, the circuitry included the temporal pole, the insula, the dorsolateral prefrontal cortex, and the Brodmann Areas (BA) 1,2,3,4,6,11 and 21, while the other group showed a network comprising the tectum, precuneus retroplenial, vermis, substantia nigra, BA 22,39,8, and 9. The DRD1 polymorphic site may characterize circuitry differences in schizophrenic patients.

*Path-Of-Interest Analysis (joint DTI/fMRI modeling): We collected preliminary data using an application of the “optimal path analysis”. In this analysis, we extracted group fMRI activation due to the Stroop effect (an attentional experiment where incongruent color, in which the word is written, competes with name of the color itself, activating areas responding to conflict monitoring and selection) separately for controls and schizophrenics. This resulted in three clusters of activation, one in the right Dorsolateral Prefrontal Cortex, a second in the Anterior Cingulate Gyrus, and a third in the Medial Parietal Lobe. In the next step, we placed activation clusters in each individual space, by reversing normalization parameters used during fMRI analysis. Finally, EPI fMRI scans were co-registered to DTI scans, and the same registration parameters were applied to activation maps (fMRI results). Regions of activation were used as start and destination points for optimal path analysis, which resulted in three separate paths of optimal connectivity for each subject. The probability of the connections were then calculated for each path and each subject, and compared between groups. In our preliminary analysis, we included 10 control subjects and 10 chronic schizophrenics. Our results demonstrated a relationship between Stroop Effect fMRI activation in the medial parietal area and optimal path connectivity between parietal and cingulate activation sites in schizophrenics (rho=-0.56; p=0.047), which was not observed in controls. These findings suggest that decreased connectivity may result in schizophrenics relying more on posterior parts of the executive attentional network during performance of the Stroop task.

*Hippocampal and frontal memory circuitry abnormalities in schizophrenia: Relation of diffusion, morphometric and fMRI markers: We performed a combined DTI, fMRI, and morphometric study on 13 patients with schizophrenia (SZ) and 14 HC. We identified areas of increased trace diffusivity (TD) in the hippocampal and insular regions as well as areas of reduced fractional anisotropy (FA) in left frontal white matter in SZ relative to HC (p<.01). Voxel based morphometry analyses in a subset of these subjects showed corresponding reductions in gray matter density in hippocampal and insular regions in patients relative to controls (p<.01). Analysis of fMRI results from the novel vs. repeated word contrast from the event-related auditory verbal episodic memory encoding/retrieval task in a subset of the subjects indicated reduced activation in frontal and temporal regions, as well as increased activation in posterior cingulate, retrosplenial, and thalamic regions in SZ relative to HC (p<.05). Further analysis showed that left frontal white matter FA was associated with activation in the left and right hippocampi as well as other frontal and temporal regions, but inversely related to activation in the retrosplenial/posterior cingulate region (p<.05). These initial findings indicate a pattern of relationships between of structural and functional brain abnormalities in schizophrenia and demonstrate the feasibility of integrated quantitative analyses across modalities.

===== Methods =====

During this year, we continued methodological development along two
directions:

*Improving fMRI detectors by incorporating Markov priors on the activation state. We integrated the improved detector into Slicer and performed substantial validation of the methods using fBIRN data provided by the UC Irvine group. Journal paper on the method has been submitted to IEEE TMI.

*Improving registration of EPI images to anatomical scans through modeling of the EPI distortions. We demonstrated an initial model that uses segmentation of the structural scan to predict the distortions in the EPI images. The preliminary results are quite encouraging.

==== Key Investigators ====

* MIT: Polina Golland, Sandy Wells, Wanmei Ou, Claire Poynton
* BWH: Martha Shenton, Marek Kubicki
* Dartmouth: Andy Saykin
* UCI: Jessica Turner, Stephen Potkin
* Toronto: Jim Kennedy

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#fMRI_Analysis NA-MIC Projects on fMRI Analysis].

=== 2.4 [[NA-MIC-Kit|NA-MIC Kit]] Theme ===

==== Progress ====
The continuing vision of the NA-MIC Kit is to provide an open source set of software tools and methodologies that will serve as the foundation for medical image computing projects for both academic and commercial use. Key elements of this vision are:

# ''Unrestrictive License.'' Users of the Kit are free to distribute their derived works under any license suitable to their needs.
# ''Cross Platform.'' This software set can be adapted to the best available price-performance computer systems for any particular use.
# ''Extensible Application Framework.'' New techniques and algorithms can be quickly integrated into a working system. Sophisticated user interfaces can be generated easily through automated processes. Sophisticated toolsets such as ITK, VTK, and KWWidgets are available to create and deploy applications quickly.
# ''Quality Software Process.'' Developers and users can rely on accurate and well documented behavior from all the parts of the Kit.
# ''Sustainable Community.'' Users are actively involved in the design process of the Kit. Documentation, training materials, and hands-on sessions are available and well publicized to the community.

====='''Slicer3'''=====

A major focus of the third year was the implementation of the Slicer3 in the NAMIC Kit. This effort addressed Item #3 above ''Extensible Application Framework.'' The previous two years of the NAMIC project, which entailed gathering requirements from Cores 1 and 3, and developing the computational foundation, toolsets, and software process, came together in the Slicer3 application platform.

Core 2 worked hard to insure that the Slicer 3 application serves, and will continue to serve, as a productive technology deployment platform. The application framework was designed carefully to provide ease-of-use, both in terms of interaction and software integration. Advanced capabilities, including the ability to launch large-scale grid computing, was designed into the application. Some of the key features of the Slicer3 application completed in the third year include the following.
* Advanced application framework including a tuned GUI for ease of use, undo/redo capabilities, 2D/3D view windows, and support for advanced interaction techniques such as 3D widgets. The application provides viewers for displaying slices, volumes, and models including the ability to edit properties. A built in transformation pipeline enables users to confidently import, edit and display data in a consistent coordinate system.
* The application is data-driven based on the next generation MRML scene description file format. Backward compatibility to Slicer 2.x MRML files is preserved.
* A module plug-in architecture and execution model that enables researchers to package and integrate their software into the Slicer3 framework. Plug-in modules can be implemented in a variety of programming languages, and are described using a simple XML description. These modules, when located and loaded into Slicer3, have the capability to automatically generate their user interface, which is seamlessly integrated into the Slicer3 GUI.
* Support for editing and marking data including support for fudicials, paint and draw editors.
* The creation of several simple plug-in modules, including the conversion of previous Slicer 2.x modules to the new Slicer3 architecture.

====='''EM Segment'''=====

As an application framework, Slicer3 provides tools for loading, viewing, measuring, editing, and saving data. To support advanced medical image analysis, plug-in modules are required in conjunction with the Slicer3 core. To demonstrate the capabilities of the framework we implemented the [http://www.na-mic.org/Wiki/index.php/Slicer3:EM EM Segment] module, a sophisticated and proven method for automatically segmenting complex anatomical structures.

To use this module, users specify parameters defining the image protocol and the anatomical structures of interests. This process results in a template that the module uses to automatically segment large data sets. The template is composed of atlas data and a non-trivial collection of parameters for the EM Segment algorithm ([[Media:Pohl-miccai-short-2005.pdf|Pohl et al.]]). Once the parameters are specified, the target images are segmented using the algorithm; if the results are satisfactory, the template is saved and can be used later to segment new images (via the GUI or batch processing). If the results are unsatisfactory, the parameters can be modified and the segmentation re-run.

Besides successfully demonstrating the use of complex algorithms in the Slicer3 framework, this effort also led us to develop tools, including modifications to the underlying KWWidgets GUI toolkit, to support module workflow. With these tools, it is possible to simplify complex modules by dividing the complicated template specification task into a number of smaller, intuitive steps. These steps are enforced by the GUI and reduce the potential for user error, while improving the overall user interface.

====='''Quality Software Process'''=====

Building on [http://lwn.net/Articles/188693/ last year's success with the KDE community], the NAMIC community continued to extend its world-class, open source software process tools CMake, DART, CTest and DART. These tools, which form the core of a quality-oriented, test-driven development (TDD) software process. In particular, the CPack system is now able to automatically package and distibute code, libraries, executables and installers across all of NAMIC's supported platforms (i.e., Linux, Windows, Mac). This enables the NAMIC developer community to rapidly deploy software tools to the user community.

==== Key Investigators ====

* GE: Bill Lorensen, Jim Miller, Xiaodong Tao, Dan Blezek
* Isomics: Steve Pieper, Alex Yarmarkovich
* Kitware: Will Schroeder, Luis Ibanez, Brad Davis, Andy Cedilnik, Sebastien Barre, Bill Hoffman
* UCLA: Mike Pan, Jagadeeswaran Rajendiran
* UCSD: Neil Jones, Jeffrey Grethe, Mark Ellisman
* BWH: Nicole Aucoin, Katie Hayes, Wendy Plesniak, Mike Halle, Gordon Kindlmann, Raul San Jose Estepar, Haiying Liu, Ron Kikinis
* MIT: Lauren O'Donnell, Kilian Pohl

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#NA-MIC_Kit NA-MIC Kit Projects].

== 3. Highlights ==
The third year of the NAMIC project saw continued development and dissemination of medical image analysis software. With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NAMIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NAMIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

=== 3.1 Advanced Algorithms ===
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NAMIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NAMIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NAMIC Sandbox in preparation for formal acceptance into the NAMIC Kit.

=== 3.2 Technology Deployment Platform: Slicer3 ===
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NAMIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NAMIC Kit, and are now available to all other modules built on the Slicer3 framework.

=== 3.3 Outreach and Technology Transfer ===
Cores 4-5-6 continue to support, train and dissemniate to the NAMIC community, and the broader biomedical computing community.
* NAMIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NAMIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair.

== 4. Impact and Value to Biocomputing ==

In NA-MIC's third year, it is evident that NA-MIC is developing a
culture, environment, and resources to foster and incite collaborative
research in medical image analysis that draws together mathematicians,
computer scientists, software engineers, and clinical
researchers. These artefacts of NA-MIC impact how NA-MIC
operates, make NA-MIC a fulcrum for NIH funded research,
and draws new collaborators from across the country
and around the world to NA-MIC.

=== 4.1 Impact within the Center ===

Within the center, the NA-MIC organization, NA-MIC processes, and the
NA-MIC calendar has permeated the research. The organization is
nimble, forming ad hoc distributed teams within and between cores to
address specific biocomputing tasks. Information is shared freely on
the NA-MIC Wiki, on the weekly Engineering telephone conferences, and
in the NA-MIC Subversion source code repository. The software
engineering tools of CMake, Dart 2 and CTest, CPack, and KWWidgets
facilitate a cross platform software environment for medical image
analysis that be easily built, tested, and distributed to
end-users. Core 2 has provided a platform, Slicer 3, that allows Core
1 to easily integrate new technology and deliver this technology in an
end user application to Core 3. Core 1 has developed a host of
techniques to apply to structural and diffusion analysis which are
under evaluation by Core 3. Major NA-MIC events, such as the annual
All Hands Meeting, the Summer Project Week, the Spring Algorithms
meeting, and Engineering Teleconferences are avidly attended by NA-MIC
researchers as opportunities to foster collaborations.

=== 4.2 Impact within NIH Funded Research ===

Within NIH funded research, NA-MIC continues to forge relationships
with other large NIH funded projects such as BIRN, caBIG, NAC, and
IGT. Here, we are sharing the NA-MIC culture, engineering practices,
and tools. BIRN hosts data for the NA-MIC researchers and NA-MIC hosts
BIRN wikis. caBIG lists the 3D Slicer among the applications available
on the National Cancer Imaging Archive. NAC and IGT use the NA-MIC
infrastructure and are involved in the development of the 3D
Slicer. BIRN recently held an event modeled after the NA-MIC Project
Week. NA-MIC has become a resource on open source licensing to the medical image analysis community.

NA-MIC is also attracting NIH funded collaborations. Two grants have
been funded under PAR-05-063 to collaborate with NA-MIC: ''Automated FE Mesh Development'' and ''Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI''. Five additional
applications to collaborate with NA-MIC via the NCBC collaborative
grant mechanism are under consideration. Additional grant applications
submitted under other calls are planning to use and extend the NA-MIC tools.

=== 4.3 National and International Impact ===

NA-MIC events and tools garner national and international interest.
There were nearly 100 participants at the NA-MIC All Hands Meeting in
January 2007, with many of these participants from outside of NA-MIC.
Several researchers from outside the NA-MIC community have attended
the Summer Project Weeks and the Winter Project Half-Weeks to gain
access to the NA-MIC tools and people. These external researchers are
contributing ideas and technology back into NA-MIC.

Components of the NA-MIC kit are used globally. The software
engineering tools of CMake, Dart 2 and CTest are used by many open
source projects and commercial applications. For example, the K Desktop Environment (KDE) for
Linux and Unix workstations uses CMake and Dart. KDE is one of the
largest open source projects in the world. Many open source projects and
commercial products are benefiting from the NA-MIC related
contributions to ITK and VTK. Finally, Slicer 3 is being used as an
image analysis platform in several fields outside of medical image
analysis, in particular, biological image analysis, astronomy, and industrial inspection.

NA-MIC co-sponsored the ''Workshop on Open Science'' at the Medical
Image Computing and Computer-Assisted Intervention (MICCAI) 2006
conference. The proceedings of the workshop are published on the
electronic Insight Journal, another NIH-funded activity.

Over 50 NA-MIC related publications have been produced since the inception of the center.

== 5.NA-MIC Timeline ==

This section provides a table of NAMIC timelines from the original proposal that graphically depicts completed tasks/goals in years 1, 2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines have also been described.

[[2007_Scientific_Report_Timeline|2007 Scientific Report Timeline]]

== Appendix A Publications ==

http://www.na-mic.org/Wiki/index.php/Publications

== Appendix B EAB Report ==

===EAB Report===
The NA-MIC External Advisory Board (EAB), chaired by Professor Chris Johnson of the University of Utah, met at the annual All-Hands Meeting. After individual presentations by NA-MIC investigators and open as well as closed-door EAB discussion, the Board provided its independent expert assessment of the Center.

[https://share.spl.harvard.edu/grants/u54/2007/APR%202007/NAMIC-EAB-Report07-ver5.doc This is a password protected link to the EAB report.]

===Response to EAB Report===

The NA-MIC leadership discussed the excellent suggestions made by the EAB, and in the following section we respond to each of them.

''EAB: The Center should actively pursue methods to distribute their software and other resources to a broader community. As we noted in last year’s EAB Report, the Center is doing a good job at distributing their software toolkit, however, largely the distribution has been focused internally to NA-MIC collaborators. The EAB recommends that the Center work to more broadly distribute its software to the larger community of neuroimaging researchers. In addition, because the Center’s software tools will also be useful to biomedical researchers outside of neuroimaging, the EAB recommends the Center consider additional methods to “get the word out” about their resources.''

NA-MIC: The focus in the last two years of the Center has been to broaden the outreach effort to the neuroimaging community and to other biomedical researchers outside of this community. Many algorithms have been developed in ITK/VTK, and then ported to Slicer to try to make them as accessible as possible. We believe that the ITK/VTK platforms have been quite widely disseminated. However, as the EAB has noted, the current training and dissemination funds are limited, and the Center leadership will continue to look for creative solutions to get the word out within these resource constraints.

''EAB: The Center is well suited to assess and compare algorithms. An important question in image analysis involves understanding which image-processing algorithm is best suited for a particular task. Given the large number of different image analysis algorithms being created by NA-MIC, the Center is in a good position to evaluate the effectiveness of different algorithms and to make appropriate recommendations.''

NA-MIC: This topic has been discussed extensively within the Center, and we have addressed this issue in a manner that is aligned with our core expertise in open source software. More specifically, we will continue to build upon our strength and provide infrastructure and tools needed for open science, as well as to train researchers in the use of these tools. It is our hope that by providing easy access to these tools, we will be facilitating the process of comparison between algorithms for various biomedical tasks. This being said, many times there is no optimal algorithm for every imaging purpose. A certain segmentation routine may be excellent for one purpose, but not for another. This is a well recognized problem in image processing and computer vision, and not a fundamental limitation of medical imaging. Comparisons, however, among different algorithms to determine which algorithms are best for which questions being addressed is nonetheless important and we have made some inroads here. Sylvain Bouix, for example, has a paper in press in Neuroimage on evaluating brain tissue classifiers without ground truth, which compares several different segmentation algorithms. Also, in the shape analysis field, every NA-MIC related publication performs the necessary comparisons. Furthermore, Martin Styner and coworkers authored a paper on shape analysis comparisons at ISBI 2006. In addition, Martin is co-organizing a caudate and liver segmentation comparison workshop at MICCAI 2007, with currently over 35 registered participating methods. Our philosophy is to make a large menu of algorithmic methodologies available in as easy to use form as possible, so as to encourage the kind of comparisons recently done by Drs. Bouix and Styner.

''EAB: The Center should consider additional techniques to validate and verify techniques and software. The EAB was pleased to see the progress the Center made in quantifying and validating their algorithms and implementations within the NA-MIC Kit. However, because this is such an important issue, the EAB recommends the Center continue to make progress in this area.
''

NA-MIC: The Center intends to continue its effort in validation and verification of its software implementations. Center researchers have also come up with original techniques for validation, e.g., the Laplacian method for comparing the accuracy of segmentations. These validation techniques have been used to drive new original algorithmic as well. In addition, work within the Center has continued to compare manual measures of brain structure, i.e., the caudate as a gold standard for automated measures of caudate in the same subjects. This kind of validation will also continue.

''EAB: The Center should generate a set of success criteria. The EAB recommends that the Center create a set of benchmarks of success that it will use in future years to measure the Center’s success and as ways to evaluate the Center’s continuing progress and at the time of the Center’s first renewal.''

NA-MIC: We note that we have established benchmarks for success that are similar to criteria used by study sections for reviewing the success of other grant mechanisms, although also includes specific criteria for NA-MIC, and includes:

• Papers in peer reviewed publications

• Technology transfers from Core 1 to Core 3 researchers

• Software released as part of the NA-MIC Kit

• Training outside researchers in the use of the NA-MIC Kit

= Logistics =

== Schedule and process for preparation of this report ==

* March 30 - Assign section/theme leads (Ron). Last year: introduction (marty), structural analysis (allen, martin), dti (guido, marty/marek), fmri (polina, andy), namic kit(bill), timeline (ross), highlights (will), impact (bill).
*April 7,10 - tcons with Marty, Ross, Will, Tina to finalize the layout, process, and timeline of report.
* April 13 - update projects list using last year's [[NA-MIC_Collaborations]] and [[2007_Project_Half_Week#Projects|projects pursued at the half week in SLC]]. Remind investigators to update individual pages. (Tina)
* April 23- complete project description pages in updated list: [[NA-MIC_Collaborations]] (all project owners).
* April 30 - complete section summaries, introduction, highlights, impact, timeline (owners of these topics)
* May 8 - submit wiki report to NA-MIC editor, Ann (Tina)
* May 17 - submit Edited report to Rachana in pdf format (Ann)
* May 25 - submit final report, if changes are needed (Ann)
* May 29 - ship final package to NIH (Rachana)

== Guidelines from NIH Program Officer ==
===NIH Reviewer Comments on Yr2 APR (July 2006)===
The following additional guidelines were provided by the NIH progress review committee after they reviewed the 2006 report:

1. Comment on obstacles and challenges encountered during the year.

2. Comment on outcomes of DBPs.

3. Create a context for the report in the Introduction.

4. Comment on how all the themes as a whole are moving forward.

5. Full citations should be given within the report as footnotes or in a bibliography, not just as hyperlinks.

6. Explain time-lines with text including reasons for delays. Link timelines to papers and citations.

7. Define terms used in the timeline to describe the amount of progress being made, especially with respect to software development.

8. Put priority weights on completed tasks within timeline. Note if a task is a stepping stone or a final goal.

9. Make sure the projects presented in the annual report are also listed in the project pages of the NAMIC Wiki and vice versa.

10. In the budget pages, please elaborate on the number and locations for travel.

===Feb 2006===
The following guidelines were provided by Grace Peng, NA-MIC program officer, in Feb 2006.

The key is to synthesize all the individual elements into bigger picture stories that really speak of each area’s impact to the community.

The specialized scientific report should have the following format:

# Introductory page describing the new grouping of NAMIC project themes.
# A description of progress in each NAMIC project theme (not to exceed 2 pages each), tying together relevant activities from participating subcomponents and referencing cores in parentheses.
# A table of NAMIC timelines (from original proposal), graphically depicting completed tasks/goals in years 1,2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines should be described.
# A description of 3 highlights selected from all NAMIC projects to showcase NAMIC.
# A discussion of NAMIC’s impact and value to the biocomputing community this year.

2007 Annual Scientific Report

2007-05-20T17:02:24Z

Shenton: /* Response to EAB Report */

Back to [[2007_Progress_Report]]

For reference:

*[[2006_Progress_Report]]

*[[Media:2005_NAMIC_Specialized_ProgReport.pdf|2005 Annual Scientific Report]]

== 1. Introduction ==

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with espect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs continue to include neuropsychiatric disorders such as Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Schizophrenia (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a adopting a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

(Please note that this report is available on the NA-MIC wiki at the following url: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report)

== 2. Four Main Themes ==

This year's activities focus on four main themes: Diffusion Image Analysis, Structural Analysis, Functional MRI Analysis, and the NA-MIC Kit. Each of the following sections begins with an overview of the theme, provides a progress update and list of key investigators, and concludes with a set of links to additional information for individual projects in that theme.

These thematic activities involve scientists from each of the 7 NA-MIC cores (Appendix).

* [http://www.na-mic.org/Wiki/index.php/Algorithm:Main Core 1 Algorithms]-Ross Whitaker PI
* [http://www.na-mic.org/Wiki/index.php/Engineering:Main Core 2 Engineering]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/DBP:Main Core 3 Driving Biological Problems] DBP1-Martha Shenton PI / DBP2-Andy Saykin PI / DBP3-Steven Potkin PI
* [http://www.na-mic.org/Wiki/index.php/Service:Main Core 4 Service]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/Training:Main Core 5 Training]-Randy Gollub PI
* [http://www.na-mic.org/Wiki/index.php/Dissemination:Main Core 6 Dissemination]-Tina Kapur Co-PI; Steve Pieper Co-PI
* [http://www.na-mic.org/Wiki/index.php/Leadership:Main Core 7 Leadership]-Ron Kikinis

=== 2.1 Diffusion Image Analysis Theme ===

==== Progress ====
Over the past year, we have continued to develop a number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical diffusion tensor analysis. These tools have been already integrated into diffusion dedicated software (e.g., Fiber Viewer-UNC). In addition, we started developing tools for group data analysis, such as white matter atlases, and tools for data integration, that would combine and interface among different imaging modalities (such as structural, fMRI and DTI), to better estimate anatomical and functional connectivity. This software has continued to be used in multiple clinical projects involving several psychiatric populations. Below we provide detailed progress in the area of diffusion image analysis.

==== Fiber Tract Extraction and Analysis ====
* Since last year, all of the algorithms for fiber tractography and anisotropy estimation have been implemented in both the “Fiber Viewer” and “Slicer” packages, and now the resulting methods are being applied to clinical studies. Fiber Tract integrity has been investigated and results have been presented at several international conferences for four key fiber tracts in schizophrenia (corpus callosum, fornix, inferior occipito-frontal fasciculus, and uncinate fasciculus). These tracts have been extracted, and fractional anisotropy has been compared between groups, demonstrating fronto-temporal connectivity abnormalities in schizophrenia.

* In addition to clinical studies, teams have been working on other methods to define and estimate brain connectivity. The most important developments in this regard included volumentric connectivity and stochastic tractography measures. For volumentric connectivity, a PDE-based approach to white matter connectivity from DTI has been developed, that is founded on the principal of minimal paths through the tensor volume. This method computes a volumetric representation of a white matter tract given two endpoint regions. In addition, statistical methods for quantifying the full tensor data along these pathways have been also developed. Directional PDE-based flows have been proposed and implemented for a similar purpose. Further, an approach called "stochastic tractography," has been developed to calculate probability of two regions being connected by the fiber tract. This should be especialy useful when tract has to go through the regions characterized by low diffusion anisotropy.

* Some initial attempts have been made to population based analysis of DT-MRI. One method is based on the unbiased non-rigid registration of a population to a common coordinate system. The registration jointly produces an average DTI atlas, which is unbiased with respect to the choice of a template image, along with a diffeomorphic correspondence for each image. The anatomically significant correspondence provides a basis for comparison of tensor features and fiber tract geometry in clinical studies.

==== Fractional Anisotropy Analysis ====
* We have used tools developed last year, and applied them to our population of chronic schizophrenia subjects in order to investigate two fiber tracts: the cingulum bundle and the corpus callosum. In the case of cingulum bundle, manually drawn regions of interest and Finsler geometry were used to extract entire cingulum bundle fiber tract, and FA was estimated along the tract, and compared between groups. Corpus Callosum cross sectional area and its probabilistic subdivisions were determined automatically from the structural MRI scans using a model based deformable contour segmentation. The structural scan was then co-registered with the DTI scan and the anatomical corpus callosum subdivisions were propagated to the associated FA map, and compared between groups, demonstrating deficient interhemispheric communication in schizophrenia.

==== Integration of fMRI and DTI, Path-of-Interest Analysis ====
* Progress has been also made in the development of a tool that would successfully combine and integrate functional and anatomical information. The Optimal Path Analysis method has been applied to the Harvard- BWH fMRI dataset, and anatomical connections between regions active during the fMRI experiment have been extracted for each subject. The connectivity has calculated and compared between groups. This tool is now being ported to Slicer3.

==== Key Investigators ====

* BWH: Martha Shenton, Marek Kubicki, Marc Niethammer, Sylvain Bouix, Jennifer Fitzsimmons, Katarina Quintis, Doug Markant, Kate Smith, Georgia Bushell, Mark Dreusicke, Carl-Fredrik Westin, Raul San Jose, Gordon Kindlmann
* MGH: Bruce Fischl, Denis Jen, David Kennedy
* MIT: Lauren O'Donnell, Polina Golland, Tri Ngo
* UCI: James Fallon, Martina Panzenboeck
* UNC: Guido Gerig, Isabelle Corouge, Casey Goodlett, Martin Styner
* Utah: Tom Fletcher, Ross Whitaker, Saurav Basu, Davis McKay
* GA Tech: Eric Pichon, John Melonakos, Xavier LaFaucheur, Vandana Mohan, Allen Tannenbaum
* Dartmouth: John West, Andrew Saykin, Laura Flashman, Paul Wang, Heather Pixley, Robert Roth
* Isomics: Steve Pieper
* Kitware: Luis Ibanez

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Diffusion_Image_Analysis NA-MIC Projects on Diffusion Image Analysis].

=== 2.2 Structural Analysis Theme ===

==== Progress ====

Within the NAMIC's structural analysis theme, the main topics of interest are structural segmentation, registration and shape analysis. These topics are of course intertwined, e.g., segmentation or registration can directly deliver structural correspondence used in shape analysis, or in turn shape modeling is necessary for good structural segmentations. Here is a selection of progress highlights in the structural analysis theme

====='''Segmentation'''=====

* Wavelet based structural segmentation: We have developed a spherical wavelet based framework for the segmentation of selected brain structures, such as the hippocampus or the caudate nucleus. An automated segmentation of such structures must be highly accurate and include high frequency variations in the surface. Since shape representation is a key component of the segmentation, it must be rich enough to express shape variations at various frequency levels, from low harmonics to sharp edges. Medical object segmentation with deformable models and statistical shape modeling may be combined to obtain a more robust and accurate segmentation. To address this issue, a decomposable spherical wavelet based shape representation targeted to the population seems natural, where the shape parameters are separated into groups that describe independent global and/or local biological variations in the population, and a prior induced over each group explicitly encodes these variations. This work presents three novel contributions for shape representation, multiscale prior probability estimation and segmentation.

* Directional based segmentation: We have proposed an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. In the classical case, the Euclidean metric is locally multiplied by a scalar conformal factor (based on image information) such that the weighted length of curves lying on points of interest (typically edges) is small. We propose to add directionality to the factor, and show that one gets a well-defined minimization problem in the case that the factor defines a Finsler metric. Optimal curves may be obtained using the calculus of variations or dynamic programming. This methodology also makes connections to the important technique of graph-cuts.

* Statistical PDE methods for segmentation in shape space: This past year, we have proposed another method to perform shape-driven segmentation. In our approach, shapes are represented using binary maps, and linear PCA is utilized to provide shape priors for segmentation. Intensity based probability distributions are then employed to convert the given volume into a binary map representation, and a new energy functional is formulated whose minimization is performed using a parametric model for surface evolution in the shape space. Our algorithm is then applied to the segmentation of brain caudate nucleus and hippocampus from MRI data. Our validation shows that the proposed algorithm outperforms the log-likelihood based energy, converges in less than 5 iterations and is very obust to initialization. The overall algorithm is illustrates the potential for segmentation in shape space.

* Rule-based segmentation methods: We have continued this past year to develop segmentation methods based on heuristic rules provided to us by our Core 3 partners for segmenting various brain regions of interest in schizophrenia, e.g. the DLPFC and the striatum. The idea is to try to semi-automate these rules in order to forge an interactive tool for segmentation which can greatly shorten the time necessary for manual segmentation. Typically, these methods are used in conjunction with some Bayesian classifier which further aids to automating and in speeding up the given segmentation methodology.

* Tissue and structural segmentation via EM Segmenter: Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases segmentation is mostly performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We have developed an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior knowledge. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. The mapping between atlas and image space, structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the Maximum A posteriori Probability (MAP) estimation problem. These results are then fed into an Active Mean Field approach, which views the results as priors to a Mean Field approximation with a curve length prior. This segmentation framework has been ported into the NAMIC-toolkit and a first version of the tool is distributed with Slicer 3.

====='''Shape Analysis'''=====

* Wavelet based shape analysis: A shape representation that encodes variations at multiple scales can be useful as a rich feature set for shape analysis and classification. Combining tools from the existing shape analysis toolset, we extended it to include spherical wavelet coefficients (SWC) as features and compared the results obtained to shape analysis using a SPHARM-PDM representation. The rich SWC feature set allows the differentiation of shape differences at various scales as well as highly correlates with the existing SPHARM-PDM analysis but with increased statistical sensitivity. Wavelet coefficient shrinkage and dimension reduction are well-understood and have been widely researched for traditional types of wavelet decompositions but not much explored for the second generation wavelets. During the past year, we have developed a Bayesian model on our specific wavelet structure based on a population of surfaces. For each shape, the deviation from the mean is computed and is modeled as the sum of an unknown signal and a noise. This deviation is encoded by the wavelet transform and our goal is to estimate the wavelet coefficients belonging to the noiseless signal.

* Surface flattening for shape analysis: Flattened representations of undulated surfaces constitute an active area of research in the field of medical imaging and visualization, due to their extensive use for registration and shape analysis of various structures of interest. We have presented a method for flattening anatomical surfaces in an area preserving manner, while minimizing the geometrical distortion. This method is based on the theory of optimal mass transport and conformal mapping of surfaces. The key idea here is the use of a multiresolution scheme for the solution of optimal mass transport gradient descent equations which allows a fast and stable solution for optimal transport. The method has been implemented on a GPU, allowing us to flatten a 128 by 128 by 128 volume in about 5 seconds on a standard workstation.

* Curvature based population correspondence: We have extended the Minimum Description Length population based correspondence framework to include curvature based measurements, such as the Koenderink Shape Index S and Curvedness C in combination with the standard location information. Current methodology in population based correspondence is based mainly on minimizing distribution properties of surface point locations and are thus not invariant to alignment. We have favorably compared our combined "Curvature + Location" MDL to the standard MDL, as well as to the SPHARM approach, in complex structures, such as the striatal brain structure (composed of caudate, nucleus accumbens and putamen).

* Shape analysis toolset: A considerable amount of work was spent on the development aspect of the shape analysis tools. The distributed set of tools is continuously enhanced and the population based correspondence framework has been released as open source. All the tools including the visualization tool, KWMeshVisu, can be called directly from Slicer 3. The visualization tool allows the overlay of scalar, vector and ellipsoid data onto surfaces via versatile colormaps. The attributed surfaces are then visible within Slicer 3. This lean visualization tool fills a niche and is also used in our cortical thickness analysis tool. Also, while it is entirely possible to run all shape analysis steps by calling the individual modules, this is highly inefficient in a larger study. As a result we are developing a separate shape pipeline Slicer module that uses Batchmake to run the shape analysis pipeline as a distributed, background process. The whole shape analysis pipeline will thus become entirely encapsulated and accessible to the trained clinical collaborator.

* Particle based correspondence: We have developed a method for a multi-object correspondence optimization, and have applied it successfully to a proof-of-concept application for the analysis of brain structure complexes from a longitudinal study of pediatric autism. This new method for constructing compact statistical point-based models of ensembles of similar shapes does not rely on any specific surface parameterization, requires little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including non-manifold surfaces and objects of arbitrary topology. The method uses a dynamic particle system to optimize correspondence point positions across all structures by simultaneously maximizing both the geometric accuracy and the statistical simplicity of the model.

==== Key Investigators ====

* MIT: Kilian Pohl, Sandy Wells, Eric Grimson
* UNC: Martin Styner, Ipek Oguz, Guido Gerig, Xavier Barbero
* Utah: Ross Whitaker, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer
* GaTech: Allen Tannenbaum, John Melonakos, Tauseef ur Rehman, Shawn Lankton, Ramsey Al-Hakim, Eric Pichon, Delphine Nain, Oleg Michailovich, Yogesh Rathi, James Malcolm
* Isomics: Steve Pieper
* GE: Bill Lorensen, Jim Miller
* Kitware: Luis Ibanez, Karthik Krishnan,
* UCLA: Michael J. Pan, Jagadeeswaran Rajendiran
* BWH: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt
* Dartmouth: Andrew Saykin
* UCI: James Fallon

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Structural_Image_Analysis NA-MIC Projects on Structural Image Analysis].

=== 2.3 Functional MRI Analysis Theme ===

====Progress====

During this year, the focus of the algorithms and the engineering
cores has been on the structural and DTI analysis. While we continued
to expand the methods and the infrastructure in NAMIC-kit to support
fMRI analysis, as well as using the analysis tools to perform clinical
studies, the emphasis of the work this year has been on integrating
the fMRI analysis with other modalities and supporting other
modalities.

===== Clinical Studies =====

We would like to highlight several clinical studies within NAMIC that
focused on fMRI data and its relationship with other imaging
modalities:

* Imaging Phenotypes in Schizophrenics and Controls: Functional connectivity of the DLPFC by genotype was investigated emplying partial least squares (PLS) correlation analysis. PLS is a multivariate analytical technique used to summarize large neuroimaging data sets in such a way as to correlate patterns of activation with a variable(s) of interest (i.e., DLPFC activity). In the most recent analysis, the DRD1 genotype was used as a grouping variable. This analysis has been submitted for publication (Tura, Turner, Fallon, Kennedy, and Potkin. Genetic Impact on Functional Connectivity in Schizophrenics During a Working Memory Task). Working memory performance did not differ significantly between the two cohorts. However, imaging-genetic analysis showed a significant difference (P< 0.05) between the circuitry engaged by each group. Significance and reliability of the resulting imaging-behavioral patterns within each genotype were assessed by 200 bootstrap and 500 permutation tests, respectively. In one group, the circuitry included the temporal pole, the insula, the dorsolateral prefrontal cortex, and the Brodmann Areas (BA) 1,2,3,4,6,11 and 21, while the other group showed a network comprising the tectum, precuneus retroplenial, vermis, substantia nigra, BA 22,39,8, and 9. The DRD1 polymorphic site may characterize circuitry differences in schizophrenic patients.

*Path-Of-Interest Analysis (joint DTI/fMRI modeling): We collected preliminary data using an application of the “optimal path analysis”. In this analysis, we extracted group fMRI activation due to the Stroop effect (an attentional experiment where incongruent color, in which the word is written, competes with name of the color itself, activating areas responding to conflict monitoring and selection) separately for controls and schizophrenics. This resulted in three clusters of activation, one in the right Dorsolateral Prefrontal Cortex, a second in the Anterior Cingulate Gyrus, and a third in the Medial Parietal Lobe. In the next step, we placed activation clusters in each individual space, by reversing normalization parameters used during fMRI analysis. Finally, EPI fMRI scans were co-registered to DTI scans, and the same registration parameters were applied to activation maps (fMRI results). Regions of activation were used as start and destination points for optimal path analysis, which resulted in three separate paths of optimal connectivity for each subject. The probability of the connections were then calculated for each path and each subject, and compared between groups. In our preliminary analysis, we included 10 control subjects and 10 chronic schizophrenics. Our results demonstrated a relationship between Stroop Effect fMRI activation in the medial parietal area and optimal path connectivity between parietal and cingulate activation sites in schizophrenics (rho=-0.56; p=0.047), which was not observed in controls. These findings suggest that decreased connectivity may result in schizophrenics relying more on posterior parts of the executive attentional network during performance of the Stroop task.

*Hippocampal and frontal memory circuitry abnormalities in schizophrenia: Relation of diffusion, morphometric and fMRI markers: We performed a combined DTI, fMRI, and morphometric study on 13 patients with schizophrenia (SZ) and 14 HC. We identified areas of increased trace diffusivity (TD) in the hippocampal and insular regions as well as areas of reduced fractional anisotropy (FA) in left frontal white matter in SZ relative to HC (p<.01). Voxel based morphometry analyses in a subset of these subjects showed corresponding reductions in gray matter density in hippocampal and insular regions in patients relative to controls (p<.01). Analysis of fMRI results from the novel vs. repeated word contrast from the event-related auditory verbal episodic memory encoding/retrieval task in a subset of the subjects indicated reduced activation in frontal and temporal regions, as well as increased activation in posterior cingulate, retrosplenial, and thalamic regions in SZ relative to HC (p<.05). Further analysis showed that left frontal white matter FA was associated with activation in the left and right hippocampi as well as other frontal and temporal regions, but inversely related to activation in the retrosplenial/posterior cingulate region (p<.05). These initial findings indicate a pattern of relationships between of structural and functional brain abnormalities in schizophrenia and demonstrate the feasibility of integrated quantitative analyses across modalities.

===== Methods =====

During this year, we continued methodological development along two
directions:

*Improving fMRI detectors by incorporating Markov priors on the activation state. We integrated the improved detector into Slicer and performed substantial validation of the methods using fBIRN data provided by the UC Irvine group. Journal paper on the method has been submitted to IEEE TMI.

*Improving registration of EPI images to anatomical scans through modeling of the EPI distortions. We demonstrated an initial model that uses segmentation of the structural scan to predict the distortions in the EPI images. The preliminary results are quite encouraging.

==== Key Investigators ====

* MIT: Polina Golland, Sandy Wells, Wanmei Ou, Claire Poynton
* BWH: Martha Shenton, Marek Kubicki
* Dartmouth: Andy Saykin
* UCI: Jessica Turner, Stephen Potkin
* Toronto: Jim Kennedy

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#fMRI_Analysis NA-MIC Projects on fMRI Analysis].

=== 2.4 [[NA-MIC-Kit|NA-MIC Kit]] Theme ===

==== Progress ====
The continuing vision of the NA-MIC Kit is to provide an open source set of software tools and methodologies that will serve as the foundation for medical image computing projects for both academic and commercial use. Key elements of this vision are:

# ''Unrestrictive License.'' Users of the Kit are free to distribute their derived works under any license suitable to their needs.
# ''Cross Platform.'' This software set can be adapted to the best available price-performance computer systems for any particular use.
# ''Extensible Application Framework.'' New techniques and algorithms can be quickly integrated into a working system. Sophisticated user interfaces can be generated easily through automated processes. Sophisticated toolsets such as ITK, VTK, and KWWidgets are available to create and deploy applications quickly.
# ''Quality Software Process.'' Developers and users can rely on accurate and well documented behavior from all the parts of the Kit.
# ''Sustainable Community.'' Users are actively involved in the design process of the Kit. Documentation, training materials, and hands-on sessions are available and well publicized to the community.

====='''Slicer3'''=====

A major focus of the third year was the implementation of the Slicer3 in the NAMIC Kit. This effort addressed Item #3 above ''Extensible Application Framework.'' The previous two years of the NAMIC project, which entailed gathering requirements from Cores 1 and 3, and developing the computational foundation, toolsets, and software process, came together in the Slicer3 application platform.

Core 2 worked hard to insure that the Slicer 3 application serves, and will continue to serve, as a productive technology deployment platform. The application framework was designed carefully to provide ease-of-use, both in terms of interaction and software integration. Advanced capabilities, including the ability to launch large-scale grid computing, was designed into the application. Some of the key features of the Slicer3 application completed in the third year include the following.
* Advanced application framework including a tuned GUI for ease of use, undo/redo capabilities, 2D/3D view windows, and support for advanced interaction techniques such as 3D widgets. The application provides viewers for displaying slices, volumes, and models including the ability to edit properties. A built in transformation pipeline enables users to confidently import, edit and display data in a consistent coordinate system.
* The application is data-driven based on the next generation MRML scene description file format. Backward compatibility to Slicer 2.x MRML files is preserved.
* A module plug-in architecture and execution model that enables researchers to package and integrate their software into the Slicer3 framework. Plug-in modules can be implemented in a variety of programming languages, and are described using a simple XML description. These modules, when located and loaded into Slicer3, have the capability to automatically generate their user interface, which is seamlessly integrated into the Slicer3 GUI.
* Support for editing and marking data including support for fudicials, paint and draw editors.
* The creation of several simple plug-in modules, including the conversion of previous Slicer 2.x modules to the new Slicer3 architecture.

====='''EM Segment'''=====

As an application framework, Slicer3 provides tools for loading, viewing, measuring, editing, and saving data. To support advanced medical image analysis, plug-in modules are required in conjunction with the Slicer3 core. To demonstrate the capabilities of the framework we implemented the [http://www.na-mic.org/Wiki/index.php/Slicer3:EM EM Segment] module, a sophisticated and proven method for automatically segmenting complex anatomical structures.

To use this module, users specify parameters defining the image protocol and the anatomical structures of interests. This process results in a template that the module uses to automatically segment large data sets. The template is composed of atlas data and a non-trivial collection of parameters for the EM Segment algorithm ([[Media:Pohl-miccai-short-2005.pdf|Pohl et al.]]). Once the parameters are specified, the target images are segmented using the algorithm; if the results are satisfactory, the template is saved and can be used later to segment new images (via the GUI or batch processing). If the results are unsatisfactory, the parameters can be modified and the segmentation re-run.

Besides successfully demonstrating the use of complex algorithms in the Slicer3 framework, this effort also led us to develop tools, including modifications to the underlying KWWidgets GUI toolkit, to support module workflow. With these tools, it is possible to simplify complex modules by dividing the complicated template specification task into a number of smaller, intuitive steps. These steps are enforced by the GUI and reduce the potential for user error, while improving the overall user interface.

====='''Quality Software Process'''=====

Building on [http://lwn.net/Articles/188693/ last year's success with the KDE community], the NAMIC community continued to extend its world-class, open source software process tools CMake, DART, CTest and DART. These tools, which form the core of a quality-oriented, test-driven development (TDD) software process. In particular, the CPack system is now able to automatically package and distibute code, libraries, executables and installers across all of NAMIC's supported platforms (i.e., Linux, Windows, Mac). This enables the NAMIC developer community to rapidly deploy software tools to the user community.

==== Key Investigators ====

* GE: Bill Lorensen, Jim Miller, Xiaodong Tao, Dan Blezek
* Isomics: Steve Pieper, Alex Yarmarkovich
* Kitware: Will Schroeder, Luis Ibanez, Brad Davis, Andy Cedilnik, Sebastien Barre, Bill Hoffman
* UCLA: Mike Pan, Jagadeeswaran Rajendiran
* UCSD: Neil Jones, Jeffrey Grethe, Mark Ellisman
* BWH: Nicole Aucoin, Katie Hayes, Wendy Plesniak, Mike Halle, Gordon Kindlmann, Raul San Jose Estepar, Haiying Liu, Ron Kikinis
* MIT: Lauren O'Donnell, Kilian Pohl

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#NA-MIC_Kit NA-MIC Kit Projects].

== 3. Highlights ==
The third year of the NAMIC project saw continued development and dissemination of medical image analysis software. With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NAMIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NAMIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

=== 3.1 Advanced Algorithms ===
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NAMIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NAMIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NAMIC Sandbox in preparation for formal acceptance into the NAMIC Kit.

=== 3.2 Technology Deployment Platform: Slicer3 ===
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NAMIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NAMIC Kit, and are now available to all other modules built on the Slicer3 framework.

=== 3.3 Outreach and Technology Transfer ===
Cores 4-5-6 continue to support, train and dissemniate to the NAMIC community, and the broader biomedical computing community.
* NAMIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NAMIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair.

== 4. Impact and Value to Biocomputing ==

In NA-MIC's third year, it is evident that NA-MIC is developing a
culture, environment, and resources to foster and incite collaborative
research in medical image analysis that draws together mathematicians,
computer scientists, software engineers, and clinical
researchers. These artefacts of NA-MIC impact how NA-MIC
operates, make NA-MIC a fulcrum for NIH funded research,
and draws new collaborators from across the country
and around the world to NA-MIC.

=== 4.1 Impact within the Center ===

Within the center, the NA-MIC organization, NA-MIC processes, and the
NA-MIC calendar has permeated the research. The organization is
nimble, forming ad hoc distributed teams within and between cores to
address specific biocomputing tasks. Information is shared freely on
the NA-MIC Wiki, on the weekly Engineering telephone conferences, and
in the NA-MIC Subversion source code repository. The software
engineering tools of CMake, Dart 2 and CTest, CPack, and KWWidgets
facilitate a cross platform software environment for medical image
analysis that be easily built, tested, and distributed to
end-users. Core 2 has provided a platform, Slicer 3, that allows Core
1 to easily integrate new technology and deliver this technology in an
end user application to Core 3. Core 1 has developed a host of
techniques to apply to structural and diffusion analysis which are
under evaluation by Core 3. Major NA-MIC events, such as the annual
All Hands Meeting, the Summer Project Week, the Spring Algorithms
meeting, and Engineering Teleconferences are avidly attended by NA-MIC
researchers as opportunities to foster collaborations.

=== 4.2 Impact within NIH Funded Research ===

Within NIH funded research, NA-MIC continues to forge relationships
with other large NIH funded projects such as BIRN, caBIG, NAC, and
IGT. Here, we are sharing the NA-MIC culture, engineering practices,
and tools. BIRN hosts data for the NA-MIC researchers and NA-MIC hosts
BIRN wikis. caBIG lists the 3D Slicer among the applications available
on the National Cancer Imaging Archive. NAC and IGT use the NA-MIC
infrastructure and are involved in the development of the 3D
Slicer. BIRN recently held an event modeled after the NA-MIC Project
Week. NA-MIC has become a resource on open source licensing to the medical image analysis community.

NA-MIC is also attracting NIH funded collaborations. Two grants have
been funded under PAR-05-063 to collaborate with NA-MIC: ''Automated FE Mesh Development'' and ''Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI''. Five additional
applications to collaborate with NA-MIC via the NCBC collaborative
grant mechanism are under consideration. Additional grant applications
submitted under other calls are planning to use and extend the NA-MIC tools.

=== 4.3 National and International Impact ===

NA-MIC events and tools garner national and international interest.
There were nearly 100 participants at the NA-MIC All Hands Meeting in
January 2007, with many of these participants from outside of NA-MIC.
Several researchers from outside the NA-MIC community have attended
the Summer Project Weeks and the Winter Project Half-Weeks to gain
access to the NA-MIC tools and people. These external researchers are
contributing ideas and technology back into NA-MIC.

Components of the NA-MIC kit are used globally. The software
engineering tools of CMake, Dart 2 and CTest are used by many open
source projects and commercial applications. For example, the K Desktop Environment (KDE) for
Linux and Unix workstations uses CMake and Dart. KDE is one of the
largest open source projects in the world. Many open source projects and
commercial products are benefiting from the NA-MIC related
contributions to ITK and VTK. Finally, Slicer 3 is being used as an
image analysis platform in several fields outside of medical image
analysis, in particular, biological image analysis, astronomy, and industrial inspection.

NA-MIC co-sponsored the ''Workshop on Open Science'' at the Medical
Image Computing and Computer-Assisted Intervention (MICCAI) 2006
conference. The proceedings of the workshop are published on the
electronic Insight Journal, another NIH-funded activity.

Over 50 NA-MIC related publications have been produced since the inception of the center.

== 5.NA-MIC Timeline ==

This section provides a table of NAMIC timelines from the original proposal that graphically depicts completed tasks/goals in years 1, 2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines have also been described.

[[2007_Scientific_Report_Timeline|2007 Scientific Report Timeline]]

== Appendix A Publications ==

http://www.na-mic.org/Wiki/index.php/Publications

== Appendix B EAB Report ==

===EAB Report===
The NA-MIC External Advisory Board (EAB), chaired by Professor Chris Johnson of the University of Utah, met at the annual All-Hands Meeting. After individual presentations by NA-MIC investigators and open as well as closed-door EAB discussion, the Board provided its independent expert assessment of the Center.

[https://share.spl.harvard.edu/grants/u54/2007/APR%202007/NAMIC-EAB-Report07-ver5.doc This is a password protected link to the EAB report.]

===Response to EAB Report===

The NA-MIC leadership discussed the excellent suggestions made by the EAB, and in the following section we respond to each of them.

''EAB: The Center should actively pursue methods to distribute their software and other resources to a broader community. As we noted in last year’s EAB Report, the Center is doing a good job at distributing their software toolkit, however, largely the distribution has been focused internally to NA-MIC collaborators. The EAB recommends that the Center work to more broadly distribute its software to the larger community of neuroimaging researchers. In addition, because the Center’s software tools will also be useful to biomedical researchers outside of neuroimaging, the EAB recommends the Center consider additional methods to “get the word out” about their resources.''

NA-MIC: The focus in the last two years of the Center has been to broaden the outreach effort to the neuroimaging community and to other biomedical researchers outside of this community. Many algorithms have been developed in ITK/VTK, and then ported to Slicer to try to make them as accessible as possible. We believe that the ITK/VTK platforms have been quite widely disseminated. However, as the EAB has noted, the current training and dissemination funds are limited, and the Center leadership will continue to look for creative solutions to get the word out within these resource constraints.

''EAB: The Center is well suited to assess and compare algorithms. An important question in image analysis involves understanding which image-processing algorithm is best suited for a particular task. Given the large number of different image analysis algorithms being created by NA-MIC, the Center is in a good position to evaluate the effectiveness of different algorithms and to make appropriate recommendations.''

NA-MIC: This topic has been discussed extensively within the Center, and we have addressed this issue in a manner that is aligned with our core expertise in open source software. More specifically, we will continue to build upon our strength and provide infrastructure and tools needed for open science, as well as to train researchers in the use of these tools. It is our hope that by providing easy access to these tools, we will be facilitating the process of comparison between algorithms for various biomedical tasks. This being said, many times there is no optimal algorithm for every imaging purpose. A certain segmentation routine may be excellent for one purpose, but not for another. This is a well recognized problem in image processing and computer vision, and not a fundamental limitation of medical imaging. Comparisons, however, among different algorithms to determine which algorithms are best for which questions being addressed is nonetheless important and we have made some inroads here. Sylvain Bouix, for example, has a paper in press in Neuroimage on evaluating brain tissue classifiers without ground truth, which compares several different segmentation algorithms. Also, in the shape analysis field, every NA-MIC related publication performs the necessary comparisons. Furthermore, Martin Styner et al authored a paper on shape analysis comparisons at ISBI 2006, as well as he is co-organizing a caudate and liver segmentation comparison workshop at MICCAI 2007 with currently over 35 registered participating methods. Our philosophy is to make a large menu of algorithmic methodologies available in as easy to use form as possible, so as to encourage the kind of comparisons recently done by Drs. Bouix and Styner.

''EAB: The Center should consider additional techniques to validate and verify techniques and software. The EAB was pleased to see the progress the Center made in quantifying and validating their algorithms and implementations within the NA-MIC Kit. However, because this is such an important issue, the EAB recommends the Center continue to make progress in this area.
''

NA-MIC: The Center intends to continue its effort in validation and verification of its software implementations. Center researchers have also come up with original techniques for validation, e.g., the Laplacian method for comparing the accuracy of segmentations. These validation techniques have been used to drive new original algorithmic as well. In addition, work within the Center has continued to compare manual measures of brain structure, i.e., the caudate as a gold standard for automated measures of caudate in the same subjects. This kind of validation will also continue.

''EAB: The Center should generate a set of success criteria. The EAB recommends that the Center create a set of benchmarks of success that it will use in future years to measure the Center’s success and as ways to evaluate the Center’s continuing progress and at the time of the Center’s first renewal.''

NA-MIC: We note that we have established benchmarks for success that are similar to criteria used by study sections for reviewing the success of other grant mechanisms, although also includes specific criteria for NA-MIC, and includes:

• Papers in peer reviewed publications

• Technology transfers from Core 1 to Core 3 researchers

• Software released as part of the NA-MIC Kit

• Training outside researchers in the use of the NA-MIC Kit

= Logistics =

== Schedule and process for preparation of this report ==

* March 30 - Assign section/theme leads (Ron). Last year: introduction (marty), structural analysis (allen, martin), dti (guido, marty/marek), fmri (polina, andy), namic kit(bill), timeline (ross), highlights (will), impact (bill).
*April 7,10 - tcons with Marty, Ross, Will, Tina to finalize the layout, process, and timeline of report.
* April 13 - update projects list using last year's [[NA-MIC_Collaborations]] and [[2007_Project_Half_Week#Projects|projects pursued at the half week in SLC]]. Remind investigators to update individual pages. (Tina)
* April 23- complete project description pages in updated list: [[NA-MIC_Collaborations]] (all project owners).
* April 30 - complete section summaries, introduction, highlights, impact, timeline (owners of these topics)
* May 8 - submit wiki report to NA-MIC editor, Ann (Tina)
* May 17 - submit Edited report to Rachana in pdf format (Ann)
* May 25 - submit final report, if changes are needed (Ann)
* May 29 - ship final package to NIH (Rachana)

== Guidelines from NIH Program Officer ==
===NIH Reviewer Comments on Yr2 APR (July 2006)===
The following additional guidelines were provided by the NIH progress review committee after they reviewed the 2006 report:

1. Comment on obstacles and challenges encountered during the year.

2. Comment on outcomes of DBPs.

3. Create a context for the report in the Introduction.

4. Comment on how all the themes as a whole are moving forward.

5. Full citations should be given within the report as footnotes or in a bibliography, not just as hyperlinks.

6. Explain time-lines with text including reasons for delays. Link timelines to papers and citations.

7. Define terms used in the timeline to describe the amount of progress being made, especially with respect to software development.

8. Put priority weights on completed tasks within timeline. Note if a task is a stepping stone or a final goal.

9. Make sure the projects presented in the annual report are also listed in the project pages of the NAMIC Wiki and vice versa.

10. In the budget pages, please elaborate on the number and locations for travel.

===Feb 2006===
The following guidelines were provided by Grace Peng, NA-MIC program officer, in Feb 2006.

The key is to synthesize all the individual elements into bigger picture stories that really speak of each area’s impact to the community.

The specialized scientific report should have the following format:

# Introductory page describing the new grouping of NAMIC project themes.
# A description of progress in each NAMIC project theme (not to exceed 2 pages each), tying together relevant activities from participating subcomponents and referencing cores in parentheses.
# A table of NAMIC timelines (from original proposal), graphically depicting completed tasks/goals in years 1,2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines should be described.
# A description of 3 highlights selected from all NAMIC projects to showcase NAMIC.
# A discussion of NAMIC’s impact and value to the biocomputing community this year.

2007 Annual Scientific Report

2007-05-20T17:01:46Z

Shenton: /* Response to EAB Report */

Back to [[2007_Progress_Report]]

For reference:

*[[2006_Progress_Report]]

*[[Media:2005_NAMIC_Specialized_ProgReport.pdf|2005 Annual Scientific Report]]

== 1. Introduction ==

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with espect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs continue to include neuropsychiatric disorders such as Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Schizophrenia (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a adopting a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

(Please note that this report is available on the NA-MIC wiki at the following url: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report)

== 2. Four Main Themes ==

This year's activities focus on four main themes: Diffusion Image Analysis, Structural Analysis, Functional MRI Analysis, and the NA-MIC Kit. Each of the following sections begins with an overview of the theme, provides a progress update and list of key investigators, and concludes with a set of links to additional information for individual projects in that theme.

These thematic activities involve scientists from each of the 7 NA-MIC cores (Appendix).

* [http://www.na-mic.org/Wiki/index.php/Algorithm:Main Core 1 Algorithms]-Ross Whitaker PI
* [http://www.na-mic.org/Wiki/index.php/Engineering:Main Core 2 Engineering]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/DBP:Main Core 3 Driving Biological Problems] DBP1-Martha Shenton PI / DBP2-Andy Saykin PI / DBP3-Steven Potkin PI
* [http://www.na-mic.org/Wiki/index.php/Service:Main Core 4 Service]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/Training:Main Core 5 Training]-Randy Gollub PI
* [http://www.na-mic.org/Wiki/index.php/Dissemination:Main Core 6 Dissemination]-Tina Kapur Co-PI; Steve Pieper Co-PI
* [http://www.na-mic.org/Wiki/index.php/Leadership:Main Core 7 Leadership]-Ron Kikinis

=== 2.1 Diffusion Image Analysis Theme ===

==== Progress ====
Over the past year, we have continued to develop a number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical diffusion tensor analysis. These tools have been already integrated into diffusion dedicated software (e.g., Fiber Viewer-UNC). In addition, we started developing tools for group data analysis, such as white matter atlases, and tools for data integration, that would combine and interface among different imaging modalities (such as structural, fMRI and DTI), to better estimate anatomical and functional connectivity. This software has continued to be used in multiple clinical projects involving several psychiatric populations. Below we provide detailed progress in the area of diffusion image analysis.

==== Fiber Tract Extraction and Analysis ====
* Since last year, all of the algorithms for fiber tractography and anisotropy estimation have been implemented in both the “Fiber Viewer” and “Slicer” packages, and now the resulting methods are being applied to clinical studies. Fiber Tract integrity has been investigated and results have been presented at several international conferences for four key fiber tracts in schizophrenia (corpus callosum, fornix, inferior occipito-frontal fasciculus, and uncinate fasciculus). These tracts have been extracted, and fractional anisotropy has been compared between groups, demonstrating fronto-temporal connectivity abnormalities in schizophrenia.

* In addition to clinical studies, teams have been working on other methods to define and estimate brain connectivity. The most important developments in this regard included volumentric connectivity and stochastic tractography measures. For volumentric connectivity, a PDE-based approach to white matter connectivity from DTI has been developed, that is founded on the principal of minimal paths through the tensor volume. This method computes a volumetric representation of a white matter tract given two endpoint regions. In addition, statistical methods for quantifying the full tensor data along these pathways have been also developed. Directional PDE-based flows have been proposed and implemented for a similar purpose. Further, an approach called "stochastic tractography," has been developed to calculate probability of two regions being connected by the fiber tract. This should be especialy useful when tract has to go through the regions characterized by low diffusion anisotropy.

* Some initial attempts have been made to population based analysis of DT-MRI. One method is based on the unbiased non-rigid registration of a population to a common coordinate system. The registration jointly produces an average DTI atlas, which is unbiased with respect to the choice of a template image, along with a diffeomorphic correspondence for each image. The anatomically significant correspondence provides a basis for comparison of tensor features and fiber tract geometry in clinical studies.

==== Fractional Anisotropy Analysis ====
* We have used tools developed last year, and applied them to our population of chronic schizophrenia subjects in order to investigate two fiber tracts: the cingulum bundle and the corpus callosum. In the case of cingulum bundle, manually drawn regions of interest and Finsler geometry were used to extract entire cingulum bundle fiber tract, and FA was estimated along the tract, and compared between groups. Corpus Callosum cross sectional area and its probabilistic subdivisions were determined automatically from the structural MRI scans using a model based deformable contour segmentation. The structural scan was then co-registered with the DTI scan and the anatomical corpus callosum subdivisions were propagated to the associated FA map, and compared between groups, demonstrating deficient interhemispheric communication in schizophrenia.

==== Integration of fMRI and DTI, Path-of-Interest Analysis ====
* Progress has been also made in the development of a tool that would successfully combine and integrate functional and anatomical information. The Optimal Path Analysis method has been applied to the Harvard- BWH fMRI dataset, and anatomical connections between regions active during the fMRI experiment have been extracted for each subject. The connectivity has calculated and compared between groups. This tool is now being ported to Slicer3.

==== Key Investigators ====

* BWH: Martha Shenton, Marek Kubicki, Marc Niethammer, Sylvain Bouix, Jennifer Fitzsimmons, Katarina Quintis, Doug Markant, Kate Smith, Georgia Bushell, Mark Dreusicke, Carl-Fredrik Westin, Raul San Jose, Gordon Kindlmann
* MGH: Bruce Fischl, Denis Jen, David Kennedy
* MIT: Lauren O'Donnell, Polina Golland, Tri Ngo
* UCI: James Fallon, Martina Panzenboeck
* UNC: Guido Gerig, Isabelle Corouge, Casey Goodlett, Martin Styner
* Utah: Tom Fletcher, Ross Whitaker, Saurav Basu, Davis McKay
* GA Tech: Eric Pichon, John Melonakos, Xavier LaFaucheur, Vandana Mohan, Allen Tannenbaum
* Dartmouth: John West, Andrew Saykin, Laura Flashman, Paul Wang, Heather Pixley, Robert Roth
* Isomics: Steve Pieper
* Kitware: Luis Ibanez

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Diffusion_Image_Analysis NA-MIC Projects on Diffusion Image Analysis].

=== 2.2 Structural Analysis Theme ===

==== Progress ====

Within the NAMIC's structural analysis theme, the main topics of interest are structural segmentation, registration and shape analysis. These topics are of course intertwined, e.g., segmentation or registration can directly deliver structural correspondence used in shape analysis, or in turn shape modeling is necessary for good structural segmentations. Here is a selection of progress highlights in the structural analysis theme

====='''Segmentation'''=====

* Wavelet based structural segmentation: We have developed a spherical wavelet based framework for the segmentation of selected brain structures, such as the hippocampus or the caudate nucleus. An automated segmentation of such structures must be highly accurate and include high frequency variations in the surface. Since shape representation is a key component of the segmentation, it must be rich enough to express shape variations at various frequency levels, from low harmonics to sharp edges. Medical object segmentation with deformable models and statistical shape modeling may be combined to obtain a more robust and accurate segmentation. To address this issue, a decomposable spherical wavelet based shape representation targeted to the population seems natural, where the shape parameters are separated into groups that describe independent global and/or local biological variations in the population, and a prior induced over each group explicitly encodes these variations. This work presents three novel contributions for shape representation, multiscale prior probability estimation and segmentation.

* Directional based segmentation: We have proposed an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. In the classical case, the Euclidean metric is locally multiplied by a scalar conformal factor (based on image information) such that the weighted length of curves lying on points of interest (typically edges) is small. We propose to add directionality to the factor, and show that one gets a well-defined minimization problem in the case that the factor defines a Finsler metric. Optimal curves may be obtained using the calculus of variations or dynamic programming. This methodology also makes connections to the important technique of graph-cuts.

* Statistical PDE methods for segmentation in shape space: This past year, we have proposed another method to perform shape-driven segmentation. In our approach, shapes are represented using binary maps, and linear PCA is utilized to provide shape priors for segmentation. Intensity based probability distributions are then employed to convert the given volume into a binary map representation, and a new energy functional is formulated whose minimization is performed using a parametric model for surface evolution in the shape space. Our algorithm is then applied to the segmentation of brain caudate nucleus and hippocampus from MRI data. Our validation shows that the proposed algorithm outperforms the log-likelihood based energy, converges in less than 5 iterations and is very obust to initialization. The overall algorithm is illustrates the potential for segmentation in shape space.

* Rule-based segmentation methods: We have continued this past year to develop segmentation methods based on heuristic rules provided to us by our Core 3 partners for segmenting various brain regions of interest in schizophrenia, e.g. the DLPFC and the striatum. The idea is to try to semi-automate these rules in order to forge an interactive tool for segmentation which can greatly shorten the time necessary for manual segmentation. Typically, these methods are used in conjunction with some Bayesian classifier which further aids to automating and in speeding up the given segmentation methodology.

* Tissue and structural segmentation via EM Segmenter: Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases segmentation is mostly performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We have developed an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior knowledge. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. The mapping between atlas and image space, structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the Maximum A posteriori Probability (MAP) estimation problem. These results are then fed into an Active Mean Field approach, which views the results as priors to a Mean Field approximation with a curve length prior. This segmentation framework has been ported into the NAMIC-toolkit and a first version of the tool is distributed with Slicer 3.

====='''Shape Analysis'''=====

* Wavelet based shape analysis: A shape representation that encodes variations at multiple scales can be useful as a rich feature set for shape analysis and classification. Combining tools from the existing shape analysis toolset, we extended it to include spherical wavelet coefficients (SWC) as features and compared the results obtained to shape analysis using a SPHARM-PDM representation. The rich SWC feature set allows the differentiation of shape differences at various scales as well as highly correlates with the existing SPHARM-PDM analysis but with increased statistical sensitivity. Wavelet coefficient shrinkage and dimension reduction are well-understood and have been widely researched for traditional types of wavelet decompositions but not much explored for the second generation wavelets. During the past year, we have developed a Bayesian model on our specific wavelet structure based on a population of surfaces. For each shape, the deviation from the mean is computed and is modeled as the sum of an unknown signal and a noise. This deviation is encoded by the wavelet transform and our goal is to estimate the wavelet coefficients belonging to the noiseless signal.

* Surface flattening for shape analysis: Flattened representations of undulated surfaces constitute an active area of research in the field of medical imaging and visualization, due to their extensive use for registration and shape analysis of various structures of interest. We have presented a method for flattening anatomical surfaces in an area preserving manner, while minimizing the geometrical distortion. This method is based on the theory of optimal mass transport and conformal mapping of surfaces. The key idea here is the use of a multiresolution scheme for the solution of optimal mass transport gradient descent equations which allows a fast and stable solution for optimal transport. The method has been implemented on a GPU, allowing us to flatten a 128 by 128 by 128 volume in about 5 seconds on a standard workstation.

* Curvature based population correspondence: We have extended the Minimum Description Length population based correspondence framework to include curvature based measurements, such as the Koenderink Shape Index S and Curvedness C in combination with the standard location information. Current methodology in population based correspondence is based mainly on minimizing distribution properties of surface point locations and are thus not invariant to alignment. We have favorably compared our combined "Curvature + Location" MDL to the standard MDL, as well as to the SPHARM approach, in complex structures, such as the striatal brain structure (composed of caudate, nucleus accumbens and putamen).

* Shape analysis toolset: A considerable amount of work was spent on the development aspect of the shape analysis tools. The distributed set of tools is continuously enhanced and the population based correspondence framework has been released as open source. All the tools including the visualization tool, KWMeshVisu, can be called directly from Slicer 3. The visualization tool allows the overlay of scalar, vector and ellipsoid data onto surfaces via versatile colormaps. The attributed surfaces are then visible within Slicer 3. This lean visualization tool fills a niche and is also used in our cortical thickness analysis tool. Also, while it is entirely possible to run all shape analysis steps by calling the individual modules, this is highly inefficient in a larger study. As a result we are developing a separate shape pipeline Slicer module that uses Batchmake to run the shape analysis pipeline as a distributed, background process. The whole shape analysis pipeline will thus become entirely encapsulated and accessible to the trained clinical collaborator.

* Particle based correspondence: We have developed a method for a multi-object correspondence optimization, and have applied it successfully to a proof-of-concept application for the analysis of brain structure complexes from a longitudinal study of pediatric autism. This new method for constructing compact statistical point-based models of ensembles of similar shapes does not rely on any specific surface parameterization, requires little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including non-manifold surfaces and objects of arbitrary topology. The method uses a dynamic particle system to optimize correspondence point positions across all structures by simultaneously maximizing both the geometric accuracy and the statistical simplicity of the model.

==== Key Investigators ====

* MIT: Kilian Pohl, Sandy Wells, Eric Grimson
* UNC: Martin Styner, Ipek Oguz, Guido Gerig, Xavier Barbero
* Utah: Ross Whitaker, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer
* GaTech: Allen Tannenbaum, John Melonakos, Tauseef ur Rehman, Shawn Lankton, Ramsey Al-Hakim, Eric Pichon, Delphine Nain, Oleg Michailovich, Yogesh Rathi, James Malcolm
* Isomics: Steve Pieper
* GE: Bill Lorensen, Jim Miller
* Kitware: Luis Ibanez, Karthik Krishnan,
* UCLA: Michael J. Pan, Jagadeeswaran Rajendiran
* BWH: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt
* Dartmouth: Andrew Saykin
* UCI: James Fallon

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Structural_Image_Analysis NA-MIC Projects on Structural Image Analysis].

=== 2.3 Functional MRI Analysis Theme ===

====Progress====

During this year, the focus of the algorithms and the engineering
cores has been on the structural and DTI analysis. While we continued
to expand the methods and the infrastructure in NAMIC-kit to support
fMRI analysis, as well as using the analysis tools to perform clinical
studies, the emphasis of the work this year has been on integrating
the fMRI analysis with other modalities and supporting other
modalities.

===== Clinical Studies =====

We would like to highlight several clinical studies within NAMIC that
focused on fMRI data and its relationship with other imaging
modalities:

* Imaging Phenotypes in Schizophrenics and Controls: Functional connectivity of the DLPFC by genotype was investigated emplying partial least squares (PLS) correlation analysis. PLS is a multivariate analytical technique used to summarize large neuroimaging data sets in such a way as to correlate patterns of activation with a variable(s) of interest (i.e., DLPFC activity). In the most recent analysis, the DRD1 genotype was used as a grouping variable. This analysis has been submitted for publication (Tura, Turner, Fallon, Kennedy, and Potkin. Genetic Impact on Functional Connectivity in Schizophrenics During a Working Memory Task). Working memory performance did not differ significantly between the two cohorts. However, imaging-genetic analysis showed a significant difference (P< 0.05) between the circuitry engaged by each group. Significance and reliability of the resulting imaging-behavioral patterns within each genotype were assessed by 200 bootstrap and 500 permutation tests, respectively. In one group, the circuitry included the temporal pole, the insula, the dorsolateral prefrontal cortex, and the Brodmann Areas (BA) 1,2,3,4,6,11 and 21, while the other group showed a network comprising the tectum, precuneus retroplenial, vermis, substantia nigra, BA 22,39,8, and 9. The DRD1 polymorphic site may characterize circuitry differences in schizophrenic patients.

*Path-Of-Interest Analysis (joint DTI/fMRI modeling): We collected preliminary data using an application of the “optimal path analysis”. In this analysis, we extracted group fMRI activation due to the Stroop effect (an attentional experiment where incongruent color, in which the word is written, competes with name of the color itself, activating areas responding to conflict monitoring and selection) separately for controls and schizophrenics. This resulted in three clusters of activation, one in the right Dorsolateral Prefrontal Cortex, a second in the Anterior Cingulate Gyrus, and a third in the Medial Parietal Lobe. In the next step, we placed activation clusters in each individual space, by reversing normalization parameters used during fMRI analysis. Finally, EPI fMRI scans were co-registered to DTI scans, and the same registration parameters were applied to activation maps (fMRI results). Regions of activation were used as start and destination points for optimal path analysis, which resulted in three separate paths of optimal connectivity for each subject. The probability of the connections were then calculated for each path and each subject, and compared between groups. In our preliminary analysis, we included 10 control subjects and 10 chronic schizophrenics. Our results demonstrated a relationship between Stroop Effect fMRI activation in the medial parietal area and optimal path connectivity between parietal and cingulate activation sites in schizophrenics (rho=-0.56; p=0.047), which was not observed in controls. These findings suggest that decreased connectivity may result in schizophrenics relying more on posterior parts of the executive attentional network during performance of the Stroop task.

*Hippocampal and frontal memory circuitry abnormalities in schizophrenia: Relation of diffusion, morphometric and fMRI markers: We performed a combined DTI, fMRI, and morphometric study on 13 patients with schizophrenia (SZ) and 14 HC. We identified areas of increased trace diffusivity (TD) in the hippocampal and insular regions as well as areas of reduced fractional anisotropy (FA) in left frontal white matter in SZ relative to HC (p<.01). Voxel based morphometry analyses in a subset of these subjects showed corresponding reductions in gray matter density in hippocampal and insular regions in patients relative to controls (p<.01). Analysis of fMRI results from the novel vs. repeated word contrast from the event-related auditory verbal episodic memory encoding/retrieval task in a subset of the subjects indicated reduced activation in frontal and temporal regions, as well as increased activation in posterior cingulate, retrosplenial, and thalamic regions in SZ relative to HC (p<.05). Further analysis showed that left frontal white matter FA was associated with activation in the left and right hippocampi as well as other frontal and temporal regions, but inversely related to activation in the retrosplenial/posterior cingulate region (p<.05). These initial findings indicate a pattern of relationships between of structural and functional brain abnormalities in schizophrenia and demonstrate the feasibility of integrated quantitative analyses across modalities.

===== Methods =====

During this year, we continued methodological development along two
directions:

*Improving fMRI detectors by incorporating Markov priors on the activation state. We integrated the improved detector into Slicer and performed substantial validation of the methods using fBIRN data provided by the UC Irvine group. Journal paper on the method has been submitted to IEEE TMI.

*Improving registration of EPI images to anatomical scans through modeling of the EPI distortions. We demonstrated an initial model that uses segmentation of the structural scan to predict the distortions in the EPI images. The preliminary results are quite encouraging.

==== Key Investigators ====

* MIT: Polina Golland, Sandy Wells, Wanmei Ou, Claire Poynton
* BWH: Martha Shenton, Marek Kubicki
* Dartmouth: Andy Saykin
* UCI: Jessica Turner, Stephen Potkin
* Toronto: Jim Kennedy

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#fMRI_Analysis NA-MIC Projects on fMRI Analysis].

=== 2.4 [[NA-MIC-Kit|NA-MIC Kit]] Theme ===

==== Progress ====
The continuing vision of the NA-MIC Kit is to provide an open source set of software tools and methodologies that will serve as the foundation for medical image computing projects for both academic and commercial use. Key elements of this vision are:

# ''Unrestrictive License.'' Users of the Kit are free to distribute their derived works under any license suitable to their needs.
# ''Cross Platform.'' This software set can be adapted to the best available price-performance computer systems for any particular use.
# ''Extensible Application Framework.'' New techniques and algorithms can be quickly integrated into a working system. Sophisticated user interfaces can be generated easily through automated processes. Sophisticated toolsets such as ITK, VTK, and KWWidgets are available to create and deploy applications quickly.
# ''Quality Software Process.'' Developers and users can rely on accurate and well documented behavior from all the parts of the Kit.
# ''Sustainable Community.'' Users are actively involved in the design process of the Kit. Documentation, training materials, and hands-on sessions are available and well publicized to the community.

====='''Slicer3'''=====

A major focus of the third year was the implementation of the Slicer3 in the NAMIC Kit. This effort addressed Item #3 above ''Extensible Application Framework.'' The previous two years of the NAMIC project, which entailed gathering requirements from Cores 1 and 3, and developing the computational foundation, toolsets, and software process, came together in the Slicer3 application platform.

Core 2 worked hard to insure that the Slicer 3 application serves, and will continue to serve, as a productive technology deployment platform. The application framework was designed carefully to provide ease-of-use, both in terms of interaction and software integration. Advanced capabilities, including the ability to launch large-scale grid computing, was designed into the application. Some of the key features of the Slicer3 application completed in the third year include the following.
* Advanced application framework including a tuned GUI for ease of use, undo/redo capabilities, 2D/3D view windows, and support for advanced interaction techniques such as 3D widgets. The application provides viewers for displaying slices, volumes, and models including the ability to edit properties. A built in transformation pipeline enables users to confidently import, edit and display data in a consistent coordinate system.
* The application is data-driven based on the next generation MRML scene description file format. Backward compatibility to Slicer 2.x MRML files is preserved.
* A module plug-in architecture and execution model that enables researchers to package and integrate their software into the Slicer3 framework. Plug-in modules can be implemented in a variety of programming languages, and are described using a simple XML description. These modules, when located and loaded into Slicer3, have the capability to automatically generate their user interface, which is seamlessly integrated into the Slicer3 GUI.
* Support for editing and marking data including support for fudicials, paint and draw editors.
* The creation of several simple plug-in modules, including the conversion of previous Slicer 2.x modules to the new Slicer3 architecture.

====='''EM Segment'''=====

As an application framework, Slicer3 provides tools for loading, viewing, measuring, editing, and saving data. To support advanced medical image analysis, plug-in modules are required in conjunction with the Slicer3 core. To demonstrate the capabilities of the framework we implemented the [http://www.na-mic.org/Wiki/index.php/Slicer3:EM EM Segment] module, a sophisticated and proven method for automatically segmenting complex anatomical structures.

To use this module, users specify parameters defining the image protocol and the anatomical structures of interests. This process results in a template that the module uses to automatically segment large data sets. The template is composed of atlas data and a non-trivial collection of parameters for the EM Segment algorithm ([[Media:Pohl-miccai-short-2005.pdf|Pohl et al.]]). Once the parameters are specified, the target images are segmented using the algorithm; if the results are satisfactory, the template is saved and can be used later to segment new images (via the GUI or batch processing). If the results are unsatisfactory, the parameters can be modified and the segmentation re-run.

Besides successfully demonstrating the use of complex algorithms in the Slicer3 framework, this effort also led us to develop tools, including modifications to the underlying KWWidgets GUI toolkit, to support module workflow. With these tools, it is possible to simplify complex modules by dividing the complicated template specification task into a number of smaller, intuitive steps. These steps are enforced by the GUI and reduce the potential for user error, while improving the overall user interface.

====='''Quality Software Process'''=====

Building on [http://lwn.net/Articles/188693/ last year's success with the KDE community], the NAMIC community continued to extend its world-class, open source software process tools CMake, DART, CTest and DART. These tools, which form the core of a quality-oriented, test-driven development (TDD) software process. In particular, the CPack system is now able to automatically package and distibute code, libraries, executables and installers across all of NAMIC's supported platforms (i.e., Linux, Windows, Mac). This enables the NAMIC developer community to rapidly deploy software tools to the user community.

==== Key Investigators ====

* GE: Bill Lorensen, Jim Miller, Xiaodong Tao, Dan Blezek
* Isomics: Steve Pieper, Alex Yarmarkovich
* Kitware: Will Schroeder, Luis Ibanez, Brad Davis, Andy Cedilnik, Sebastien Barre, Bill Hoffman
* UCLA: Mike Pan, Jagadeeswaran Rajendiran
* UCSD: Neil Jones, Jeffrey Grethe, Mark Ellisman
* BWH: Nicole Aucoin, Katie Hayes, Wendy Plesniak, Mike Halle, Gordon Kindlmann, Raul San Jose Estepar, Haiying Liu, Ron Kikinis
* MIT: Lauren O'Donnell, Kilian Pohl

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#NA-MIC_Kit NA-MIC Kit Projects].

== 3. Highlights ==
The third year of the NAMIC project saw continued development and dissemination of medical image analysis software. With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NAMIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NAMIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

=== 3.1 Advanced Algorithms ===
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NAMIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NAMIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NAMIC Sandbox in preparation for formal acceptance into the NAMIC Kit.

=== 3.2 Technology Deployment Platform: Slicer3 ===
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NAMIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NAMIC Kit, and are now available to all other modules built on the Slicer3 framework.

=== 3.3 Outreach and Technology Transfer ===
Cores 4-5-6 continue to support, train and dissemniate to the NAMIC community, and the broader biomedical computing community.
* NAMIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NAMIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair.

== 4. Impact and Value to Biocomputing ==

In NA-MIC's third year, it is evident that NA-MIC is developing a
culture, environment, and resources to foster and incite collaborative
research in medical image analysis that draws together mathematicians,
computer scientists, software engineers, and clinical
researchers. These artefacts of NA-MIC impact how NA-MIC
operates, make NA-MIC a fulcrum for NIH funded research,
and draws new collaborators from across the country
and around the world to NA-MIC.

=== 4.1 Impact within the Center ===

Within the center, the NA-MIC organization, NA-MIC processes, and the
NA-MIC calendar has permeated the research. The organization is
nimble, forming ad hoc distributed teams within and between cores to
address specific biocomputing tasks. Information is shared freely on
the NA-MIC Wiki, on the weekly Engineering telephone conferences, and
in the NA-MIC Subversion source code repository. The software
engineering tools of CMake, Dart 2 and CTest, CPack, and KWWidgets
facilitate a cross platform software environment for medical image
analysis that be easily built, tested, and distributed to
end-users. Core 2 has provided a platform, Slicer 3, that allows Core
1 to easily integrate new technology and deliver this technology in an
end user application to Core 3. Core 1 has developed a host of
techniques to apply to structural and diffusion analysis which are
under evaluation by Core 3. Major NA-MIC events, such as the annual
All Hands Meeting, the Summer Project Week, the Spring Algorithms
meeting, and Engineering Teleconferences are avidly attended by NA-MIC
researchers as opportunities to foster collaborations.

=== 4.2 Impact within NIH Funded Research ===

Within NIH funded research, NA-MIC continues to forge relationships
with other large NIH funded projects such as BIRN, caBIG, NAC, and
IGT. Here, we are sharing the NA-MIC culture, engineering practices,
and tools. BIRN hosts data for the NA-MIC researchers and NA-MIC hosts
BIRN wikis. caBIG lists the 3D Slicer among the applications available
on the National Cancer Imaging Archive. NAC and IGT use the NA-MIC
infrastructure and are involved in the development of the 3D
Slicer. BIRN recently held an event modeled after the NA-MIC Project
Week. NA-MIC has become a resource on open source licensing to the medical image analysis community.

NA-MIC is also attracting NIH funded collaborations. Two grants have
been funded under PAR-05-063 to collaborate with NA-MIC: ''Automated FE Mesh Development'' and ''Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI''. Five additional
applications to collaborate with NA-MIC via the NCBC collaborative
grant mechanism are under consideration. Additional grant applications
submitted under other calls are planning to use and extend the NA-MIC tools.

=== 4.3 National and International Impact ===

NA-MIC events and tools garner national and international interest.
There were nearly 100 participants at the NA-MIC All Hands Meeting in
January 2007, with many of these participants from outside of NA-MIC.
Several researchers from outside the NA-MIC community have attended
the Summer Project Weeks and the Winter Project Half-Weeks to gain
access to the NA-MIC tools and people. These external researchers are
contributing ideas and technology back into NA-MIC.

Components of the NA-MIC kit are used globally. The software
engineering tools of CMake, Dart 2 and CTest are used by many open
source projects and commercial applications. For example, the K Desktop Environment (KDE) for
Linux and Unix workstations uses CMake and Dart. KDE is one of the
largest open source projects in the world. Many open source projects and
commercial products are benefiting from the NA-MIC related
contributions to ITK and VTK. Finally, Slicer 3 is being used as an
image analysis platform in several fields outside of medical image
analysis, in particular, biological image analysis, astronomy, and industrial inspection.

NA-MIC co-sponsored the ''Workshop on Open Science'' at the Medical
Image Computing and Computer-Assisted Intervention (MICCAI) 2006
conference. The proceedings of the workshop are published on the
electronic Insight Journal, another NIH-funded activity.

Over 50 NA-MIC related publications have been produced since the inception of the center.

== 5.NA-MIC Timeline ==

This section provides a table of NAMIC timelines from the original proposal that graphically depicts completed tasks/goals in years 1, 2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines have also been described.

[[2007_Scientific_Report_Timeline|2007 Scientific Report Timeline]]

== Appendix A Publications ==

http://www.na-mic.org/Wiki/index.php/Publications

== Appendix B EAB Report ==

===EAB Report===
The NA-MIC External Advisory Board (EAB), chaired by Professor Chris Johnson of the University of Utah, met at the annual All-Hands Meeting. After individual presentations by NA-MIC investigators and open as well as closed-door EAB discussion, the Board provided its independent expert assessment of the Center.

[https://share.spl.harvard.edu/grants/u54/2007/APR%202007/NAMIC-EAB-Report07-ver5.doc This is a password protected link to the EAB report.]

===Response to EAB Report===

The NA-MIC leadership discussed the excellent suggestions made by the EAB, and in the following section we respond to each of them.

''EAB: The Center should actively pursue methods to distribute their software and other resources to a broader community. As we noted in last year’s EAB Report, the Center is doing a good job at distributing their software toolkit, however, largely the distribution has been focused internally to NA-MIC collaborators. The EAB recommends that the Center work to more broadly distribute its software to the larger community of neuroimaging researchers. In addition, because the Center’s software tools will also be useful to biomedical researchers outside of neuroimaging, the EAB recommends the Center consider additional methods to “get the word out” about their resources.''

NA-MIC: The focus in the last two years of the Center has been to broaden the outreach effort to the neuroimaging community and to other biomedical researchers outside of this community. Many algorithms have been developed in ITK/VTK, and then ported to Slicer to try to make them as accessible as possible. We believe that the ITK/VTK platforms have been quite widely disseminated. However, as the EAB has noted, the current training and dissemination funds are limited, and the Center leadership will continue to look for creative solutions to get the word out within these resource constraints.

''EAB: The Center is well suited to assess and compare algorithms. An important question in image analysis involves understanding which image-processing algorithm is best suited for a particular task. Given the large number of different image analysis algorithms being created by NA-MIC, the Center is in a good position to evaluate the effectiveness of different algorithms and to make appropriate recommendations.''

NA-MIC: This topic has been discussed extensively within the Center, and we have addressed this issue in a manner that is aligned with our core expertise in open source software. More specifically, we will continue to build upon our strength and provide infrastructure and tools needed for open science, as well as to train researchers in the use of these tools. It is our hope that by providing easy access to these tools, we will be facilitating the process of comparison between algorithms for various biomedical tasks. This being said, many times there is no optimal algorithm for every imaging purpose. A certain segmentation routine may be excellent for one purpose, but not for another. This is a well recognized problem in image processing and computer vision, and not a fundamental limitation of medical imaging. Comparisons, however, among different algorithms to determine which algorithms are best for which questions being addressed is nonetheless important and we have made some inroads here. Sylvain Bouix, for example, has a paper in press in Neuroimage on evaluating brain tissue classifiers without ground truth, which compares several different segmentation algorithms. Also, in the shape analysis field, every NAMIC related publication performs the necessary comparisons. Furthermore, Martin Styner et al authored a paper on shape analysis comparisons at ISBI 2006, as well as he is co-organizing a caudate and liver segmentation comparison workshop at MICCAI 2007 with currently over 35 registered participating methods. Our philosophy is to make a large menu of algorithmic methodologies available in as easy to use form as possible, so as to encourage the kind of comparisons recently done by Drs. Bouix and Styner.

''EAB: The Center should consider additional techniques to validate and verify techniques and software. The EAB was pleased to see the progress the Center made in quantifying and validating their algorithms and implementations within the NA-MIC Kit. However, because this is such an important issue, the EAB recommends the Center continue to make progress in this area.
''

NA-MIC: The Center intends to continue its effort in validation and verification of its software implementations. Center researchers have also come up with original techniques for validation, e.g., the Laplacian method for comparing the accuracy of segmentations. These validation techniques have been used to drive new original algorithmic as well. In addition, work within the Center has continued to compare manual measures of brain structure, i.e., the caudate as a gold standard for automated measures of caudate in the same subjects. This kind of validation will also continue.

''EAB: The Center should generate a set of success criteria. The EAB recommends that the Center create a set of benchmarks of success that it will use in future years to measure the Center’s success and as ways to evaluate the Center’s continuing progress and at the time of the Center’s first renewal.''

NA-MIC: We note that we have established benchmarks for success that are similar to criteria used by study sections for reviewing the success of other grant mechanisms, although also includes specific criteria for NA-MIC, and includes:

• Papers in peer reviewed publications

• Technology transfers from Core 1 to Core 3 researchers

• Software released as part of the NA-MIC Kit

• Training outside researchers in the use of the NA-MIC Kit

= Logistics =

== Schedule and process for preparation of this report ==

* March 30 - Assign section/theme leads (Ron). Last year: introduction (marty), structural analysis (allen, martin), dti (guido, marty/marek), fmri (polina, andy), namic kit(bill), timeline (ross), highlights (will), impact (bill).
*April 7,10 - tcons with Marty, Ross, Will, Tina to finalize the layout, process, and timeline of report.
* April 13 - update projects list using last year's [[NA-MIC_Collaborations]] and [[2007_Project_Half_Week#Projects|projects pursued at the half week in SLC]]. Remind investigators to update individual pages. (Tina)
* April 23- complete project description pages in updated list: [[NA-MIC_Collaborations]] (all project owners).
* April 30 - complete section summaries, introduction, highlights, impact, timeline (owners of these topics)
* May 8 - submit wiki report to NA-MIC editor, Ann (Tina)
* May 17 - submit Edited report to Rachana in pdf format (Ann)
* May 25 - submit final report, if changes are needed (Ann)
* May 29 - ship final package to NIH (Rachana)

== Guidelines from NIH Program Officer ==
===NIH Reviewer Comments on Yr2 APR (July 2006)===
The following additional guidelines were provided by the NIH progress review committee after they reviewed the 2006 report:

1. Comment on obstacles and challenges encountered during the year.

2. Comment on outcomes of DBPs.

3. Create a context for the report in the Introduction.

4. Comment on how all the themes as a whole are moving forward.

5. Full citations should be given within the report as footnotes or in a bibliography, not just as hyperlinks.

6. Explain time-lines with text including reasons for delays. Link timelines to papers and citations.

7. Define terms used in the timeline to describe the amount of progress being made, especially with respect to software development.

8. Put priority weights on completed tasks within timeline. Note if a task is a stepping stone or a final goal.

9. Make sure the projects presented in the annual report are also listed in the project pages of the NAMIC Wiki and vice versa.

10. In the budget pages, please elaborate on the number and locations for travel.

===Feb 2006===
The following guidelines were provided by Grace Peng, NA-MIC program officer, in Feb 2006.

The key is to synthesize all the individual elements into bigger picture stories that really speak of each area’s impact to the community.

The specialized scientific report should have the following format:

# Introductory page describing the new grouping of NAMIC project themes.
# A description of progress in each NAMIC project theme (not to exceed 2 pages each), tying together relevant activities from participating subcomponents and referencing cores in parentheses.
# A table of NAMIC timelines (from original proposal), graphically depicting completed tasks/goals in years 1,2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines should be described.
# A description of 3 highlights selected from all NAMIC projects to showcase NAMIC.
# A discussion of NAMIC’s impact and value to the biocomputing community this year.

2007 Annual Scientific Report

2007-05-20T16:59:25Z

Shenton: /* Response to EAB Report */

Back to [[2007_Progress_Report]]

For reference:

*[[2006_Progress_Report]]

*[[Media:2005_NAMIC_Specialized_ProgReport.pdf|2005 Annual Scientific Report]]

== 1. Introduction ==

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with espect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs continue to include neuropsychiatric disorders such as Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Schizophrenia (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a adopting a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

(Please note that this report is available on the NA-MIC wiki at the following url: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report)

== 2. Four Main Themes ==

This year's activities focus on four main themes: Diffusion Image Analysis, Structural Analysis, Functional MRI Analysis, and the NA-MIC Kit. Each of the following sections begins with an overview of the theme, provides a progress update and list of key investigators, and concludes with a set of links to additional information for individual projects in that theme.

These thematic activities involve scientists from each of the 7 NA-MIC cores (Appendix).

* [http://www.na-mic.org/Wiki/index.php/Algorithm:Main Core 1 Algorithms]-Ross Whitaker PI
* [http://www.na-mic.org/Wiki/index.php/Engineering:Main Core 2 Engineering]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/DBP:Main Core 3 Driving Biological Problems] DBP1-Martha Shenton PI / DBP2-Andy Saykin PI / DBP3-Steven Potkin PI
* [http://www.na-mic.org/Wiki/index.php/Service:Main Core 4 Service]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/Training:Main Core 5 Training]-Randy Gollub PI
* [http://www.na-mic.org/Wiki/index.php/Dissemination:Main Core 6 Dissemination]-Tina Kapur Co-PI; Steve Pieper Co-PI
* [http://www.na-mic.org/Wiki/index.php/Leadership:Main Core 7 Leadership]-Ron Kikinis

=== 2.1 Diffusion Image Analysis Theme ===

==== Progress ====
Over the past year, we have continued to develop a number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical diffusion tensor analysis. These tools have been already integrated into diffusion dedicated software (e.g., Fiber Viewer-UNC). In addition, we started developing tools for group data analysis, such as white matter atlases, and tools for data integration, that would combine and interface among different imaging modalities (such as structural, fMRI and DTI), to better estimate anatomical and functional connectivity. This software has continued to be used in multiple clinical projects involving several psychiatric populations. Below we provide detailed progress in the area of diffusion image analysis.

==== Fiber Tract Extraction and Analysis ====
* Since last year, all of the algorithms for fiber tractography and anisotropy estimation have been implemented in both the “Fiber Viewer” and “Slicer” packages, and now the resulting methods are being applied to clinical studies. Fiber Tract integrity has been investigated and results have been presented at several international conferences for four key fiber tracts in schizophrenia (corpus callosum, fornix, inferior occipito-frontal fasciculus, and uncinate fasciculus). These tracts have been extracted, and fractional anisotropy has been compared between groups, demonstrating fronto-temporal connectivity abnormalities in schizophrenia.

* In addition to clinical studies, teams have been working on other methods to define and estimate brain connectivity. The most important developments in this regard included volumentric connectivity and stochastic tractography measures. For volumentric connectivity, a PDE-based approach to white matter connectivity from DTI has been developed, that is founded on the principal of minimal paths through the tensor volume. This method computes a volumetric representation of a white matter tract given two endpoint regions. In addition, statistical methods for quantifying the full tensor data along these pathways have been also developed. Directional PDE-based flows have been proposed and implemented for a similar purpose. Further, an approach called "stochastic tractography," has been developed to calculate probability of two regions being connected by the fiber tract. This should be especialy useful when tract has to go through the regions characterized by low diffusion anisotropy.

* Some initial attempts have been made to population based analysis of DT-MRI. One method is based on the unbiased non-rigid registration of a population to a common coordinate system. The registration jointly produces an average DTI atlas, which is unbiased with respect to the choice of a template image, along with a diffeomorphic correspondence for each image. The anatomically significant correspondence provides a basis for comparison of tensor features and fiber tract geometry in clinical studies.

==== Fractional Anisotropy Analysis ====
* We have used tools developed last year, and applied them to our population of chronic schizophrenia subjects in order to investigate two fiber tracts: the cingulum bundle and the corpus callosum. In the case of cingulum bundle, manually drawn regions of interest and Finsler geometry were used to extract entire cingulum bundle fiber tract, and FA was estimated along the tract, and compared between groups. Corpus Callosum cross sectional area and its probabilistic subdivisions were determined automatically from the structural MRI scans using a model based deformable contour segmentation. The structural scan was then co-registered with the DTI scan and the anatomical corpus callosum subdivisions were propagated to the associated FA map, and compared between groups, demonstrating deficient interhemispheric communication in schizophrenia.

==== Integration of fMRI and DTI, Path-of-Interest Analysis ====
* Progress has been also made in the development of a tool that would successfully combine and integrate functional and anatomical information. The Optimal Path Analysis method has been applied to the Harvard- BWH fMRI dataset, and anatomical connections between regions active during the fMRI experiment have been extracted for each subject. The connectivity has calculated and compared between groups. This tool is now being ported to Slicer3.

==== Key Investigators ====

* BWH: Martha Shenton, Marek Kubicki, Marc Niethammer, Sylvain Bouix, Jennifer Fitzsimmons, Katarina Quintis, Doug Markant, Kate Smith, Georgia Bushell, Mark Dreusicke, Carl-Fredrik Westin, Raul San Jose, Gordon Kindlmann
* MGH: Bruce Fischl, Denis Jen, David Kennedy
* MIT: Lauren O'Donnell, Polina Golland, Tri Ngo
* UCI: James Fallon, Martina Panzenboeck
* UNC: Guido Gerig, Isabelle Corouge, Casey Goodlett, Martin Styner
* Utah: Tom Fletcher, Ross Whitaker, Saurav Basu, Davis McKay
* GA Tech: Eric Pichon, John Melonakos, Xavier LaFaucheur, Vandana Mohan, Allen Tannenbaum
* Dartmouth: John West, Andrew Saykin, Laura Flashman, Paul Wang, Heather Pixley, Robert Roth
* Isomics: Steve Pieper
* Kitware: Luis Ibanez

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Diffusion_Image_Analysis NA-MIC Projects on Diffusion Image Analysis].

=== 2.2 Structural Analysis Theme ===

==== Progress ====

Within the NAMIC's structural analysis theme, the main topics of interest are structural segmentation, registration and shape analysis. These topics are of course intertwined, e.g., segmentation or registration can directly deliver structural correspondence used in shape analysis, or in turn shape modeling is necessary for good structural segmentations. Here is a selection of progress highlights in the structural analysis theme

====='''Segmentation'''=====

* Wavelet based structural segmentation: We have developed a spherical wavelet based framework for the segmentation of selected brain structures, such as the hippocampus or the caudate nucleus. An automated segmentation of such structures must be highly accurate and include high frequency variations in the surface. Since shape representation is a key component of the segmentation, it must be rich enough to express shape variations at various frequency levels, from low harmonics to sharp edges. Medical object segmentation with deformable models and statistical shape modeling may be combined to obtain a more robust and accurate segmentation. To address this issue, a decomposable spherical wavelet based shape representation targeted to the population seems natural, where the shape parameters are separated into groups that describe independent global and/or local biological variations in the population, and a prior induced over each group explicitly encodes these variations. This work presents three novel contributions for shape representation, multiscale prior probability estimation and segmentation.

* Directional based segmentation: We have proposed an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. In the classical case, the Euclidean metric is locally multiplied by a scalar conformal factor (based on image information) such that the weighted length of curves lying on points of interest (typically edges) is small. We propose to add directionality to the factor, and show that one gets a well-defined minimization problem in the case that the factor defines a Finsler metric. Optimal curves may be obtained using the calculus of variations or dynamic programming. This methodology also makes connections to the important technique of graph-cuts.

* Statistical PDE methods for segmentation in shape space: This past year, we have proposed another method to perform shape-driven segmentation. In our approach, shapes are represented using binary maps, and linear PCA is utilized to provide shape priors for segmentation. Intensity based probability distributions are then employed to convert the given volume into a binary map representation, and a new energy functional is formulated whose minimization is performed using a parametric model for surface evolution in the shape space. Our algorithm is then applied to the segmentation of brain caudate nucleus and hippocampus from MRI data. Our validation shows that the proposed algorithm outperforms the log-likelihood based energy, converges in less than 5 iterations and is very obust to initialization. The overall algorithm is illustrates the potential for segmentation in shape space.

* Rule-based segmentation methods: We have continued this past year to develop segmentation methods based on heuristic rules provided to us by our Core 3 partners for segmenting various brain regions of interest in schizophrenia, e.g. the DLPFC and the striatum. The idea is to try to semi-automate these rules in order to forge an interactive tool for segmentation which can greatly shorten the time necessary for manual segmentation. Typically, these methods are used in conjunction with some Bayesian classifier which further aids to automating and in speeding up the given segmentation methodology.

* Tissue and structural segmentation via EM Segmenter: Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases segmentation is mostly performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We have developed an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior knowledge. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. The mapping between atlas and image space, structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the Maximum A posteriori Probability (MAP) estimation problem. These results are then fed into an Active Mean Field approach, which views the results as priors to a Mean Field approximation with a curve length prior. This segmentation framework has been ported into the NAMIC-toolkit and a first version of the tool is distributed with Slicer 3.

====='''Shape Analysis'''=====

* Wavelet based shape analysis: A shape representation that encodes variations at multiple scales can be useful as a rich feature set for shape analysis and classification. Combining tools from the existing shape analysis toolset, we extended it to include spherical wavelet coefficients (SWC) as features and compared the results obtained to shape analysis using a SPHARM-PDM representation. The rich SWC feature set allows the differentiation of shape differences at various scales as well as highly correlates with the existing SPHARM-PDM analysis but with increased statistical sensitivity. Wavelet coefficient shrinkage and dimension reduction are well-understood and have been widely researched for traditional types of wavelet decompositions but not much explored for the second generation wavelets. During the past year, we have developed a Bayesian model on our specific wavelet structure based on a population of surfaces. For each shape, the deviation from the mean is computed and is modeled as the sum of an unknown signal and a noise. This deviation is encoded by the wavelet transform and our goal is to estimate the wavelet coefficients belonging to the noiseless signal.

* Surface flattening for shape analysis: Flattened representations of undulated surfaces constitute an active area of research in the field of medical imaging and visualization, due to their extensive use for registration and shape analysis of various structures of interest. We have presented a method for flattening anatomical surfaces in an area preserving manner, while minimizing the geometrical distortion. This method is based on the theory of optimal mass transport and conformal mapping of surfaces. The key idea here is the use of a multiresolution scheme for the solution of optimal mass transport gradient descent equations which allows a fast and stable solution for optimal transport. The method has been implemented on a GPU, allowing us to flatten a 128 by 128 by 128 volume in about 5 seconds on a standard workstation.

* Curvature based population correspondence: We have extended the Minimum Description Length population based correspondence framework to include curvature based measurements, such as the Koenderink Shape Index S and Curvedness C in combination with the standard location information. Current methodology in population based correspondence is based mainly on minimizing distribution properties of surface point locations and are thus not invariant to alignment. We have favorably compared our combined "Curvature + Location" MDL to the standard MDL, as well as to the SPHARM approach, in complex structures, such as the striatal brain structure (composed of caudate, nucleus accumbens and putamen).

* Shape analysis toolset: A considerable amount of work was spent on the development aspect of the shape analysis tools. The distributed set of tools is continuously enhanced and the population based correspondence framework has been released as open source. All the tools including the visualization tool, KWMeshVisu, can be called directly from Slicer 3. The visualization tool allows the overlay of scalar, vector and ellipsoid data onto surfaces via versatile colormaps. The attributed surfaces are then visible within Slicer 3. This lean visualization tool fills a niche and is also used in our cortical thickness analysis tool. Also, while it is entirely possible to run all shape analysis steps by calling the individual modules, this is highly inefficient in a larger study. As a result we are developing a separate shape pipeline Slicer module that uses Batchmake to run the shape analysis pipeline as a distributed, background process. The whole shape analysis pipeline will thus become entirely encapsulated and accessible to the trained clinical collaborator.

* Particle based correspondence: We have developed a method for a multi-object correspondence optimization, and have applied it successfully to a proof-of-concept application for the analysis of brain structure complexes from a longitudinal study of pediatric autism. This new method for constructing compact statistical point-based models of ensembles of similar shapes does not rely on any specific surface parameterization, requires little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including non-manifold surfaces and objects of arbitrary topology. The method uses a dynamic particle system to optimize correspondence point positions across all structures by simultaneously maximizing both the geometric accuracy and the statistical simplicity of the model.

==== Key Investigators ====

* MIT: Kilian Pohl, Sandy Wells, Eric Grimson
* UNC: Martin Styner, Ipek Oguz, Guido Gerig, Xavier Barbero
* Utah: Ross Whitaker, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer
* GaTech: Allen Tannenbaum, John Melonakos, Tauseef ur Rehman, Shawn Lankton, Ramsey Al-Hakim, Eric Pichon, Delphine Nain, Oleg Michailovich, Yogesh Rathi, James Malcolm
* Isomics: Steve Pieper
* GE: Bill Lorensen, Jim Miller
* Kitware: Luis Ibanez, Karthik Krishnan,
* UCLA: Michael J. Pan, Jagadeeswaran Rajendiran
* BWH: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt
* Dartmouth: Andrew Saykin
* UCI: James Fallon

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Structural_Image_Analysis NA-MIC Projects on Structural Image Analysis].

=== 2.3 Functional MRI Analysis Theme ===

====Progress====

During this year, the focus of the algorithms and the engineering
cores has been on the structural and DTI analysis. While we continued
to expand the methods and the infrastructure in NAMIC-kit to support
fMRI analysis, as well as using the analysis tools to perform clinical
studies, the emphasis of the work this year has been on integrating
the fMRI analysis with other modalities and supporting other
modalities.

===== Clinical Studies =====

We would like to highlight several clinical studies within NAMIC that
focused on fMRI data and its relationship with other imaging
modalities:

* Imaging Phenotypes in Schizophrenics and Controls: Functional connectivity of the DLPFC by genotype was investigated emplying partial least squares (PLS) correlation analysis. PLS is a multivariate analytical technique used to summarize large neuroimaging data sets in such a way as to correlate patterns of activation with a variable(s) of interest (i.e., DLPFC activity). In the most recent analysis, the DRD1 genotype was used as a grouping variable. This analysis has been submitted for publication (Tura, Turner, Fallon, Kennedy, and Potkin. Genetic Impact on Functional Connectivity in Schizophrenics During a Working Memory Task). Working memory performance did not differ significantly between the two cohorts. However, imaging-genetic analysis showed a significant difference (P< 0.05) between the circuitry engaged by each group. Significance and reliability of the resulting imaging-behavioral patterns within each genotype were assessed by 200 bootstrap and 500 permutation tests, respectively. In one group, the circuitry included the temporal pole, the insula, the dorsolateral prefrontal cortex, and the Brodmann Areas (BA) 1,2,3,4,6,11 and 21, while the other group showed a network comprising the tectum, precuneus retroplenial, vermis, substantia nigra, BA 22,39,8, and 9. The DRD1 polymorphic site may characterize circuitry differences in schizophrenic patients.

*Path-Of-Interest Analysis (joint DTI/fMRI modeling): We collected preliminary data using an application of the “optimal path analysis”. In this analysis, we extracted group fMRI activation due to the Stroop effect (an attentional experiment where incongruent color, in which the word is written, competes with name of the color itself, activating areas responding to conflict monitoring and selection) separately for controls and schizophrenics. This resulted in three clusters of activation, one in the right Dorsolateral Prefrontal Cortex, a second in the Anterior Cingulate Gyrus, and a third in the Medial Parietal Lobe. In the next step, we placed activation clusters in each individual space, by reversing normalization parameters used during fMRI analysis. Finally, EPI fMRI scans were co-registered to DTI scans, and the same registration parameters were applied to activation maps (fMRI results). Regions of activation were used as start and destination points for optimal path analysis, which resulted in three separate paths of optimal connectivity for each subject. The probability of the connections were then calculated for each path and each subject, and compared between groups. In our preliminary analysis, we included 10 control subjects and 10 chronic schizophrenics. Our results demonstrated a relationship between Stroop Effect fMRI activation in the medial parietal area and optimal path connectivity between parietal and cingulate activation sites in schizophrenics (rho=-0.56; p=0.047), which was not observed in controls. These findings suggest that decreased connectivity may result in schizophrenics relying more on posterior parts of the executive attentional network during performance of the Stroop task.

*Hippocampal and frontal memory circuitry abnormalities in schizophrenia: Relation of diffusion, morphometric and fMRI markers: We performed a combined DTI, fMRI, and morphometric study on 13 patients with schizophrenia (SZ) and 14 HC. We identified areas of increased trace diffusivity (TD) in the hippocampal and insular regions as well as areas of reduced fractional anisotropy (FA) in left frontal white matter in SZ relative to HC (p<.01). Voxel based morphometry analyses in a subset of these subjects showed corresponding reductions in gray matter density in hippocampal and insular regions in patients relative to controls (p<.01). Analysis of fMRI results from the novel vs. repeated word contrast from the event-related auditory verbal episodic memory encoding/retrieval task in a subset of the subjects indicated reduced activation in frontal and temporal regions, as well as increased activation in posterior cingulate, retrosplenial, and thalamic regions in SZ relative to HC (p<.05). Further analysis showed that left frontal white matter FA was associated with activation in the left and right hippocampi as well as other frontal and temporal regions, but inversely related to activation in the retrosplenial/posterior cingulate region (p<.05). These initial findings indicate a pattern of relationships between of structural and functional brain abnormalities in schizophrenia and demonstrate the feasibility of integrated quantitative analyses across modalities.

===== Methods =====

During this year, we continued methodological development along two
directions:

*Improving fMRI detectors by incorporating Markov priors on the activation state. We integrated the improved detector into Slicer and performed substantial validation of the methods using fBIRN data provided by the UC Irvine group. Journal paper on the method has been submitted to IEEE TMI.

*Improving registration of EPI images to anatomical scans through modeling of the EPI distortions. We demonstrated an initial model that uses segmentation of the structural scan to predict the distortions in the EPI images. The preliminary results are quite encouraging.

==== Key Investigators ====

* MIT: Polina Golland, Sandy Wells, Wanmei Ou, Claire Poynton
* BWH: Martha Shenton, Marek Kubicki
* Dartmouth: Andy Saykin
* UCI: Jessica Turner, Stephen Potkin
* Toronto: Jim Kennedy

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#fMRI_Analysis NA-MIC Projects on fMRI Analysis].

=== 2.4 [[NA-MIC-Kit|NA-MIC Kit]] Theme ===

==== Progress ====
The continuing vision of the NA-MIC Kit is to provide an open source set of software tools and methodologies that will serve as the foundation for medical image computing projects for both academic and commercial use. Key elements of this vision are:

# ''Unrestrictive License.'' Users of the Kit are free to distribute their derived works under any license suitable to their needs.
# ''Cross Platform.'' This software set can be adapted to the best available price-performance computer systems for any particular use.
# ''Extensible Application Framework.'' New techniques and algorithms can be quickly integrated into a working system. Sophisticated user interfaces can be generated easily through automated processes. Sophisticated toolsets such as ITK, VTK, and KWWidgets are available to create and deploy applications quickly.
# ''Quality Software Process.'' Developers and users can rely on accurate and well documented behavior from all the parts of the Kit.
# ''Sustainable Community.'' Users are actively involved in the design process of the Kit. Documentation, training materials, and hands-on sessions are available and well publicized to the community.

====='''Slicer3'''=====

A major focus of the third year was the implementation of the Slicer3 in the NAMIC Kit. This effort addressed Item #3 above ''Extensible Application Framework.'' The previous two years of the NAMIC project, which entailed gathering requirements from Cores 1 and 3, and developing the computational foundation, toolsets, and software process, came together in the Slicer3 application platform.

Core 2 worked hard to insure that the Slicer 3 application serves, and will continue to serve, as a productive technology deployment platform. The application framework was designed carefully to provide ease-of-use, both in terms of interaction and software integration. Advanced capabilities, including the ability to launch large-scale grid computing, was designed into the application. Some of the key features of the Slicer3 application completed in the third year include the following.
* Advanced application framework including a tuned GUI for ease of use, undo/redo capabilities, 2D/3D view windows, and support for advanced interaction techniques such as 3D widgets. The application provides viewers for displaying slices, volumes, and models including the ability to edit properties. A built in transformation pipeline enables users to confidently import, edit and display data in a consistent coordinate system.
* The application is data-driven based on the next generation MRML scene description file format. Backward compatibility to Slicer 2.x MRML files is preserved.
* A module plug-in architecture and execution model that enables researchers to package and integrate their software into the Slicer3 framework. Plug-in modules can be implemented in a variety of programming languages, and are described using a simple XML description. These modules, when located and loaded into Slicer3, have the capability to automatically generate their user interface, which is seamlessly integrated into the Slicer3 GUI.
* Support for editing and marking data including support for fudicials, paint and draw editors.
* The creation of several simple plug-in modules, including the conversion of previous Slicer 2.x modules to the new Slicer3 architecture.

====='''EM Segment'''=====

As an application framework, Slicer3 provides tools for loading, viewing, measuring, editing, and saving data. To support advanced medical image analysis, plug-in modules are required in conjunction with the Slicer3 core. To demonstrate the capabilities of the framework we implemented the [http://www.na-mic.org/Wiki/index.php/Slicer3:EM EM Segment] module, a sophisticated and proven method for automatically segmenting complex anatomical structures.

To use this module, users specify parameters defining the image protocol and the anatomical structures of interests. This process results in a template that the module uses to automatically segment large data sets. The template is composed of atlas data and a non-trivial collection of parameters for the EM Segment algorithm ([[Media:Pohl-miccai-short-2005.pdf|Pohl et al.]]). Once the parameters are specified, the target images are segmented using the algorithm; if the results are satisfactory, the template is saved and can be used later to segment new images (via the GUI or batch processing). If the results are unsatisfactory, the parameters can be modified and the segmentation re-run.

Besides successfully demonstrating the use of complex algorithms in the Slicer3 framework, this effort also led us to develop tools, including modifications to the underlying KWWidgets GUI toolkit, to support module workflow. With these tools, it is possible to simplify complex modules by dividing the complicated template specification task into a number of smaller, intuitive steps. These steps are enforced by the GUI and reduce the potential for user error, while improving the overall user interface.

====='''Quality Software Process'''=====

Building on [http://lwn.net/Articles/188693/ last year's success with the KDE community], the NAMIC community continued to extend its world-class, open source software process tools CMake, DART, CTest and DART. These tools, which form the core of a quality-oriented, test-driven development (TDD) software process. In particular, the CPack system is now able to automatically package and distibute code, libraries, executables and installers across all of NAMIC's supported platforms (i.e., Linux, Windows, Mac). This enables the NAMIC developer community to rapidly deploy software tools to the user community.

==== Key Investigators ====

* GE: Bill Lorensen, Jim Miller, Xiaodong Tao, Dan Blezek
* Isomics: Steve Pieper, Alex Yarmarkovich
* Kitware: Will Schroeder, Luis Ibanez, Brad Davis, Andy Cedilnik, Sebastien Barre, Bill Hoffman
* UCLA: Mike Pan, Jagadeeswaran Rajendiran
* UCSD: Neil Jones, Jeffrey Grethe, Mark Ellisman
* BWH: Nicole Aucoin, Katie Hayes, Wendy Plesniak, Mike Halle, Gordon Kindlmann, Raul San Jose Estepar, Haiying Liu, Ron Kikinis
* MIT: Lauren O'Donnell, Kilian Pohl

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#NA-MIC_Kit NA-MIC Kit Projects].

== 3. Highlights ==
The third year of the NAMIC project saw continued development and dissemination of medical image analysis software. With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NAMIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NAMIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

=== 3.1 Advanced Algorithms ===
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NAMIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NAMIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NAMIC Sandbox in preparation for formal acceptance into the NAMIC Kit.

=== 3.2 Technology Deployment Platform: Slicer3 ===
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NAMIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NAMIC Kit, and are now available to all other modules built on the Slicer3 framework.

=== 3.3 Outreach and Technology Transfer ===
Cores 4-5-6 continue to support, train and dissemniate to the NAMIC community, and the broader biomedical computing community.
* NAMIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NAMIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair.

== 4. Impact and Value to Biocomputing ==

In NA-MIC's third year, it is evident that NA-MIC is developing a
culture, environment, and resources to foster and incite collaborative
research in medical image analysis that draws together mathematicians,
computer scientists, software engineers, and clinical
researchers. These artefacts of NA-MIC impact how NA-MIC
operates, make NA-MIC a fulcrum for NIH funded research,
and draws new collaborators from across the country
and around the world to NA-MIC.

=== 4.1 Impact within the Center ===

Within the center, the NA-MIC organization, NA-MIC processes, and the
NA-MIC calendar has permeated the research. The organization is
nimble, forming ad hoc distributed teams within and between cores to
address specific biocomputing tasks. Information is shared freely on
the NA-MIC Wiki, on the weekly Engineering telephone conferences, and
in the NA-MIC Subversion source code repository. The software
engineering tools of CMake, Dart 2 and CTest, CPack, and KWWidgets
facilitate a cross platform software environment for medical image
analysis that be easily built, tested, and distributed to
end-users. Core 2 has provided a platform, Slicer 3, that allows Core
1 to easily integrate new technology and deliver this technology in an
end user application to Core 3. Core 1 has developed a host of
techniques to apply to structural and diffusion analysis which are
under evaluation by Core 3. Major NA-MIC events, such as the annual
All Hands Meeting, the Summer Project Week, the Spring Algorithms
meeting, and Engineering Teleconferences are avidly attended by NA-MIC
researchers as opportunities to foster collaborations.

=== 4.2 Impact within NIH Funded Research ===

Within NIH funded research, NA-MIC continues to forge relationships
with other large NIH funded projects such as BIRN, caBIG, NAC, and
IGT. Here, we are sharing the NA-MIC culture, engineering practices,
and tools. BIRN hosts data for the NA-MIC researchers and NA-MIC hosts
BIRN wikis. caBIG lists the 3D Slicer among the applications available
on the National Cancer Imaging Archive. NAC and IGT use the NA-MIC
infrastructure and are involved in the development of the 3D
Slicer. BIRN recently held an event modeled after the NA-MIC Project
Week. NA-MIC has become a resource on open source licensing to the medical image analysis community.

NA-MIC is also attracting NIH funded collaborations. Two grants have
been funded under PAR-05-063 to collaborate with NA-MIC: ''Automated FE Mesh Development'' and ''Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI''. Five additional
applications to collaborate with NA-MIC via the NCBC collaborative
grant mechanism are under consideration. Additional grant applications
submitted under other calls are planning to use and extend the NA-MIC tools.

=== 4.3 National and International Impact ===

NA-MIC events and tools garner national and international interest.
There were nearly 100 participants at the NA-MIC All Hands Meeting in
January 2007, with many of these participants from outside of NA-MIC.
Several researchers from outside the NA-MIC community have attended
the Summer Project Weeks and the Winter Project Half-Weeks to gain
access to the NA-MIC tools and people. These external researchers are
contributing ideas and technology back into NA-MIC.

Components of the NA-MIC kit are used globally. The software
engineering tools of CMake, Dart 2 and CTest are used by many open
source projects and commercial applications. For example, the K Desktop Environment (KDE) for
Linux and Unix workstations uses CMake and Dart. KDE is one of the
largest open source projects in the world. Many open source projects and
commercial products are benefiting from the NA-MIC related
contributions to ITK and VTK. Finally, Slicer 3 is being used as an
image analysis platform in several fields outside of medical image
analysis, in particular, biological image analysis, astronomy, and industrial inspection.

NA-MIC co-sponsored the ''Workshop on Open Science'' at the Medical
Image Computing and Computer-Assisted Intervention (MICCAI) 2006
conference. The proceedings of the workshop are published on the
electronic Insight Journal, another NIH-funded activity.

Over 50 NA-MIC related publications have been produced since the inception of the center.

== 5.NA-MIC Timeline ==

This section provides a table of NAMIC timelines from the original proposal that graphically depicts completed tasks/goals in years 1, 2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines have also been described.

[[2007_Scientific_Report_Timeline|2007 Scientific Report Timeline]]

== Appendix A Publications ==

http://www.na-mic.org/Wiki/index.php/Publications

== Appendix B EAB Report ==

===EAB Report===
The NA-MIC External Advisory Board (EAB), chaired by Professor Chris Johnson of the University of Utah, met at the annual All-Hands Meeting. After individual presentations by NA-MIC investigators and open as well as closed-door EAB discussion, the Board provided its independent expert assessment of the Center.

[https://share.spl.harvard.edu/grants/u54/2007/APR%202007/NAMIC-EAB-Report07-ver5.doc This is a password protected link to the EAB report.]

===Response to EAB Report===

The NA-MIC leadership discussed the excellent suggestions made by the EAB, and in the following section we respond to each of them.

''EAB: The Center should actively pursue methods to distribute their software and other resources to a broader community. As we noted in last year’s EAB Report, the Center is doing a good job at distributing their software toolkit, however, largely the distribution has been focused internally to NA-MIC collaborators. The EAB recommends that the Center work to more broadly distribute its software to the larger community of neuroimaging researchers. In addition, because the Center’s software tools will also be useful to biomedical researchers outside of neuroimaging, the EAB recommends the Center consider additional methods to “get the word out” about their resources.''

NA-MIC: The focus in the last two years of the Center has been to broaden the outreach effort to the neuroimaging community and to other biomedical researchers outside of this community. Many algorithms have been developed in ITK/VTK, and then ported to Slicer to try to make them as accessible as possible. We believe that the ITK/VTK platforms have been quite widely disseminated. However, as the EAB has noted, the current training and dissemination funds are limited, and the Center leadership will continue to look for creative solutions to get the word out within these resource constraints.

''EAB: The Center is well suited to assess and compare algorithms. An important question in image analysis involves understanding which image-processing algorithm is best suited for a particular task. Given the large number of different image analysis algorithms being created by NA-MIC, the Center is in a good position to evaluate the effectiveness of different algorithms and to make appropriate recommendations.''

NA-MIC: This topic has been discussed extensively within the Center, and we have addressed this issue in a manner that is aligned with our core expertise in open source software. More specifically, we will continue to build upon our strength and provide infrastructure and tools needed for open science, as well as to train researchers in the use of these tools. It is our hope that by providing easy access to these tools, we will be facilitating the process of comparison between algorithms for various biomedical tasks. This being said, many times there is no optimal algorithm for every imaging purpose. A certain segmentation routine may be excellent for one purpose, but not for another. This is a well recognized problem in image processing and computer vision, and not a fundamental limitation of medical imaging. Comparisons, however, among different algorithms to determine which algorithms are best for which questions being addressed is nonetheless important and we have made some inroads here. Sylvain Bouix, for example, has a paper in press in Neuroimage on evaluating brain tissue classifiers without ground truth, which compares several different segmentation algorithms. Our philosophy is to make a large menu of algorthmic methodologies available in as easy to use form as possible, so as to encourage the kind of comparisons recently done by Dr. Bouix..

''EAB: The Center should consider additional techniques to validate and verify techniques and software. The EAB was pleased to see the progress the Center made in quantifying and validating their algorithms and implementations within the NA-MIC Kit. However, because this is such an important issue, the EAB recommends the Center continue to make progress in this area.
''
NA-MIC: The Center intends to continue its effort in validation and verification of its software implementations. Center researchers have also come up with original techniques for validation, e.g., the Laplacian method for comparing the accuracy of segmentations. These validation techniques have been used to drive new original algorithmic as well. In addition, work within the Center has continued to compare manual measures of brain structure, i.e., the caudate as a gold standard for automated measures of caudate in the same subjects. This kind of validation will also continue.

''EAB: The Center should generate a set of success criteria. The EAB recommends that the Center create a set of benchmarks of success that it will use in future years to measure the Center’s success and as ways to evaluate the Center’s continuing progress and at the time of the Center’s first renewal.''

NA-MIC: We note that we have established benchmarks for success that are similar to criteria used by study sections for reviewing the success of other grant mechanisms, although also includes specific criteria for NA-MIC, and includes:

• Papers in peer reviewed publications

• Technology transfers from Core 1 to Core 3 researchers

• Software released as part of the NA-MIC Kit

• Training outside researchers in the use of the NA-MIC Kit

= Logistics =

== Schedule and process for preparation of this report ==

* March 30 - Assign section/theme leads (Ron). Last year: introduction (marty), structural analysis (allen, martin), dti (guido, marty/marek), fmri (polina, andy), namic kit(bill), timeline (ross), highlights (will), impact (bill).
*April 7,10 - tcons with Marty, Ross, Will, Tina to finalize the layout, process, and timeline of report.
* April 13 - update projects list using last year's [[NA-MIC_Collaborations]] and [[2007_Project_Half_Week#Projects|projects pursued at the half week in SLC]]. Remind investigators to update individual pages. (Tina)
* April 23- complete project description pages in updated list: [[NA-MIC_Collaborations]] (all project owners).
* April 30 - complete section summaries, introduction, highlights, impact, timeline (owners of these topics)
* May 8 - submit wiki report to NA-MIC editor, Ann (Tina)
* May 17 - submit Edited report to Rachana in pdf format (Ann)
* May 25 - submit final report, if changes are needed (Ann)
* May 29 - ship final package to NIH (Rachana)

== Guidelines from NIH Program Officer ==
===NIH Reviewer Comments on Yr2 APR (July 2006)===
The following additional guidelines were provided by the NIH progress review committee after they reviewed the 2006 report:

1. Comment on obstacles and challenges encountered during the year.

2. Comment on outcomes of DBPs.

3. Create a context for the report in the Introduction.

4. Comment on how all the themes as a whole are moving forward.

5. Full citations should be given within the report as footnotes or in a bibliography, not just as hyperlinks.

6. Explain time-lines with text including reasons for delays. Link timelines to papers and citations.

7. Define terms used in the timeline to describe the amount of progress being made, especially with respect to software development.

8. Put priority weights on completed tasks within timeline. Note if a task is a stepping stone or a final goal.

9. Make sure the projects presented in the annual report are also listed in the project pages of the NAMIC Wiki and vice versa.

10. In the budget pages, please elaborate on the number and locations for travel.

===Feb 2006===
The following guidelines were provided by Grace Peng, NA-MIC program officer, in Feb 2006.

The key is to synthesize all the individual elements into bigger picture stories that really speak of each area’s impact to the community.

The specialized scientific report should have the following format:

# Introductory page describing the new grouping of NAMIC project themes.
# A description of progress in each NAMIC project theme (not to exceed 2 pages each), tying together relevant activities from participating subcomponents and referencing cores in parentheses.
# A table of NAMIC timelines (from original proposal), graphically depicting completed tasks/goals in years 1,2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines should be described.
# A description of 3 highlights selected from all NAMIC projects to showcase NAMIC.
# A discussion of NAMIC’s impact and value to the biocomputing community this year.

2007 Annual Scientific Report

2007-05-19T23:27:15Z

Shenton: /* EAB Report */

Back to [[2007_Progress_Report]]

For reference:

*[[2006_Progress_Report]]

*[[Media:2005_NAMIC_Specialized_ProgReport.pdf|2005 Annual Scientific Report]]

== 1. Introduction ==

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) for the first three years of the Center came from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with espect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

In addition, the end of the first three years of the center marks a transition from the first set of DBPs that were focused entirely on Schizophrenia to a new set that span a wider range of biological problems. The new DBPs continue to include neuropsychiatric disorders such as Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Schizophrenia (Harvard), and Autism (University of North Carolina, Chapel Hill), along with a adopting a direction that is new but synergistic for NA-MIC: Prostate Interventions (Johns Hopkins University). Funding for the second round of DBPs starts in the next cycle, but the PIs were able to attend the recent All-hands meeting and start developing plans for their future research in NA-MIC.

Finally, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

(Please note that this report is available on the NA-MIC wiki at the following url: http://www.na-mic.org/Wiki/index.php/2007_Annual_Scientific_Report)

== 2. Four Main Themes ==

This year's activities focus on four main themes: Diffusion Image Analysis, Structural Analysis, Functional MRI Analysis, and the NA-MIC Kit. Each of the following sections begins with an overview of the theme, provides a progress update and list of key investigators, and concludes with a set of links to additional information for individual projects in that theme.

These thematic activities involve scientists from each of the 7 NA-MIC cores (Appendix).

* [http://www.na-mic.org/Wiki/index.php/Algorithm:Main Core 1 Algorithms]-Ross Whitaker PI
* [http://www.na-mic.org/Wiki/index.php/Engineering:Main Core 2 Engineering]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/DBP:Main Core 3 Driving Biological Problems] DBP1-Martha Shenton PI / DBP2-Andy Saykin PI / DBP3-Steven Potkin PI
* [http://www.na-mic.org/Wiki/index.php/Service:Main Core 4 Service]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/Training:Main Core 5 Training]-Randy Gollub PI
* [http://www.na-mic.org/Wiki/index.php/Dissemination:Main Core 6 Dissemination]-Tina Kapur Co-PI; Steve Pieper Co-PI
* [http://www.na-mic.org/Wiki/index.php/Leadership:Main Core 7 Leadership]-Ron Kikinis

=== 2.1 Diffusion Image Analysis Theme ===

==== Progress ====
Over the past year, we have continued to develop a number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical diffusion tensor analysis. These tools have been already integrated into diffusion dedicated software (e.g., Fiber Viewer-UNC). In addition, we started developing tools for group data analysis, such as white matter atlases, and tools for data integration, that would combine and interface among different imaging modalities (such as structural, fMRI and DTI), to better estimate anatomical and functional connectivity. This software has continued to be used in multiple clinical projects involving several psychiatric populations. Below we provide detailed progress in the area of diffusion image analysis.

==== Fiber Tract Extraction and Analysis ====
* Since last year, all of the algorithms for fiber tractography and anisotropy estimation have been implemented in both the “Fiber Viewer” and “Slicer” packages, and now the resulting methods are being applied to clinical studies. Fiber Tract integrity has been investigated and results have been presented at several international conferences for four key fiber tracts in schizophrenia (corpus callosum, fornix, inferior occipito-frontal fasciculus, and uncinate fasciculus). These tracts have been extracted, and fractional anisotropy has been compared between groups, demonstrating fronto-temporal connectivity abnormalities in schizophrenia.

* In addition to clinical studies, teams have been working on other methods to define and estimate brain connectivity. The most important developments in this regard included volumentric connectivity and stochastic tractography measures. For volumentric connectivity, a PDE-based approach to white matter connectivity from DTI has been developed, that is founded on the principal of minimal paths through the tensor volume. This method computes a volumetric representation of a white matter tract given two endpoint regions. In addition, statistical methods for quantifying the full tensor data along these pathways have been also developed. Directional PDE-based flows have been proposed and implemented for a similar purpose. Further, an approach called "stochastic tractography," has been developed to calculate probability of two regions being connected by the fiber tract. This should be especialy useful when tract has to go through the regions characterized by low diffusion anisotropy.

* Some initial attempts have been made to population based analysis of DT-MRI. One method is based on the unbiased non-rigid registration of a population to a common coordinate system. The registration jointly produces an average DTI atlas, which is unbiased with respect to the choice of a template image, along with a diffeomorphic correspondence for each image. The anatomically significant correspondence provides a basis for comparison of tensor features and fiber tract geometry in clinical studies.

==== Fractional Anisotropy Analysis ====
* We have used tools developed last year, and applied them to our population of chronic schizophrenia subjects in order to investigate two fiber tracts: the cingulum bundle and the corpus callosum. In the case of cingulum bundle, manually drawn regions of interest and Finsler geometry were used to extract entire cingulum bundle fiber tract, and FA was estimated along the tract, and compared between groups. Corpus Callosum cross sectional area and its probabilistic subdivisions were determined automatically from the structural MRI scans using a model based deformable contour segmentation. The structural scan was then co-registered with the DTI scan and the anatomical corpus callosum subdivisions were propagated to the associated FA map, and compared between groups, demonstrating deficient interhemispheric communication in schizophrenia.

==== Integration of fMRI and DTI, Path-of-Interest Analysis ====
* Progress has been also made in the development of a tool that would successfully combine and integrate functional and anatomical information. The Optimal Path Analysis method has been applied to the Harvard- BWH fMRI dataset, and anatomical connections between regions active during the fMRI experiment have been extracted for each subject. The connectivity has calculated and compared between groups. This tool is now being ported to Slicer3.

==== Key Investigators ====

* BWH: Martha Shenton, Marek Kubicki, Marc Niethammer, Sylvain Bouix, Jennifer Fitzsimmons, Katarina Quintis, Doug Markant, Kate Smith, Georgia Bushell, Mark Dreusicke, Carl-Fredrik Westin, Raul San Jose, Gordon Kindlmann
* MGH: Bruce Fischl, Denis Jen, David Kennedy
* MIT: Lauren O'Donnell, Polina Golland, Tri Ngo
* UCI: James Fallon, Martina Panzenboeck
* UNC: Guido Gerig, Isabelle Corouge, Casey Goodlett, Martin Styner
* Utah: Tom Fletcher, Ross Whitaker, Saurav Basu, Davis McKay
* GA Tech: Eric Pichon, John Melonakos, Xavier LaFaucheur, Vandana Mohan, Allen Tannenbaum
* Dartmouth: John West, Andrew Saykin, Laura Flashman, Paul Wang, Heather Pixley, Robert Roth
* Isomics: Steve Pieper
* Kitware: Luis Ibanez

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Diffusion_Image_Analysis NA-MIC Projects on Diffusion Image Analysis].

=== 2.2 Structural Analysis Theme ===

==== Progress ====

Within the NAMIC's structural analysis theme, the main topics of interest are structural segmentation, registration and shape analysis. These topics are of course intertwined, e.g., segmentation or registration can directly deliver structural correspondence used in shape analysis, or in turn shape modeling is necessary for good structural segmentations. Here is a selection of progress highlights in the structural analysis theme

====='''Segmentation'''=====

* Wavelet based structural segmentation: We have developed a spherical wavelet based framework for the segmentation of selected brain structures, such as the hippocampus or the caudate nucleus. An automated segmentation of such structures must be highly accurate and include high frequency variations in the surface. Since shape representation is a key component of the segmentation, it must be rich enough to express shape variations at various frequency levels, from low harmonics to sharp edges. Medical object segmentation with deformable models and statistical shape modeling may be combined to obtain a more robust and accurate segmentation. To address this issue, a decomposable spherical wavelet based shape representation targeted to the population seems natural, where the shape parameters are separated into groups that describe independent global and/or local biological variations in the population, and a prior induced over each group explicitly encodes these variations. This work presents three novel contributions for shape representation, multiscale prior probability estimation and segmentation.

* Directional based segmentation: We have proposed an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. In the classical case, the Euclidean metric is locally multiplied by a scalar conformal factor (based on image information) such that the weighted length of curves lying on points of interest (typically edges) is small. We propose to add directionality to the factor, and show that one gets a well-defined minimization problem in the case that the factor defines a Finsler metric. Optimal curves may be obtained using the calculus of variations or dynamic programming. This methodology also makes connections to the important technique of graph-cuts.

* Statistical PDE methods for segmentation in shape space: This past year, we have proposed another method to perform shape-driven segmentation. In our approach, shapes are represented using binary maps, and linear PCA is utilized to provide shape priors for segmentation. Intensity based probability distributions are then employed to convert the given volume into a binary map representation, and a new energy functional is formulated whose minimization is performed using a parametric model for surface evolution in the shape space. Our algorithm is then applied to the segmentation of brain caudate nucleus and hippocampus from MRI data. Our validation shows that the proposed algorithm outperforms the log-likelihood based energy, converges in less than 5 iterations and is very obust to initialization. The overall algorithm is illustrates the potential for segmentation in shape space.

* Rule-based segmentation methods: We have continued this past year to develop segmentation methods based on heuristic rules provided to us by our Core 3 partners for segmenting various brain regions of interest in schizophrenia, e.g. the DLPFC and the striatum. The idea is to try to semi-automate these rules in order to forge an interactive tool for segmentation which can greatly shorten the time necessary for manual segmentation. Typically, these methods are used in conjunction with some Bayesian classifier which further aids to automating and in speeding up the given segmentation methodology.

* Tissue and structural segmentation via EM Segmenter: Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases segmentation is mostly performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We have developed an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior knowledge. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. The mapping between atlas and image space, structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the Maximum A posteriori Probability (MAP) estimation problem. These results are then fed into an Active Mean Field approach, which views the results as priors to a Mean Field approximation with a curve length prior. This segmentation framework has been ported into the NAMIC-toolkit and a first version of the tool is distributed with Slicer 3.

====='''Shape Analysis'''=====

* Wavelet based shape analysis: A shape representation that encodes variations at multiple scales can be useful as a rich feature set for shape analysis and classification. Combining tools from the existing shape analysis toolset, we extended it to include spherical wavelet coefficients (SWC) as features and compared the results obtained to shape analysis using a SPHARM-PDM representation. The rich SWC feature set allows the differentiation of shape differences at various scales as well as highly correlates with the existing SPHARM-PDM analysis but with increased statistical sensitivity. Wavelet coefficient shrinkage and dimension reduction are well-understood and have been widely researched for traditional types of wavelet decompositions but not much explored for the second generation wavelets. During the past year, we have developed a Bayesian model on our specific wavelet structure based on a population of surfaces. For each shape, the deviation from the mean is computed and is modeled as the sum of an unknown signal and a noise. This deviation is encoded by the wavelet transform and our goal is to estimate the wavelet coefficients belonging to the noiseless signal.

* Surface flattening for shape analysis: Flattened representations of undulated surfaces constitute an active area of research in the field of medical imaging and visualization, due to their extensive use for registration and shape analysis of various structures of interest. We have presented a method for flattening anatomical surfaces in an area preserving manner, while minimizing the geometrical distortion. This method is based on the theory of optimal mass transport and conformal mapping of surfaces. The key idea here is the use of a multiresolution scheme for the solution of optimal mass transport gradient descent equations which allows a fast and stable solution for optimal transport. The method has been implemented on a GPU, allowing us to flatten a 128 by 128 by 128 volume in about 5 seconds on a standard workstation.

* Curvature based population correspondence: We have extended the Minimum Description Length population based correspondence framework to include curvature based measurements, such as the Koenderink Shape Index S and Curvedness C in combination with the standard location information. Current methodology in population based correspondence is based mainly on minimizing distribution properties of surface point locations and are thus not invariant to alignment. We have favorably compared our combined "Curvature + Location" MDL to the standard MDL, as well as to the SPHARM approach, in complex structures, such as the striatal brain structure (composed of caudate, nucleus accumbens and putamen).

* Shape analysis toolset: A considerable amount of work was spent on the development aspect of the shape analysis tools. The distributed set of tools is continuously enhanced and the population based correspondence framework has been released as open source. All the tools including the visualization tool, KWMeshVisu, can be called directly from Slicer 3. The visualization tool allows the overlay of scalar, vector and ellipsoid data onto surfaces via versatile colormaps. The attributed surfaces are then visible within Slicer 3. This lean visualization tool fills a niche and is also used in our cortical thickness analysis tool. Also, while it is entirely possible to run all shape analysis steps by calling the individual modules, this is highly inefficient in a larger study. As a result we are developing a separate shape pipeline Slicer module that uses Batchmake to run the shape analysis pipeline as a distributed, background process. The whole shape analysis pipeline will thus become entirely encapsulated and accessible to the trained clinical collaborator.

* Particle based correspondence: We have developed a method for a multi-object correspondence optimization, and have applied it successfully to a proof-of-concept application for the analysis of brain structure complexes from a longitudinal study of pediatric autism. This new method for constructing compact statistical point-based models of ensembles of similar shapes does not rely on any specific surface parameterization, requires little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including non-manifold surfaces and objects of arbitrary topology. The method uses a dynamic particle system to optimize correspondence point positions across all structures by simultaneously maximizing both the geometric accuracy and the statistical simplicity of the model.

==== Key Investigators ====

* MIT: Kilian Pohl, Sandy Wells, Eric Grimson
* UNC: Martin Styner, Ipek Oguz, Guido Gerig, Xavier Barbero
* Utah: Ross Whitaker, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer
* GaTech: Allen Tannenbaum, John Melonakos, Tauseef ur Rehman, Shawn Lankton, Ramsey Al-Hakim, Eric Pichon, Delphine Nain, Oleg Michailovich, Yogesh Rathi, James Malcolm
* Isomics: Steve Pieper
* GE: Bill Lorensen, Jim Miller
* Kitware: Luis Ibanez, Karthik Krishnan,
* UCLA: Michael J. Pan, Jagadeeswaran Rajendiran
* BWH: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt
* Dartmouth: Andrew Saykin
* UCI: James Fallon

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Structural_Image_Analysis NA-MIC Projects on Structural Image Analysis].

=== 2.3 Functional MRI Analysis Theme ===

====Progress====

During this year, the focus of the algorithms and the engineering
cores has been on the structural and DTI analysis. While we continued
to expand the methods and the infrastructure in NAMIC-kit to support
fMRI analysis, as well as using the analysis tools to perform clinical
studies, the emphasis of the work this year has been on integrating
the fMRI analysis with other modalities and supporting other
modalities.

===== Clinical Studies =====

We would like to highlight several clinical studies within NAMIC that
focused on fMRI data and its relationship with other imaging
modalities:

* Imaging Phenotypes in Schizophrenics and Controls: Functional connectivity of the DLPFC by genotype was investigated emplying partial least squares (PLS) correlation analysis. PLS is a multivariate analytical technique used to summarize large neuroimaging data sets in such a way as to correlate patterns of activation with a variable(s) of interest (i.e., DLPFC activity). In the most recent analysis, the DRD1 genotype was used as a grouping variable. This analysis has been submitted for publication (Tura, Turner, Fallon, Kennedy, and Potkin. Genetic Impact on Functional Connectivity in Schizophrenics During a Working Memory Task). Working memory performance did not differ significantly between the two cohorts. However, imaging-genetic analysis showed a significant difference (P< 0.05) between the circuitry engaged by each group. Significance and reliability of the resulting imaging-behavioral patterns within each genotype were assessed by 200 bootstrap and 500 permutation tests, respectively. In one group, the circuitry included the temporal pole, the insula, the dorsolateral prefrontal cortex, and the Brodmann Areas (BA) 1,2,3,4,6,11 and 21, while the other group showed a network comprising the tectum, precuneus retroplenial, vermis, substantia nigra, BA 22,39,8, and 9. The DRD1 polymorphic site may characterize circuitry differences in schizophrenic patients.

*Path-Of-Interest Analysis (joint DTI/fMRI modeling): We collected preliminary data using an application of the “optimal path analysis”. In this analysis, we extracted group fMRI activation due to the Stroop effect (an attentional experiment where incongruent color, in which the word is written, competes with name of the color itself, activating areas responding to conflict monitoring and selection) separately for controls and schizophrenics. This resulted in three clusters of activation, one in the right Dorsolateral Prefrontal Cortex, a second in the Anterior Cingulate Gyrus, and a third in the Medial Parietal Lobe. In the next step, we placed activation clusters in each individual space, by reversing normalization parameters used during fMRI analysis. Finally, EPI fMRI scans were co-registered to DTI scans, and the same registration parameters were applied to activation maps (fMRI results). Regions of activation were used as start and destination points for optimal path analysis, which resulted in three separate paths of optimal connectivity for each subject. The probability of the connections were then calculated for each path and each subject, and compared between groups. In our preliminary analysis, we included 10 control subjects and 10 chronic schizophrenics. Our results demonstrated a relationship between Stroop Effect fMRI activation in the medial parietal area and optimal path connectivity between parietal and cingulate activation sites in schizophrenics (rho=-0.56; p=0.047), which was not observed in controls. These findings suggest that decreased connectivity may result in schizophrenics relying more on posterior parts of the executive attentional network during performance of the Stroop task.

*Hippocampal and frontal memory circuitry abnormalities in schizophrenia: Relation of diffusion, morphometric and fMRI markers: We performed a combined DTI, fMRI, and morphometric study on 13 patients with schizophrenia (SZ) and 14 HC. We identified areas of increased trace diffusivity (TD) in the hippocampal and insular regions as well as areas of reduced fractional anisotropy (FA) in left frontal white matter in SZ relative to HC (p<.01). Voxel based morphometry analyses in a subset of these subjects showed corresponding reductions in gray matter density in hippocampal and insular regions in patients relative to controls (p<.01). Analysis of fMRI results from the novel vs. repeated word contrast from the event-related auditory verbal episodic memory encoding/retrieval task in a subset of the subjects indicated reduced activation in frontal and temporal regions, as well as increased activation in posterior cingulate, retrosplenial, and thalamic regions in SZ relative to HC (p<.05). Further analysis showed that left frontal white matter FA was associated with activation in the left and right hippocampi as well as other frontal and temporal regions, but inversely related to activation in the retrosplenial/posterior cingulate region (p<.05). These initial findings indicate a pattern of relationships between of structural and functional brain abnormalities in schizophrenia and demonstrate the feasibility of integrated quantitative analyses across modalities.

===== Methods =====

During this year, we continued methodological development along two
directions:

*Improving fMRI detectors by incorporating Markov priors on the activation state. We integrated the improved detector into Slicer and performed substantial validation of the methods using fBIRN data provided by the UC Irvine group. Journal paper on the method has been submitted to IEEE TMI.

*Improving registration of EPI images to anatomical scans through modeling of the EPI distortions. We demonstrated an initial model that uses segmentation of the structural scan to predict the distortions in the EPI images. The preliminary results are quite encouraging.

==== Key Investigators ====

* MIT: Polina Golland, Sandy Wells, Wanmei Ou, Claire Poynton
* BWH: Martha Shenton, Marek Kubicki
* Dartmouth: Andy Saykin
* UCI: Jessica Turner, Stephen Potkin
* Toronto: Jim Kennedy

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#fMRI_Analysis NA-MIC Projects on fMRI Analysis].

=== 2.4 [[NA-MIC-Kit|NA-MIC Kit]] Theme ===

==== Progress ====
The continuing vision of the NA-MIC Kit is to provide an open source set of software tools and methodologies that will serve as the foundation for medical image computing projects for both academic and commercial use. Key elements of this vision are:

# ''Unrestrictive License.'' Users of the Kit are free to distribute their derived works under any license suitable to their needs.
# ''Cross Platform.'' This software set can be adapted to the best available price-performance computer systems for any particular use.
# ''Extensible Application Framework.'' New techniques and algorithms can be quickly integrated into a working system. Sophisticated user interfaces can be generated easily through automated processes. Sophisticated toolsets such as ITK, VTK, and KWWidgets are available to create and deploy applications quickly.
# ''Quality Software Process.'' Developers and users can rely on accurate and well documented behavior from all the parts of the Kit.
# ''Sustainable Community.'' Users are actively involved in the design process of the Kit. Documentation, training materials, and hands-on sessions are available and well publicized to the community.

====='''Slicer3'''=====

A major focus of the third year was the implementation of the Slicer3 in the NAMIC Kit. This effort addressed Item #3 above ''Extensible Application Framework.'' The previous two years of the NAMIC project, which entailed gathering requirements from Cores 1 and 3, and developing the computational foundation, toolsets, and software process, came together in the Slicer3 application platform.

Core 2 worked hard to insure that the Slicer 3 application serves, and will continue to serve, as a productive technology deployment platform. The application framework was designed carefully to provide ease-of-use, both in terms of interaction and software integration. Advanced capabilities, including the ability to launch large-scale grid computing, was designed into the application. Some of the key features of the Slicer3 application completed in the third year include the following.
* Advanced application framework including a tuned GUI for ease of use, undo/redo capabilities, 2D/3D view windows, and support for advanced interaction techniques such as 3D widgets. The application provides viewers for displaying slices, volumes, and models including the ability to edit properties. A built in transformation pipeline enables users to confidently import, edit and display data in a consistent coordinate system.
* The application is data-driven based on the next generation MRML scene description file format. Backward compatibility to Slicer 2.x MRML files is preserved.
* A module plug-in architecture and execution model that enables researchers to package and integrate their software into the Slicer3 framework. Plug-in modules can be implemented in a variety of programming languages, and are described using a simple XML description. These modules, when located and loaded into Slicer3, have the capability to automatically generate their user interface, which is seamlessly integrated into the Slicer3 GUI.
* Support for editing and marking data including support for fudicials, paint and draw editors.
* The creation of several simple plug-in modules, including the conversion of previous Slicer 2.x modules to the new Slicer3 architecture.

====='''EM Segment'''=====

As an application framework, Slicer3 provides tools for loading, viewing, measuring, editing, and saving data. To support advanced medical image analysis, plug-in modules are required in conjunction with the Slicer3 core. To demonstrate the capabilities of the framework we implemented the [http://www.na-mic.org/Wiki/index.php/Slicer3:EM EM Segment] module, a sophisticated and proven method for automatically segmenting complex anatomical structures.

To use this module, users specify parameters defining the image protocol and the anatomical structures of interests. This process results in a template that the module uses to automatically segment large data sets. The template is composed of atlas data and a non-trivial collection of parameters for the EM Segment algorithm ([[Media:Pohl-miccai-short-2005.pdf|Pohl et al.]]). Once the parameters are specified, the target images are segmented using the algorithm; if the results are satisfactory, the template is saved and can be used later to segment new images (via the GUI or batch processing). If the results are unsatisfactory, the parameters can be modified and the segmentation re-run.

Besides successfully demonstrating the use of complex algorithms in the Slicer3 framework, this effort also led us to develop tools, including modifications to the underlying KWWidgets GUI toolkit, to support module workflow. With these tools, it is possible to simplify complex modules by dividing the complicated template specification task into a number of smaller, intuitive steps. These steps are enforced by the GUI and reduce the potential for user error, while improving the overall user interface.

====='''Quality Software Process'''=====

Building on [http://lwn.net/Articles/188693/ last year's success with the KDE community], the NAMIC community continued to extend its world-class, open source software process tools CMake, DART, CTest and DART. These tools, which form the core of a quality-oriented, test-driven development (TDD) software process. In particular, the CPack system is now able to automatically package and distibute code, libraries, executables and installers across all of NAMIC's supported platforms (i.e., Linux, Windows, Mac). This enables the NAMIC developer community to rapidly deploy software tools to the user community.

==== Key Investigators ====

* GE: Bill Lorensen, Jim Miller, Xiaodong Tao, Dan Blezek
* Isomics: Steve Pieper, Alex Yarmarkovich
* Kitware: Will Schroeder, Luis Ibanez, Brad Davis, Andy Cedilnik, Sebastien Barre, Bill Hoffman
* UCLA: Mike Pan, Jagadeeswaran Rajendiran
* UCSD: Neil Jones, Jeffrey Grethe, Mark Ellisman
* BWH: Nicole Aucoin, Katie Hayes, Wendy Plesniak, Mike Halle, Gordon Kindlmann, Raul San Jose Estepar, Haiying Liu, Ron Kikinis
* MIT: Lauren O'Donnell, Kilian Pohl

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#NA-MIC_Kit NA-MIC Kit Projects].

== 3. Highlights ==
The third year of the NAMIC project saw continued development and dissemination of medical image analysis software. With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NAMIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NAMIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

=== 3.1 Advanced Algorithms ===
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NAMIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NAMIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NAMIC Sandbox in preparation for formal acceptance into the NAMIC Kit.

=== 3.2 Technology Deployment Platform: Slicer3 ===
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NAMIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NAMIC Kit, and are now available to all other modules built on the Slicer3 framework.

=== 3.3 Outreach and Technology Transfer ===
Cores 4-5-6 continue to support, train and dissemniate to the NAMIC community, and the broader biomedical computing community.
* NAMIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NAMIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair.

== 4. Impact and Value to Biocomputing ==

In NA-MIC's third year, it is evident that NA-MIC is developing a
culture, environment, and resources to foster and incite collaborative
research in medical image analysis that draws together mathematicians,
computer scientists, software engineers, and clinical
researchers. These artefacts of NA-MIC impact how NA-MIC
operates, make NA-MIC a fulcrum for NIH funded research,
and draws new collaborators from across the country
and around the world to NA-MIC.

=== 4.1 Impact within the Center ===

Within the center, the NA-MIC organization, NA-MIC processes, and the
NA-MIC calendar has permeated the research. The organization is
nimble, forming ad hoc distributed teams within and between cores to
address specific biocomputing tasks. Information is shared freely on
the NA-MIC Wiki, on the weekly Engineering telephone conferences, and
in the NA-MIC Subversion source code repository. The software
engineering tools of CMake, Dart 2 and CTest, CPack, and KWWidgets
facilitate a cross platform software environment for medical image
analysis that be easily built, tested, and distributed to
end-users. Core 2 has provided a platform, Slicer 3, that allows Core
1 to easily integrate new technology and deliver this technology in an
end user application to Core 3. Core 1 has developed a host of
techniques to apply to structural and diffusion analysis which are
under evaluation by Core 3. Major NA-MIC events, such as the annual
All Hands Meeting, the Summer Project Week, the Spring Algorithms
meeting, and Engineering Teleconferences are avidly attended by NA-MIC
researchers as opportunities to foster collaborations.

=== 4.2 Impact within NIH Funded Research ===

Within NIH funded research, NA-MIC continues to forge relationships
with other large NIH funded projects such as BIRN, caBIG, NAC, and
IGT. Here, we are sharing the NA-MIC culture, engineering practices,
and tools. BIRN hosts data for the NA-MIC researchers and NA-MIC hosts
BIRN wikis. caBIG lists the 3D Slicer among the applications available
on the National Cancer Imaging Archive. NAC and IGT use the NA-MIC
infrastructure and are involved in the development of the 3D
Slicer. BIRN recently held an event modeled after the NA-MIC Project
Week. NA-MIC has become a resource on open source licensing to the medical image analysis community.

NA-MIC is also attracting NIH funded collaborations. Two grants have
been funded under PAR-05-063 to collaborate with NA-MIC: ''Automated FE Mesh Development'' and ''Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI''. Five additional
applications to collaborate with NA-MIC via the NCBC collaborative
grant mechanism are under consideration. Additional grant applications
submitted under other calls are planning to use and extend the NA-MIC tools.

=== 4.3 National and International Impact ===

NA-MIC events and tools garner national and international interest.
There were nearly 100 participants at the NA-MIC All Hands Meeting in
January 2007, with many of these participants from outside of NA-MIC.
Several researchers from outside the NA-MIC community have attended
the Summer Project Weeks and the Winter Project Half-Weeks to gain
access to the NA-MIC tools and people. These external researchers are
contributing ideas and technology back into NA-MIC.

Components of the NA-MIC kit are used globally. The software
engineering tools of CMake, Dart 2 and CTest are used by many open
source projects and commercial applications. For example, the K Desktop Environment (KDE) for
Linux and Unix workstations uses CMake and Dart. KDE is one of the
largest open source projects in the world. Many open source projects and
commercial products are benefiting from the NA-MIC related
contributions to ITK and VTK. Finally, Slicer 3 is being used as an
image analysis platform in several fields outside of medical image
analysis, in particular, biological image analysis, astronomy, and industrial inspection.

NA-MIC co-sponsored the ''Workshop on Open Science'' at the Medical
Image Computing and Computer-Assisted Intervention (MICCAI) 2006
conference. The proceedings of the workshop are published on the
electronic Insight Journal, another NIH-funded activity.

Over 50 NA-MIC related publications have been produced since the inception of the center.

== 5.NA-MIC Timeline ==

This section provides a table of NAMIC timelines from the original proposal that graphically depicts completed tasks/goals in years 1, 2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines have also been described.

[[2007_Scientific_Report_Timeline|2007 Scientific Report Timeline]]

== Appendix A Publications ==

http://www.na-mic.org/Wiki/index.php/Publications

== Appendix B EAB Report ==

===EAB Report===
The NA-MIC External Advisory Board (EAB), chaired by Professor Chris Johnson of the University of Utah, met at the annual All-Hands Meeting. After individual presentations by NA-MIC investigators and open as well as closed-door EAB discussion, the Board provided its independent expert assessment of the Center.

[https://share.spl.harvard.edu/grants/u54/2007/APR%202007/NAMIC-EAB-Report07-ver5.doc This is a password protected link to the EAB report.]

===Response to EAB Report===

The NA-MIC leadership discussed the excellent suggestions by the EAB, and in the following section provides its response to these.

''EAB: The Center should actively pursue methods to distribute their software and other resources to a broader community. As we noted in last year’s EAB Report, Center is doing a good job at distributing their software toolkit, however, largely the distribution has been focused internally to NA-MIC collaborators. The EAB recommends that the Center work to more broadly distribute its software to the larger community of neuroimaging researchers. In addition, because the Center’s software tools will also be useful to biomedical researchers outside of neuroimaging, the EAB recommends the Center consider additional methods to “get the word out” about their resources. ''

NA-MIC: The focus in the last two years of the Center is indeed to broaden the outreach effort to the neuroimaging community and to other biomedical researchers outside of this community. However, as the EAB noted as well, the current training and dissemination funds are limited, and the center leadership will continue to look for creative solutions to get the word out within these resource constraints.

''The Center is well suited to assess and compare algorithms. An important question in image analysis involves understanding which image-processing algorithm is best suited for a particular task. Given the large number of different image analysis algorithms being created by NA-MIC, the Center is in a good position to evaluate the effectiveness of different algorithms and to make appropriate recommendations.''

NA-MIC: This topic has been discussed extensively within the center, and we have arrived at a solution that allows us to address this problem in a manner that is aligned with our core expertise in open source software. We will continue to build upon our strength and provide infrastructure and tools needed for open science, and train researchers in the use of these tools. It is our hope that by providing easy access to these tools, we are facilitating the process of comparison between algorithms for various biomedical tasks.

''The Center should consider additional techniques to validate and verify techniques and software. The EAB was pleased to see the progress the Center made in quantifying and validating their algorithms and implementations within the NA-MIC Kit. However, because this is such an important issue, the EAB recommends the Center continue to make progress in this area.''

NA-MIC: The center intends to continue its effort in validation and verification of its software implementations.

''The Center should generate a set of success criteria. The EAB recommends that the Center create a set of benchmarks of success that it will use in future years to measure the Center’s success and as ways to evaluate the Center’s continuing progress and at the time of the Center’s first renewal.''

NA-MIC: The following success criteria have been organically evolved in NA-MIC:
*Papers in peer reviewed publications
*Software released as part of the NA-MIC Kit
*Training outside researchers in the use of the NA-MIC Kit

= Logistics =

== Schedule and process for preparation of this report ==

* March 30 - Assign section/theme leads (Ron). Last year: introduction (marty), structural analysis (allen, martin), dti (guido, marty/marek), fmri (polina, andy), namic kit(bill), timeline (ross), highlights (will), impact (bill).
*April 7,10 - tcons with Marty, Ross, Will, Tina to finalize the layout, process, and timeline of report.
* April 13 - update projects list using last year's [[NA-MIC_Collaborations]] and [[2007_Project_Half_Week#Projects|projects pursued at the half week in SLC]]. Remind investigators to update individual pages. (Tina)
* April 23- complete project description pages in updated list: [[NA-MIC_Collaborations]] (all project owners).
* April 30 - complete section summaries, introduction, highlights, impact, timeline (owners of these topics)
* May 8 - submit wiki report to NA-MIC editor, Ann (Tina)
* May 17 - submit Edited report to Rachana in pdf format (Ann)
* May 25 - submit final report, if changes are needed (Ann)
* May 29 - ship final package to NIH (Rachana)

== Guidelines from NIH Program Officer ==
===NIH Reviewer Comments on Yr2 APR (July 2006)===
The following additional guidelines were provided by the NIH progress review committee after they reviewed the 2006 report:

1. Comment on obstacles and challenges encountered during the year.

2. Comment on outcomes of DBPs.

3. Create a context for the report in the Introduction.

4. Comment on how all the themes as a whole are moving forward.

5. Full citations should be given within the report as footnotes or in a bibliography, not just as hyperlinks.

6. Explain time-lines with text including reasons for delays. Link timelines to papers and citations.

7. Define terms used in the timeline to describe the amount of progress being made, especially with respect to software development.

8. Put priority weights on completed tasks within timeline. Note if a task is a stepping stone or a final goal.

9. Make sure the projects presented in the annual report are also listed in the project pages of the NAMIC Wiki and vice versa.

10. In the budget pages, please elaborate on the number and locations for travel.

===Feb 2006===
The following guidelines were provided by Grace Peng, NA-MIC program officer, in Feb 2006.

The key is to synthesize all the individual elements into bigger picture stories that really speak of each area’s impact to the community.

The specialized scientific report should have the following format:

# Introductory page describing the new grouping of NAMIC project themes.
# A description of progress in each NAMIC project theme (not to exceed 2 pages each), tying together relevant activities from participating subcomponents and referencing cores in parentheses.
# A table of NAMIC timelines (from original proposal), graphically depicting completed tasks/goals in years 1,2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines should be described.
# A description of 3 highlights selected from all NAMIC projects to showcase NAMIC.
# A discussion of NAMIC’s impact and value to the biocomputing community this year.

2007 Annual Scientific Report

2007-05-06T15:15:07Z

Shenton: /* 1. Introduction (Marty Shenton) */

Back to [[2007_Progress_Report]]

For reference:

*[[Media:2006_Submitted_NA-MIC_Scientific_Report.pdf|2006 Annual Scientific Report]], [[Media:2006_APR_NIH_Questions_and_Answers.pdf|2006 Followup Questions and Answers]].
*[[Media:2005_NAMIC_Specialized_ProgReport.pdf|2005 Annual Scientific Report]]

== 1. Introduction (Marty Shenton)==

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) come from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with espect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

Additionally, with respect to progress, we note that Core 3.1 (Shenton and Saykin), are in the process of applying for a Collaborative R01 to expand current research with NA-MIC, which ends on July 31, 2007. Both Drs. Shenton and Saykin have worked for three years in driving tool development for shape measures, DTI tools, and path analysis measures for fMRI as part of the driving biological project in NA-MIC, and they now plan to expand this research in a Collaborative R01 by working closely with Drs. Westin, Miller, Pieper, and Wells, to design, assess, implement, and apply tools that will enable the integration of MRI, DTI, and fMRI in individual subjects, as well as to develop an atlas of functional networks and circuits that are based on a DTI atlas (i.e., structural connectivity), which will be integrated with a network of functional connectivity that will be identified from fMRI probes of attention, memory, emotion, and semantic processing. We mention this here because this will be, to our knowledge, the first DBP to apply for further funding to continue critical work begun with NA-MIC.

Finally, we are pleased to have XX new DBPs DESCRIBE THEM. Based on our previous experience with the former DBPs, we will be starting with a theme focus TRUE??? Say something HERE TINA>>>>

== 2. Four Main Themes ==

This year's activities focus on four main themes: Diffusion Image Analysis, Structural Analysis, Functional MRI Analysis, and the NA-MIC Kit. Each of the following sections begins with an overview of the theme, provides a progress update and list of key investigators, and concludes with a set of links to additional information for individual projects in that theme.

These thematic activities involve scientists from each of the 7 NA-MIC cores (Appendix).

* [http://www.na-mic.org/Wiki/index.php/Algorithm:Main Core 1 Algorithms]-Ross Whitaker PI
* [http://www.na-mic.org/Wiki/index.php/Engineering:Main Core 2 Engineering]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/DBP:Main Core 3 Driving Biological Problems] DBP1-Martha Shenton PI / DBP2-Andy Saykin PI / DBP3-Steven Potkin PI
* [http://www.na-mic.org/Wiki/index.php/Service:Main Core 4 Service]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/Training:Main Core 5 Training]-Randy Gollub PI
* [http://www.na-mic.org/Wiki/index.php/Dissemination:Main Core 6 Dissemination]-Tina Kapur Co-PI; Steve Pieper Co-PI
* [http://www.na-mic.org/Wiki/index.php/Leadership:Main Core 7 Leadership]-Ron Kikinis

=== 2.1 Diffusion Image Analysis Theme (Marek Kubicki, Guido Gerig)===

==== Progress ====
Over the past year, we have continued to develop a number of tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical diffusion tensor analysis. These tools have been already integrated into diffusion dedicated software (e.g., Fiber Viewer-UNC). In addition, we started developing tools for group data analysis, such as white matter atlases, and tools for data integration, that would combine and interface among different imaging modalities (such as structural, fMRI and DTI), to better estimate anatomical and functional connectivity. This software has continued to be used in multiple clinical projects involving several psychiatric populations. Below we provide detailed progress in the area of diffusion image analysis.

==== Fiber Tract Extraction and Analysis ====
* Since last year, all of the algorithms for fiber tractography and anisotropy estimation have been implemented in both the “Fiber Viewer” and “Slicer” packages, and now the resulting methods are being applied to clinical studies. Fiber Tract integrity has been investigated and results have been presented at several international conferences for four key fiber tracts in schizophrenia (corpus callosum, fornix, inferior occipito-frontal fasciculus, and uncinate fasciculus). These tracts have been extracted, and fractional anisotropy has been compared between groups, demonstrating fronto-temporal connectivity abnormalities in schizophrenia.

* In addition to clinical studies, teams have been working on other methods to define and estimate brain connectivity. The most important developments in this regard included volumentric connectivity and stochastic tractography measures. For volumentric connectivity, a PDE-based approach to white matter connectivity from DTI has been developed, that is founded on the principal of minimal paths through the tensor volume. This method computes a volumetric representation of a white matter tract given two endpoint regions. In addition, statistical methods for quantifying the full tensor data along these pathways have been also developed. Directional PDE-based flows have been proposed and implemented for a similar purpose. Further, an approach called "stochastic tractography," has been developed to calculate probability of two regions being connected by the fiber tract. This should be especialy useful when tract has to go through the regions characterized by low diffusion anisotropy.

* Some initial attempts have been made to population based analysis of DT-MRI. One method is based on the unbiased non-rigid registration of a population to a common coordinate system. The registration jointly produces an average DTI atlas, which is unbiased with respect to the choice of a template image, along with a diffeomorphic correspondence for each image. The anatomically significant correspondence provides a basis for comparison of tensor features and fiber tract geometry in clinical studies.

==== Fractional Anisotropy Analysis ====
* We have used tools developed last year, and applied them to our population of chronic schizophrenia subjects in order to investigate two fiber tracts: the cingulum bundle and the corpus callosum. In the case of cingulum bundle, manually drawn regions of interest and Finsler geometry were used to extract entire cingulum bundle fiber tract, and FA was estimated along the tract, and compared between groups. Corpus Callosum cross sectional area and its probabilistic subdivisions were determined automatically from the structural MRI scans using a model based deformable contour segmentation. The structural scan was then co-registered with the DTI scan and the anatomical corpus callosum subdivisions were propagated to the associated FA map, and compared between groups, demonstrating deficient interhemispheric communication in schizophrenia.

==== Integration of fMRI and DTI, Path-of-Interest Analysis ====
* Progress has been also made in the development of a tool that would successfully combine and integrate functional and anatomical information. The Optimal Path Analysis method has been applied to the Harvard- BWH fMRI dataset, and anatomical connections between regions active during the fMRI experiment have been extracted for each subject. The connectivity has calculated and compared between groups. This tool is now being ported to Slicer3.

==== Key Investigators ====

* BWH: Martha Shenton, Marek Kubicki, Marc Niethammer, Sylvain Bouix, Jennifer Fitzsimmons, Katarina Quintis, Doug Markant, Kate Smith, Georgia Bushell, Mark Dreusicke, Carl-Fredrik Westin, Raul San Jose, Gordon Kindlmann
* MGH: Bruce Fischl, Denis Jen, David Kennedy
* MIT: Lauren O'Donnell, Polina Golland, Tri Ngo
* UCI: James Fallon, Martina Panzenboeck
* UNC: Guido Gerig, Isabelle Corouge, Casey Goodlett, Martin Styner
* Utah: Tom Fletcher, Ross Whitaker, Saurav Basu, Davis McKay
* GA Tech: Eric Pichon, John Melonakos, Xavier LaFaucheur, Vandana Mohan, Allen Tannenbaum
* Dartmouth: John West, Andrew Saykin, Laura Flashman, Paul Wang, Heather Pixley, Robert Roth
* Isomics: Steve Pieper
* Kitware: Luis Ibanez

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Diffusion_Image_Analysis NA-MIC Projects on Diffusion Image Analysis].

=== 2.2 Structural Analysis Theme (Allen Tannenbaum, Martin Styner)===

==== Progress ====

Within the NAMIC's structural analysis theme, the main topics of interest are structural segmentation, registration and shape analysis. These topics are of course intertwined, e.g., segmentation or registration can directly deliver structural correspondence used in shape analysis, or in turn shape modeling is necessary for good structural segmentations. Here is a selection of progress highlights in the structural analysis theme

====='''Segmentation'''=====

* Wavelet based structural segmentation: We have developed a spherical wavelet based framework for the segmentation of selected brain structures, such as the hippocampus or the caudate nucleus. An automated segmentation of such structures must be highly accurate and include high frequency variations in the surface. Since shape representation is a key component of the segmentation, it must be rich enough to express shape variations at various frequency levels, from low harmonics to sharp edges. Medical object segmentation with deformable models and statistical shape modeling may be combined to obtain a more robust and accurate segmentation. To address this issue, a decomposable spherical wavelet based shape representation targeted to the population seems natural, where the shape parameters are separated into groups that describe independent global and/or local biological variations in the population, and a prior induced over each group explicitly encodes these variations. This work presents three novel contributions for shape representation, multiscale prior probability estimation and segmentation.

* Directional based segmentation: We have proposed an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. In the classical case, the Euclidean metric is locally multiplied by a scalar conformal factor (based on image information) such that the weighted length of curves lying on points of interest (typically edges) is small. We propose to add directionality to the factor, and show that one gets a well-defined minimization problem in the case that the factor defines a Finsler metric. Optimal curves may be obtained using the calculus of variations or dynamic programming. This methodology also makes connections to the important technique of graph-cuts.

* Statistical PDE methods for segmentation in shape space: This past year, we have proposed another method to perform shape-driven segmentation. In our approach, shapes are represented using binary maps, and linear PCA is utilized to provide shape priors for segmentation. Intensity based probability distributions are then employed to convert the given volume into a binary map representation, and a new energy functional is formulated whose minimization is performed using a parametric model for surface evolution in the shape space. Our algorithm is then applied to the segmentation of brain caudate nucleus and hippocampus from MRI data. Our validation shows that the proposed algorithm outperforms the log-likelihood based energy, converges in less than 5 iterations and is very obust to initialization. The overall algorithm is illustrates the potential for segmentation in shape space.

* Rule-based segmentation methods: We have continued this past year to develop segmentation methods based on heuristic rules provided to us by our Core 3 partners for segmenting various brain regions of interest in schizophrenia, e.g. the DLPFC and the striatum. The idea is to try to semi-automate these rules in order to forge an interactive tool for segmentation which can greatly shorten the time necessary for manual segmentation. Typically, these methods are used in conjunction with some Bayesian classifier which further aids to automating and in speeding up the given segmentation methodology.

* Tissue and structural segmentation via EM Segmenter: Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases segmentation is mostly performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We have developed an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior knowledge. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. The mapping between atlas and image space, structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the Maximum A posteriori Probability (MAP) estimation problem. These results are then fed into an Active Mean Field approach, which views the results as priors to a Mean Field approximation with a curve length prior. This segmentation framework has been ported into the NAMIC-toolkit and a first version of the tool is distributed with Slicer 3.

====='''Shape Analysis'''=====

* Wavelet based shape analysis: A shape representation that encodes variations at multiple scales can be useful as a rich feature set for shape analysis and classification. Combining tools from the existing shape analysis toolset, we extended it to include spherical wavelet coefficients (SWC) as features and compared the results obtained to shape analysis using a SPHARM-PDM representation. The rich SWC feature set allows the differentiation of shape differences at various scales as well as highly correlates with the existing SPHARM-PDM analysis but with increased statistical sensitivity. Wavelet coefficient shrinkage and dimension reduction are well-understood and have been widely researched for traditional types of wavelet decompositions but not much explored for the second generation wavelets. During the past year, we have developed a Bayesian model on our specific wavelet structure based on a population of surfaces. For each shape, the deviation from the mean is computed and is modeled as the sum of an unknown signal and a noise. This deviation is encoded by the wavelet transform and our goal is to estimate the wavelet coefficients belonging to the noiseless signal.

* Surface flattening for shape analysis: Flattened representations of undulated surfaces constitute an active area of research in the field of medical imaging and visualization, due to their extensive use for registration and shape analysis of various structures of interest. We have presented a method for flattening anatomical surfaces in an area preserving manner, while minimizing the geometrical distortion. This method is based on the theory of optimal mass transport and conformal mapping of surfaces. The key idea here is the use of a multiresolution scheme for the solution of optimal mass transport gradient descent equations which allows a fast and stable solution for optimal transport. The method has been implemented on a GPU, allowing us to flatten a 128 by 128 by 128 volume in about 5 seconds on a standard workstation.

* Curvature based population correspondence: We have extended the Minimum Description Length population based correspondence framework to include curvature based measurements, such as the Koenderink Shape Index S and Curvedness C in combination with the standard location information. Current methodology in population based correspondence is based mainly on minimizing distribution properties of surface point locations and are thus not invariant to alignment. We have favorably compared our combined "Curvature + Location" MDL to the standard MDL, as well as to the SPHARM approach, in complex structures, such as the striatal brain structure (composed of caudate, nucleus accumbens and putamen).

* Shape analysis toolset: A considerable amount of work was spent on the development aspect of the shape analysis tools. The distributed set of tools is continuously enhanced and the population based correspondence framework has been released as open source. All the tools including the visualization tool, KWMeshVisu, can be called directly from Slicer 3. The visualization tool allows the overlay of scalar, vector and ellipsoid data onto surfaces via versatile colormaps. The attributed surfaces are then visible within Slicer 3. This lean visualization tool fills a niche and is also used in our cortical thickness analysis tool. Also, while it is entirely possible to run all shape analysis steps by calling the individual modules, this is highly inefficient in a larger study. As a result we are developing a separate shape pipeline Slicer module that uses Batchmake to run the shape analysis pipeline as a distributed, background process. The whole shape analysis pipeline will thus become entirely encapsulated and accessible to the trained clinical collaborator.

* Particle based correspondence: We have developed a method for a multi-object correspondence optimization, and have applied it successfully to a proof-of-concept application for the analysis of brain structure complexes from a longitudinal study of pediatric autism. This new method for constructing compact statistical point-based models of ensembles of similar shapes does not rely on any specific surface parameterization, requires little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including non-manifold surfaces and objects of arbitrary topology. The method uses a dynamic particle system to optimize correspondence point positions across all structures by simultaneously maximizing both the geometric accuracy and the statistical simplicity of the model.

==== Key Investigators ====

* MIT: Kilian Pohl, Sandy Wells, Eric Grimson
* UNC: Martin Styner, Ipek Oguz, Guido Gerig, Xavier Barbero
* Utah: Ross Whitaker, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer
* GaTech: Allen Tannenbaum, John Melonakos, Tauseef ur Rehman, Shawn Lankton, Ramsey Al-Hakim, Eric Pichon, Delphine Nain, Oleg Michailovich, Yogesh Rathi, James Malcolm
* Isomics: Steve Pieper
* GE: Bill Lorensen, Jim Miller
* Kitware: Luis Ibanez, Karthik Krishnan,
* UCLA: Michael J. Pan, Jagadeeswaran Rajendiran
* BWH: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt
* Dartmouth: Andrew Saykin
* UCI: James Fallon

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Structural_Image_Analysis NA-MIC Projects on Structural Image Analysis].

=== 2.3 Functional MRI Analysis Theme (Polina Golland, Andy Saykin)===

====Progress====

During this year, the focus of the algorithms and the engineering
cores has been on the structural and DTI analysis. While we continued
to expand the methods and the infrastructure in NAMIC-kit to support
fMRI analysis, as well as using the analysis tools to perform clinical
studies, the emphasis of the work this year has been on integrating
the fMRI analysis with other modalities and supporting other
modalities.

===== Clinical Studies =====

We would like to highlight several clinical studies within NAMIC that
focused on fMRI data and its relationship with other imaging
modalities:

* Imaging Phenotypes in Schizophrenics and Controls: Functional connectivity of the DLPFC by genotype was investigated emplying partial least squares (PLS) correlation analysis. PLS is a multivariate analytical technique used to summarize large neuroimaging data sets in such a way as to correlate patterns of activation with a variable(s) of interest (i.e., DLPFC activity). In the most recent analysis, the DRD1 genotype was used as a grouping variable. This analysis has been submitted for publication (Tura, Turner, Fallon, Kennedy, and Potkin. Genetic Impact on Functional Connectivity in Schizophrenics During a Working Memory Task). Working memory performance did not differ significantly between the two cohorts. However, imaging-genetic analysis showed a significant difference (P< 0.05) between the circuitry engaged by each group. Significance and reliability of the resulting imaging-behavioral patterns within each genotype were assessed by 200 bootstrap and 500 permutation tests, respectively. In one group, the circuitry included the temporal pole, the insula, the dorsolateral prefrontal cortex, and the Brodmann Areas (BA) 1,2,3,4,6,11 and 21, while the other group showed a network comprising the tectum, precuneus retroplenial, vermis, substantia nigra, BA 22,39,8, and 9. The DRD1 polymorphic site may characterize circuitry differences in schizophrenic patients.

*Path-Of-Interest Analysis (joint DTI/fMRI modeling): We collected preliminary data using an application of the “optimal path analysis”. In this analysis, we extracted group fMRI activation due to the Stroop effect (an attentional experiment where incongruent color, in which the word is written, competes with name of the color itself, activating areas responding to conflict monitoring and selection) separately for controls and schizophrenics. This resulted in three clusters of activation, one in the right Dorsolateral Prefrontal Cortex, a second in the Anterior Cingulate Gyrus, and a third in the Medial Parietal Lobe. In the next step, we placed activation clusters in each individual space, by reversing normalization parameters used during fMRI analysis. Finally, EPI fMRI scans were co-registered to DTI scans, and the same registration parameters were applied to activation maps (fMRI results). Regions of activation were used as start and destination points for optimal path analysis, which resulted in three separate paths of optimal connectivity for each subject. The probability of the connections were then calculated for each path and each subject, and compared between groups. In our preliminary analysis, we included 10 control subjects and 10 chronic schizophrenics. Our results demonstrated a relationship between Stroop Effect fMRI activation in the medial parietal area and optimal path connectivity between parietal and cingulate activation sites in schizophrenics (rho=-0.56; p=0.047), which was not observed in controls. These findings suggest that decreased connectivity may result in schizophrenics relying more on posterior parts of the executive attentional network during performance of the Stroop task.

*Hippocampal and frontal memory circuitry abnormalities in schizophrenia: Relation of diffusion, morphometric and fMRI markers: We performed a combined DTI, fMRI, and morphometric study on 13 patients with schizophrenia (SZ) and 14 HC. We identified areas of increased trace diffusivity (TD) in the hippocampal and insular regions as well as areas of reduced fractional anisotropy (FA) in left frontal white matter in SZ relative to HC (p<.01). Voxel based morphometry analyses in a subset of these subjects showed corresponding reductions in gray matter density in hippocampal and insular regions in patients relative to controls (p<.01). Analysis of fMRI results from the novel vs. repeated word contrast from the event-related auditory verbal episodic memory encoding/retrieval task in a subset of the subjects indicated reduced activation in frontal and temporal regions, as well as increased activation in posterior cingulate, retrosplenial, and thalamic regions in SZ relative to HC (p<.05). Further analysis showed that left frontal white matter FA was associated with activation in the left and right hippocampi as well as other frontal and temporal regions, but inversely related to activation in the retrosplenial/posterior cingulate region (p<.05). These initial findings indicate a pattern of relationships between of structural and functional brain abnormalities in schizophrenia and demonstrate the feasibility of integrated quantitative analyses across modalities. Publications: West JD, Saykin AJ, Roth RM, Flashman LA, Koven N, Pendergrass JC, Arfanakis K. Hippocampal and frontal memory circuitry abnormalities in schizophrenia: Relation of diffusion, morphometric and fMRI markers. 13th Annual Meeting of the Organization for Human Brain Mapping, Chicago, IL, USA, June, 2007. Journal paper in preparation.

===== Methods =====

During this year, we continued methodological development along two
directions:

*Improving fMRI detectors by incorporating Markov priors on the activation state. We integrated the improved detector into Slicer and performed substantial validation of the methods using fBIRN data provided by the UC Irvine group. Journal paper on the method has been submitted to IEEE TMI.

*Improving registration of EPI images to anatomical scans through modeling of the EPI distortions. We demonstrated an initial model that uses segmentation of the structural scan to predict the distortions in the EPI images. The preliminary results are quite encouraging.

==== Key Investigators ====

* MIT: Polina Golland, Sandy Wells, Wanmei Ou, Claire Poynton
* BWH: Martha Shenton, Marek Kubicki
* Dartmouth: Andy Saykin
* UCI: Jessica Turner, Stephen Potkin
* Toronto: Jim Kennedy

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#fMRI_Analysis NA-MIC Projects on fMRI Analysis].

=== 2.4 [[NA-MIC-Kit|NA-MIC Kit]] Theme (Will Schroeder)===

==== Progress ====
The continuing vision of the NA-MIC Kit is to provide an open source set of software tools and methodologies that will serve as the foundation for medical image computing projects for both academic and commercial use. Key elements of this vision are:

# ''Unrestrictive License.'' Users of the Kit are free to distribute their derived works under any license suitable to their needs.
# ''Cross Platform.'' This software set can be adapted to the best available price-performance computer systems for any particular use.
# ''Extensible Application Framework.'' New techniques and algorithms can be quickly integrated into a working system. Sophisticated user interfaces can be generated easily through automated processes. Sophisticated toolsets such as ITK, VTK, and KWWidgets are available to create and deploy applications quickly.
# ''Quality Software Process.'' Developers and users can rely on accurate and well documented behavior from all the parts of the Kit.
# ''Sustainable Community.'' Users are actively involved in the design process of the Kit. Documentation, training materials, and hands-on sessions are available and well publicized to the community.

====='''Slicer3'''=====

A major focus of the third year was the implementation of the Slicer3 in the NAMIC Kit. This effort addressed Item #3 above ''Extensible Application Framework.'' The previous two years of the NAMIC project, which entailed gathering requirements from Cores 1 and 3, and developing the computational foundation, toolsets, and software process, came together in the Slicer3 application platform.

Core 2 worked hard to insure that the Slicer 3 application serves, and will continue to serve, as a productive technology deployment platform. The application framework was designed carefully to provide ease-of-use, both in terms of interaction and software integration. Advanced capabilities, including the ability to launch large-scale grid computing, was designed into the application. Some of the key features of the Slicer3 application completed in the third year include the following.
* Advanced application framework including a tuned GUI for ease of use, undo/redo capabilities, 2D/3D view windows, and support for advanced interaction techniques such as 3D widgets. The application provides viewers for displaying slices, volumes, and models including the ability to edit properties. A built in transformation pipeline enables users to confidently import, edit and display data in a consistent coordinate system.
* The application is data-driven based on the next generation MRML scene description file format. Backward compatibility to Slicer 2.x MRML files is preserved.
* A module plug-in architecture and execution model that enables researchers to package and integrate their software into the Slicer3 framework. Plug-in modules can be implemented in a variety of programming languages, and are described using a simple XML description. These modules, when located and loaded into Slicer3, have the capability to automatically generate their user interface, which is seamlessly integrated into the Slicer3 GUI.
* Support for editing and marking data including support for fudicials, paint and draw editors.
* The creation of several simple plug-in modules, including the conversion of previous Slicer 2.x modules to the new Slicer3 architecture.

====='''EM Segment'''=====

As an application framework, Slicer3 provides tools for loading, viewing, measuring, editing, and saving data. To support advanced medical image analysis, plug-in modules are required in conjunction with the Slicer3 core. To demonstrate the capabilities of the framework we implemented the [http://www.na-mic.org/Wiki/index.php/Slicer3:EM EM Segment] module, a sophisticated and proven method for automatically segmenting complex anatomical structures.

To use this module, users specify parameters defining the image protocol and the anatomical structures of interests. This process results in a template that the module uses to automatically segment large data sets. The template is composed of atlas data and a non-trivial collection of parameters for the EM Segment algorithm ([[Media:Pohl-miccai-short-2005.pdf|Pohl et al.]]). Once the parameters are specified, the target images are segmented using the algorithm; if the results are satisfactory, the template is saved and can be used later to segment new images (via the GUI or batch processing). If the results are unsatisfactory, the parameters can be modified and the segmentation re-run.

Besides successfully demonstrating the use of complex algorithms in the Slicer3 framework, this effort also led us to develop tools, including modifications to the underlying KWWidgets GUI toolkit, to support module workflow. With these tools, it is possible to simplify complex modules by dividing the complicated template specification task into a number of smaller, intuitive steps. These steps are enforced by the GUI and reduce the potential for user error, while improving the overall user interface.

====='''Quality Software Process'''=====

Building on [http://lwn.net/Articles/188693/ last year's success with the KDE community], the NAMIC community continued to extend its world-class, open source software process tools CMake, DART, CTest and DART. These tools, which form the core of a quality-oriented, test-driven development (TDD) software process. In particular, the CPack system is now able to automatically package and distibute code, libraries, executables and installers across all of NAMIC's supported platforms (i.e., Linux, Windows, Mac). This enables the NAMIC developer community to rapidly deploy software tools to the user community.

==== Key Investigators ====

* GE: Bill Lorensen, Jim Miller, Xiaodong Tao, Dan Blezek
* Isomics: Steve Pieper, Alex Yarmarkovich
* Kitware: Will Schroeder, Luis Ibanez, Brad Davis, Andy Cedilnik, Sebastien Barre, Bill Hoffman
* UCLA: Mike Pan, Jagadeeswaran Rajendiran
* UCSD: Neil Jones, Jeffrey Grethe, Mark Ellisman
* BWH: Nicole Aucoin, Katie Hayes, Wendy Plesniak, Mike Halle, Gordon Kindlmann, Raul San Jose Estepar, Haiying Liu, Ron Kikinis
* MIT: Lauren O'Donnell, Kilian Pohl

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#NA-MIC_Kit NA-MIC Kit Projects].

== 3. Highlights (Will Schroeder)==
The third year of the NAMIC project saw continued development and dissemination of medical image analysis software. With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NAMIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NAMIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

=== 3.1 Advanced Algorithms ===
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NAMIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NAMIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NAMIC Sandbox in preparation for formal acceptance into the NAMIC Kit.

=== 3.2 Technology Deployment Platform: Slicer3 ===
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NAMIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NAMIC Kit, and are now available to all other modules built on the Slicer3 framework.

=== 3.3 Outreach and Technology Transfer ===
Cores 4-5-6 continue to support, train and dissemniate to the NAMIC community, and the broader biomedical computing community.
* NAMIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NAMIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair.

== 4. Impact and Value to Biocomputing (Jim Miller)==

In NA-MIC's third year, it is evident that NA-MIC is developing a
culture, environment, and resources to foster and incite collaborative
research in medical image analysis that draws together mathematicians,
computer scientists, software engineers, and clinical
researchers. These artefacts of NA-MIC impact how NA-MIC
operates, make NA-MIC a fulcrum for NIH funded research,
and draws new collaborators from across the country
and around the world to NA-MIC.

=== 4.1 Impact within the Center ===

Within the center, the NA-MIC organization, NA-MIC processes, and the
NA-MIC calendar has permeated the research. The organization is
nimble, forming ad hoc distributed teams within and between cores to
address specific biocomputing tasks. Information is shared freely on
the NA-MIC Wiki, on the weekly Engineering telephone conferences, and
in the NA-MIC Subversion source code repository. The software
engineering tools of CMake, Dart 2 and CTest, CPack, and KWWidgets
facilitate a cross platform software environment for medical image
analysis that be easily built, tested, and distributed to
end-users. Core 2 has provided a platform, Slicer 3, that allows Core
1 to easily integrate new technology and deliver this technology in an
end user application to Core 3. Core 1 has developed a host of
techniques to apply to structural and diffusion analysis which are
under evaluation by Core 3. Major NA-MIC events, such as the annual
All Hands Meeting, the Summer Project Week, the Spring Algorithms
meeting, and Engineering Teleconferences are avidly attended by NA-MIC
researchers as opportunities to foster collaborations.

=== 4.2 Impact within NIH Funded Research ===

Within NIH funded research, NA-MIC continues to forge relationships
with other large NIH funded projects such as BIRN, caBIG, NAC, and
IGT. Here, we are sharing the NA-MIC culture, engineering practices,
and tools. BIRN hosts data for the NA-MIC researchers and NA-MIC hosts
BIRN wikis. caBIG lists the 3D Slicer among the applications available
on the National Cancer Imaging Archive. NAC and IGT use the NA-MIC
infrastructure and are involved in the development of the 3D
Slicer. BIRN recently held an event modeled after the NA-MIC Project
Week. NA-MIC has become a resource on open source licensing to the medical image analysis community.

NA-MIC is also attracting NIH funded collaborations. Two grants have
been funded under PAR-05-063 to collaborate with NA-MIC: ''Automated FE Mesh Development'' and ''Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI''. Five additional
applications to collaborate with NA-MIC via the NCBC collaborative
grant mechanism are under consideration. Additional grant applications
submitted under other calls are planning to use and extend the NA-MIC tools.

=== 4.3 National and International Impact ===

NA-MIC events and tools garner national and international interest.
There were nearly 100 participants at the NA-MIC All Hands Meeting in
January 2007, with many of these participants from outside of NA-MIC.
Several researchers from outside the NA-MIC community have attended
the Summer Project Weeks and the Winter Project Half-Weeks to gain
access to the NA-MIC tools and people. These external researchers are
contributing ideas and technology back into NA-MIC.

Components of the NA-MIC kit are used globally. The software
engineering tools of CMake, Dart 2 and CTest are used by many open
source projects and commercial applications. For example, the K Desktop Environment (KDE) for
Linux and Unix workstations uses CMake and Dart. KDE is one of the
largest open source projects in the world. Many open source projects and
commercial products are benefiting from the NA-MIC related
contributions to ITK and VTK. Finally, Slicer 3 is being used as an
image analysis platform in several fields outside of medical image
analysis, in particular, biological image analysis, astronomy, and industrial inspection.

NA-MIC co-sponsored the ''Workshop on Open Science'' at the Medical
Image Computing and Computer-Assisted Intervention (MICCAI) 2006
conference. The proceedings of the workshop are published on the
electronic Insight Journal, another NIH-funded activity.

Over 50 NA-MIC related publications have been produced since the inception of the center.

== 5.NA-MIC Timeline (Ross Whitaker)==

This section provides a table of NAMIC timelines from the original proposal that graphically depicts completed tasks/goals in years 1, 2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines have also been described.

[[2007_Scientific_Report_Timeline|2007 Scientific Report Timeline]]

== Appendix A Publications ==

http://www.na-mic.org/Wiki/index.php/Publications

== Appendix B EAB Report ==

The NA-MIC External Advisory Board (EAB), chaired by Prof. Chris Johnson of the University of Utah, met at the annual All-Hands Meeting. After individual presentations by NA-MIC investigators and open as well as closed-door EAB discussion, the Board provided its independent expert assessment of the Center (Appendix 2).

= Logistics =

== Schedule and process for preparation of this report ==

* March 30 - Assign section/theme leads (Ron). Last year: introduction (marty), structural analysis (allen, martin), dti (guido, marty/marek), fmri (polina, andy), namic kit(bill), timeline (ross), highlights (will), impact (bill).
*April 7,10 - tcons with Marty, Ross, Will, Tina to finalize the layout, process, and timeline of report.
* April 13 - update projects list using last year's [[NA-MIC_Collaborations]] and [[2007_Project_Half_Week#Projects|projects pursued at the half week in SLC]]. Remind investigators to update individual pages. (Tina)
* April 23- complete project description pages in updated list: [[NA-MIC_Collaborations]] (all project owners).
* April 30 - complete section summaries, introduction, highlights, impact, timeline (owners of these topics)
* May 3 - submit wiki report to NA-MIC editor, Ann (Tina)
* May 17 - submit Edited report to Rachana (Ann)
* May 31 - ship final package to NIH (Rachana)

== Guidelines from NIH Program Officer ==

The following guidelines were provided by Grace Peng, NA-MIC program officer, in Feb 2006.

The key is to synthesize all the individual elements into bigger picture stories that really speak of each area’s impact to the community.

The specialized scientific report should have the following format:

# Introductory page describing the new grouping of NAMIC project themes.
# A description of progress in each NAMIC project theme (not to exceed 2 pages each), tying together relevant activities from participating subcomponents and referencing cores in parentheses.
# A table of NAMIC timelines (from original proposal), graphically depicting completed tasks/goals in years 1,2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines should be described.
# A description of 3 highlights selected from all NAMIC projects to showcase NAMIC.
# A discussion of NAMIC’s impact and value to the biocomputing community this year.

2007 Annual Scientific Report

2007-05-05T14:16:18Z

Shenton: /* 1. Introduction (Marty Shenton) */

Back to [[2007_Progress_Report]]

For reference:

*[[Media:2006_Submitted_NA-MIC_Scientific_Report.pdf|2006 Annual Scientific Report]], [[Media:2006_APR_NIH_Questions_and_Answers.pdf|2006 Followup Questions and Answers]].
*[[Media:2005_NAMIC_Specialized_ProgReport.pdf|2005 Annual Scientific Report]]

== 1. Introduction (Marty Shenton)==

The National Alliance for Medical Imaging Computing (NA-MIC) is now in its third year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The driving biological projects (DBPs) come from schizophrenia, although the methods and tools developed are clearly applicable to many other diseases.

In the first year of this endeavor, our main focus was to develop alliances among the many cores to increase awareness of the kinds of tools needed for specific imaging applications. Our first annual report and all-hands meeting reflected this emphasis on cores, which was necessary to bring together members of an interdisciplinary team of scientists with such diverse expertise and interests. In the second year of the center our emphasis shifted from the integration of cores to the identification of themes that cut across cores and are driven by the requirements of the DBPs. We saw this shift as a natural evolution, given that the development and application of computational tools became more closely aligned with specific clinical applications. This change in emphasis was reflected in the Center's four main themes, which included Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. In the third year of the center, collaborative efforts have continued along each of these themes among computer scientists, clinical core counterparts, and engineering partners. We are thus quite pleased with the focus on themes, and we also note that our progress has not only continued but that more projects have come to fruition with espect to publications and presentations from NA-MIC investigators, which are listed on our publications page.

Below, in the next section (Section 2) we summarize our progress over the last year using the same four central themes to organize the progress report. These four themes include: Diffusion Image analysis (Section 2.1), Structural analysis (Section 2.2), Functional MRI analysis (Section 2.3), and the NA-MIC toolkit (Section 2.4). Section 3 highlights four important accomplishments of the third year: advanced algorithm development in Shape and DTI analysis, the newly architected open source application platform, Slicer 3, and our outreach and technology transfer efforts. Section 4 summarizes the impact and value of our work to the biocomputing community at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 5, provides a timeline of Center activities.

== 2. Four Main Themes ==

This year's activities focus on four main themes: Diffusion Image Analysis, Structural Analysis, Functional MRI Analysis, and the NA-MIC Kit. Each of the following sections begins with an overview of the theme, provides a progress update and list of key investigators, and concludes with a set of links to additional information for individual projects in that theme.

These thematic activities involve scientists from each of the 7 NA-MIC cores (Appendix).

* [http://www.na-mic.org/Wiki/index.php/Algorithm:Main Core 1 Algorithms]-Ross Whitaker PI
* [http://www.na-mic.org/Wiki/index.php/Engineering:Main Core 2 Engineering]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/DBP:Main Core 3 Driving Biological Problems] DBP1-Martha Shenton PI / DBP2-Andy Saykin PI / DBP3-Steven Potkin PI
* [http://www.na-mic.org/Wiki/index.php/Service:Main Core 4 Service]-Will Schroeder PI
* [http://www.na-mic.org/Wiki/index.php/Training:Main Core 5 Training]-Randy Gollub PI
* [http://www.na-mic.org/Wiki/index.php/Dissemination:Main Core 6 Dissemination]-Tina Kapur Co-PI; Steve Pieper Co-PI
* [http://www.na-mic.org/Wiki/index.php/Leadership:Main Core 7 Leadership]-Ron Kikinis

=== 2.1 Diffusion Image Analysis Theme (Marek Kubicki, Guido Gerig)===

==== Progress ====
Over the past year, we continued developing tools relevant to diffusion tensor estimation, fiber tractography and geometric and statistical diffusion tensor analysis. These tools have been already integrated into diffusion dedicated software (i.e., Fiber Viewer-UNC). In addition, we started developing tools for group data analysis, such as white matter atlases, and tools for data integration, that would combine and interfere between different imaging modalities (such as structural, fMRI and DTI), to better estimate anatomical and functional connectivity. This software is continued to be used in multiple clinical projects involving several psychiatric populations, below we provide detailed progress in the area of diffusion image analysis.

==== Fiber Tract Extraction and Analysis ====
* Since last year, when all algorithms for fiber tractography and anisotropy estimation have been implemented in both “Fiber viewer” and “Slicer” packages, methods have been applied to clinical studies. Fiber Tract integrity has been investigated and results presented at the international conferences for four fiber tracts in schizophrenia (corpus callosum, fornix, inferior occipito-frontal fasciculus and uncinate fasciculus have been extracted, and fractional anisotropy compared between groups, demonstrating fronto-temporal connectivity abnormalities in schizophrenia).

* In addition to clinical studies, teams have been working on other methods to define and estimate brain connectivity. The most important developments in this regard included volumentric connectivity and stochastic tractography measures. For volumentric connectivity, a PDE-based approach to white matter connectivity from DTI has been developed, that is founded on the principal of minimal paths through the tensor volume. Method computes a volumetric representation of a white matter tract given two endpoint regions. In addition, statistical methods for quantifying the full tensor data along these pathways have been also developed. Second approach- stochastic tractography, has been developed to calculate probability of two regions being connected by the fiber tract. This should be especialy usefull when tract has to go through the regions characterized by low diffusion anisotropy.

* First attempts have been made to population based analysis of DT-MRI. Developed method is based on unbiased non-rigid registration of a population to a common coordinate system. The registration jointly produces an average DTI atlas, which is unbiased with respect to the choice of a template image, along with diffeomorphic correspondence between each image. The anatomically significant correspondence provides a basis for comparison of tensor features and fiber tract geometry in clinical studies.

==== Fractional Anisotropy Analysis ====
* We have used tools developed last year, and applied them to our population of chronic schizophrenia subjects in order to investigate two fiber tracts- cingulum bundle nd corpus callosum. In the case of cingulum bundle, manually drawn regions of interest and Finsler geometry were used to extract entire cingulum bundle fiber tract, and FA was estimated along the tract, and compared between groups. Corpus Callosum cross sectional area and its probabilistic subdivisions were determined automatically from the structural MRI scans using a model based deformable contour segmentation. The structural scan was then co-registered with the DTI scan and the anatomical corpus callosum subdivisions were propagated to the associated FA map, and compared between groups, demonstrating defitient interhemispheric communication in schizophrenia.

==== Integration of fMRI and DTI, Path-of-Interest Analysis ====
* Progress has been also made in development of the tool that would successfully combine and integrate functional and anatomical information. Optimal Path Analysis method has been applied to Harvard- BWH fMRI dataset, and anatomical connections between regions active during the fMRI experiment have been extracted for each subject, and connectivity calculated and compared between groups. Tool now is being ported to Slicer3.

==== Key Investigators ====

* BWH: Martha Shenton, Marek Kubicki, Marc Niethammer, Sylvain Bouix, Jennifer Fitzsimmons, Katarina Quintis, Doug Markant, Kate Smith, Georgia Bushell, Mark Dreusicke, Carl-Fredrik Westin, Raul San Jose, Gordon Kindlmann
* MGH: Bruce Fischl, Denis Jen, David Kennedy
* MIT: Lauren O'Donnell, Polina Golland, Tri Ngo
* UCI: James Fallon, Martina Panzenboeck
* UNC: Guido Gerig, Isabelle Corouge, Casey Goodlett, Martin Styner
* Utah: Tom Fletcher, Ross Whitaker, Saurav Basu, Davis McKay
* GA Tech: Eric Pichon, John Melonakos, Xavier LaFaucheur, Vandana Mohan, Allen Tannenbaum
* Dartmouth: John West, Andrew Saykin, Laura Flashman, Paul Wang, Heather Pixley, Robert Roth
* Isomics: Steve Pieper
* Kitware: Luis Ibanez

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Diffusion_Image_Analysis NA-MIC Projects on Diffusion Image Analysis].

=== 2.2 Structural Analysis Theme (Allen Tannenbaum, Martin Styner)===

==== Progress ====

Within the NAMIC's structural analysis theme, the main topics of interest are structural segmentation, registration and shape analysis. These topics are of course intertwined, e.g. in that segmentation or registration can directly deliver structural correspondence used in shape analysis, or in turn shape modeling is necessary for good structural segmentations. Here is a selection of progress highlights in the structural analysis theme

====='''Segmentation'''=====

* Wavelet based structural segmentation: We have developed a spherical wavelet based framework for the segmentation of selected brain structures, such as the hippocampus or the caudate nucleus. An automated segmentation of such structures must be highly accurate and include high frequency variations in the surface. Since shape representation is a key component of the segmentation, it must be rich enough to express shape variations at various frequency levels, from low harmonics to sharp edges. Medical object segmentation with deformable models and statistical shape modeling may be combined to obtain a more robust and accurate segmentation. To address this issue, a decomposable spherical wavelet based shape representation targeted to the population seems natural, where the shape parameters are separated into groups that describe independent global and/or local biological variations in the population, and a prior induced over each group explicitly encodes these variations. This work presents three novel contributions for shape representation, multiscale prior probability estimation and segmentation.

* Tissue and structural segmentation via EM Segmenter: Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases segmentation is mostly performed manually using prior knowledge of the shape and relative location of the underlying structures combined with partially discernible boundaries. We have developed an automated approach guided by covariant shape deformations of neighboring structures, which is an additional source of prior knowledge. Captured by a shape atlas, these deformations are transformed into a statistical model using the logistic function. The mapping between atlas and image space, structure boundaries, anatomical labels, and image inhomogeneities are estimated simultaneously within an Expectation-Maximization formulation of the Maximum A posteriori Probability (MAP) estimation problem. These results are then fed into an Active Mean Field approach, which views the results as priors to a Mean Field approximation with a curve length prior. This segmentation framework has been ported into the NAMIC-toolkit and a first version of the tool is distributed with Slicer 3.

====='''Shape Analysis'''=====

* Wavelet based shape analysis: A shape representation that encodes variations at multiple scales can be useful as a rich feature set for shape analysis and classification. Combining tools from the existing shape analysis toolset, we extended it to include spherical wavelet coefficients (SWC) as features and compared the results obtained to shape analysis using a SPHARM-PDM representation. The rich SWC feature set allows the differentiation of shape differences at various scales as well as highly correlates with the existing SPHARM-PDM analysis but with increased statistical sensitivity.
* Curvature based population correspondence: We have extended the Minimum Description Length population based correspondence framework to include curvature based measurements, such as the Koenderink Shape Index S and Curvedness C in combination with the standard location information. Current methodology in population based correspondence is based mainly on minimizing distribution properties of surface point locations and are thus not invariant to alignment. We have favorably compared our combined "Curvature + Location" MDL to the standard MDL, as well as to the SPHARM approach, in complex structures, such as the striatal brain structure (composed of caudate, nucleus accumbens and putamen).
* Shape Analysis Toolset: A considerable amount of work was spent on the development aspect of the shape analysis tools. The distributed set of tools is continuously enhanced and the population based correspondence framework has been released as open source. All the tools including the visualization tool, KWMeshVisu, can be called directly from Slicer 3. The visualization tool allows the overlay of scalar, vector and ellipsoid data onto surfaces via versatile colormaps. The attribured surfaces are then visible within Slicer 3. This lean visualization tool fills a niche and is also used in our cortical thickness analysis tool. Also, while it is entirely possible to run all shape analysis steps by calling the individual modules, this is highly inefficient in a larger study. As a result we are developing a separate shape pipeline Slicer module that uses Batchmake to run the shape analysis pipeline as a distributed, background process. The whole shape analysis pipeline will thus become entirely encapsulated and accessible to the trained clinical collaborator.
* Particle based correspondence: We have developed a method for a multi-object correspondence optimization, and have applied it successfully to a proof-of-concept application for the analysis of brain structure complexes from a longitudinal study of pediatric autism. This new method for constructing compact statistical point-based models of ensembles of similar shapes does not rely on any specific surface parameterization, requires little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including non-manifold surfaces and objects of arbitrary topology. The method uses a dynamic particle system to optimize correspondence point positions across all structures by simultaneously maximizing both the geometric accuracy and the statistical simplicity of the model.

==== Key Investigators ====

* MIT: Kilian Pohl, Sandy Wells, Eric Grimson
* UNC: Martin Styner, Ipek Oguz, Guido Gerig, Xavier Barbero
* Utah: Ross Whitaker, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer
* GaTech: Allen Tannenbaum, John Melonakos, Tauseef ur Rehman, Shawn Lankton, Ramsey Al-Hakim, Eric Pichon, Delphine Nain, Oleg Michailovich, Yogesh Rathi, James Malcolm
* Isomics: Steve Pieper
* GE: Bill Lorensen, Jim Miller
* Kitware: Luis Ibanez, Karthik Krishnan,
* UCLA: Michael J. Pan, Jagadeeswaran Rajendiran
* BWH: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt
* Dartmouth: Andrew Saykin
* UCI: James Fallon

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#Structural_Image_Analysis NA-MIC Projects on Structural Image Analysis].

=== 2.3 Functional MRI Analysis Theme (Polina Golland, Andy Saykin)===

====Progress====

During this year, the focus of the algorithms and the engineering
cores has been on the structural and DTI analysis. While we continued
to expand the methods and the infrastructure in NAMIC-kit to support
fMRI analysis, as well as using the analysis tools to perform clinical
studies, the emphasis of the work this year has been on integrating
the fMRI analysis with other modalities and supporting other
modalities.

===== Clinical Studies =====

We would like to highlight several clinical studies within NAMIC that
focused on fMRI data and its relationship with other imaging
modalities:

* Imaging Phenotypes in Schizophrenics and Controls: Functional connectivity of the DLPFC by genotype was investigated using partial least squares (PLS) correlation analysis. PLS is a multivariate analytical technique used to summarize large neuroimaging data sets in such a way as to correlate patterns of activation with a variable(s) of interest (i.e., DLPFC activity). In the most recent analysis, the DRD1 genotype was used as a grouping variable. This analysis has been submitted for publication (Tura, Turner, Fallon, Kennedy, and Potkin. Genetic Impact on Functional Connectivity in Schizophrenics During a Working Memory Task). Working memory performance did not differ significantly between the two cohorts. However, imaging-genetic analysis showed a significant difference (P< 0.05) between the circuitry engaged by each group. Significance and reliability of the resulting imaging-behavioral patterns within each genotype were assessed by 200 bootstrap and 500 permutation tests, respectively. In one group, the circuitry included the temporal pole, the insula, the dorsolateral prefrontal cortex, and the Brodmann Areas (BA) 1,2,3,4,6,11 and 21, while the other group showed a network comprising the tectum, precuneus retroplenial, vermis, substantia nigra, BA 22,39,8, and 9. The DRD1 polymorphic site may characterize circuitry differences in schizophrenic patients.

*Path-Of-Interest Analysis (joint DTI/fMRI modeling): We collected preliminary data using an application of the “optimal path analysis”. In this analysis, we extracted group fMRI activation due to the Stroop effect (attentional experiment where incongruent color, in which the word is written, competes with name of the color itself, activating areas responding to conflict monitoring and selection) separately for controls and schizophrenics. This resulted in three clusters of activation, one in the right Dorsolateral Prefrontal Cortex, a second in the Anterior Cingulate Gyrus, and a third in the Medial Parietal Lobe. In the next step, we placed activation clusters in each individual space, by reversing normalization parameters used during fMRI analysis. Finally, EPI fMRI scans were co-registered to DTI scans, and the same registration parameters were applied to activation maps (fMRI results). Regions of activation were used as start and destination points for optimal path analysis, which resulted in three separate paths of optimal connectivity for each subject. The probability of the connections were then calculated for each path and each subject, and compared between groups. In our preliminary analysis we included 10 control subjects and 10 chronic schizophrenics. Our results demonstrated a relationship between Stroop Effect fMRI activation in the medial parietal area and optimal path connectivity between parietal and cingulate activation sites in schizophrenics (rho=-0.56; p=0.047), which was not observed in controls. These findings suggest that decreased connectivity may result in schizophrenics relying more on posterior parts of the executive attentional network during performance of the Stroop task.

*Hippocampal and frontal memory circuitry abnormalities in schizophrenia: Relation of diffusion, morphometric and fMRI markers: We performed a combined DTI, fMRI, and morphometric study on 13 patients with schizophrenia (SZ) and 14 HC. We identified areas of increased trace diffusivity (TD) in the hippocampal and insular regions as well as areas of reduced fractional anisotropy (FA) in left frontal white matter in SZ relative to HC (p<.01). Voxel based morphometry analyses in a subset of these subjects showed corresponding reductions in gray matter density in hippocampal and insular regions in patients relative to controls (p<.01). Analysis of fMRI results from the novel vs. repeated word contrast from the event-related auditory verbal episodic memory encoding/retrieval task in a subset of the subjects indicated reduced activation in frontal and temporal regions, as well as increased activation in posterior cingulate, retrosplenial, and thalamic regions in SZ relative to HC (p<.05). Further analysis showed that left frontal white matter FA was associated with activation in the left and right hippocampi as well as other frontal and temporal regions, but inversely related to activation in the retrosplenial/posterior cingulate region (p<.05). These initial findings indicate a pattern of relationships between of structural and functional brain abnormalities in schizophrenia and demonstrate the feasibility of integrated quantitative analyses across modalities. Publications: West JD, Saykin AJ, Roth RM, Flashman LA, Koven N, Pendergrass JC, Arfanakis K. Hippocampal and frontal memory circuitry abnormalities in schizophrenia: Relation of diffusion, morphometric and fMRI markers. 13th Annual Meeting of the Organization for Human Brain Mapping, Chicago, IL, USA, June, 2007. Journal paper in preparation.

===== Methods =====

During this year, we continued methodological development along two
directions:

*Improving fMRI detectors by incorporating Markov priors on the activation state. We integrated the improved detector into Slicer and performed substantial validation of the methods using fBIRN data provided by the UC Irvine group. Journal paper on the method has been submitted to IEEE TMI.

*Improving registration of EPI images to anatomical scans through modeling of the EPI distortions. We demonstrated an initial model that uses segmentation of the structural scan to predict the distortions in the EPI images. The preliminary results are quite encouraging.

==== Key Investigators ====

* MIT: Polina Golland, Sandy Wells, Wanmei Ou, Claire Poynton
* BWH: Martha Shenton, Marek Kubicki
* Dartmouth: Andy Saykin
* UCI: Jessica Turner, Stephen Potkin
* Toronto: Jim Kennedy

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#fMRI_Analysis NA-MIC Projects on fMRI Analysis].

=== 2.4 [[NA-MIC-Kit|NA-MIC Kit]] Theme (Will Schroeder)===

==== Progress ====
The continuing vision of the NA-MIC Kit is to provide an open source set of software tools and methodologies that will serve as the foundation for medical image computing projects for both academic and commercial use. Key elements of this vision are:

# ''Unrestrictive License.'' Users of the Kit are free to distribute their derived works under any license suitable to their needs.
# ''Cross Platform.'' This software set can be adapted to the best available price-performance computer systems for any particular use.
# ''Extensible Application Framework.'' New techniques and algorithms can be quickly integrated into a working system. Sophisticated user interfaces can be generated easily through automated processes. Sophisticated toolsets such as ITK, VTK, and KWWidgets are available to create and deploy applications quickly.
# ''Quality Software Process.'' Developers and users can rely on accurate and well documented behavior from all the parts of the Kit.
# ''Sustainable Community.'' Users are actively involved in the design process of the Kit. Documentation, training materials, and hands-on sessions are available and well publicized to the community.

====='''Slicer3'''=====

A major focus of the third year was the implementation of the Slicer3 in the NAMIC Kit. This effort addressed Item #3 above ''Extensible Application Framework.'' The previous two years of the NAMIC project, which entailed gathering requirements from Cores 1 and 3, and developing the computational foundation, toolsets, and software process, came together in the Slicer3 application platform.

Core 2 worked hard to insure that the Slicer 3 application serves, and will continue to serve, as a productive technology deployment platform. The application framework was designed carefully to provide ease-of-use, both in terms of interaction and software integration. Advanced capabilities, including the ability to launch large-scale grid computing, was designed into the application. Some of the key features of the Slicer3 application completed in the third year include the following.
* Advanced application framework including a tuned GUI for ease of use, undo/redo capabilities, 2D/3D view windows, and support for advanced interaction techniques such as 3D widgets. The application provides viewers for displaying slices, volumes, and models including the ability to edit properties. A built in transformation pipeline enables users to confidently import, edit and display data in a consistent coordinate system.
* The application is data-driven based on the next generation MRML scene description file format. Backward compatibility to Slicer 2.x MRML files is preserved.
* A module plug-in architecture and execution model that enables researchers to package and integrate their software into the Slicer3 framework. Plug-in modules can be implemented in a variety of programming languages, and are described using a simple XML description. These modules, when located and loaded into Slicer3, have the capability to automatically generate their user interface, which is seamlessly integrated into the Slicer3 GUI.
* Support for editing and marking data including support for fudicials, paint and draw editors.
* The creation of several simple plug-in modules, including the conversion of previous Slicer 2.x modules to the new Slicer3 architecture.

====='''EM Segment'''=====

As an application framework, Slicer3 provides tools for loading, viewing, measuring, editing, and saving data. To support advanced medical image analysis, plug-in modules are required in conjunction with the Slicer3 core. To demonstrate the capabilities of the framework we implemented the [http://www.na-mic.org/Wiki/index.php/Slicer3:EM EM Segment] module, a sophisticated and proven method for automatically segmenting complex anatomical structures.

To use this module, users specify parameters defining the image protocol and the anatomical structures of interests. This process results in a template that the module uses to automatically segment large data sets. The template is composed of atlas data and a non-trivial collection of parameters for the EM Segment algorithm ([[Media:Pohl-miccai-short-2005.pdf|Pohl et al.]]). Once the parameters are specified, the target images are segmented using the algorithm; if the results are satisfactory, the template is saved and can be used later to segment new images (via the GUI or batch processing). If the results are unsatisfactory, the parameters can be modified and the segmentation re-run.

Besides successfully demonstrating the use of complex algorithms in the Slicer3 framework, this effort also led us to develop tools, including modifications to the underlying KWWidgets GUI toolkit, to support module workflow. With these tools, it is possible to simplify complex modules by dividing the complicated template specification task into a number of smaller, intuitive steps. These steps are enforced by the GUI and reduce the potential for user error, while improving the overall user interface.

====='''Quality Software Process'''=====

Building on [http://lwn.net/Articles/188693/ last year's success with the KDE community], the NAMIC community continued to extend its world-class, open source software process tools CMake, DART, CTest and DART. These tools, which form the core of a quality-oriented, test-driven development (TDD) software process. In particular, the CPack system is now able to automatically package and distibute code, libraries, executables and installers across all of NAMIC's supported platforms (i.e., Linux, Windows, Mac). This enables the NAMIC developer community to rapidly deploy software tools to the user community.

==== Key Investigators ====

* GE: Bill Lorensen, Jim Miller, Xiaodong Tao, Dan Blezek
* Isomics: Steve Pieper, Alex Yarmarkovich
* Kitware: Will Schroeder, Luis Ibanez, Brad Davis, Andy Cedilnik, Sebastien Barre, Bill Hoffman
* UCLA: Mike Pan, Jagadeeswaran Rajendiran
* UCSD: Neil Jones, Jeffrey Grethe, Mark Ellisman
* BWH: Nicole Aucoin, Katie Hayes, Wendy Plesniak, Mike Halle, Gordon Kindlmann, Raul San Jose Estepar, Haiying Liu, Ron Kikinis
* MIT: Lauren O'Donnell, Kilian Pohl

==== Additional Information ====

For details of each of the projects in this theme, please see [http://www.na-mic.org/Wiki/index.php/NA-MIC_Collaborations#NA-MIC_Kit NA-MIC Kit Projects].

== 3. Highlights (Will Schroeder)==
The third year of the NAMIC project saw continued development and dissemination of medical image analysis software. With the release of the first version of Slicer3, the transfer of this technology is accelerating. Because of NAMIC's strong ties with several large open source communities, such as ITK, VTK, and CMake, NAMIC continues to make significant impact on the nation's broader biocomputing infrastructure. The following are just a few of the many highlights from the third year of the NAMIC effort.

=== 3.1 Advanced Algorithms ===
Core 1 continues to lead the biomedical community in DTI and shape analysis.
* NAMIC published an open source framework for shape analysis, including providing access to the open source software repository. Shape analysis has become of increasing relevance to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. The software has been downloaded many times since the first online publication in October 2006, and is now used by several prestigious image analysis groups.
* The spherical based wavelet shape analysis package has been contributed into ITK, and in the next few months the multiscale segmentation work will be incorporated as well.
* The NAMIC community has implemented a very fast method for the optimal transport approach to elastic image registration which is currently being added to ITK.
* The conformal flattening algorithm has been implemented as an ITK filter and is in the NAMIC Sandbox in preparation for formal acceptance into the NAMIC Kit.

=== 3.2 Technology Deployment Platform: Slicer3 ===
Core 2 in conjunction with Algorithms (Core 1) and DBP (Core 3) are creating new tools to accelerate the transition of technology to the biomedical imaging community.
* One of the year's major achievements was the release of the first viable version of Slicer3 application, which evolved from concept to a full-featured application. The second beta version of Slicer3 was released in April 2007. The application provides a full range of functionality for loading, viewing, editing, and saving models, volumes, transforms, fiducials and other common medical data types. Slicer3 also includes a powerful execution model that enables Core 1 developers (and other in the NAMIC community) to easily deploy algorithms to Core 2 and other biocomputing clients.

* Slicer3's execution model supports plug-in modules. These modules can be run stand alone or integrated into the Slicer3 framework. When integrated, the GUI to the module can be automatically generated from an associated XML file describing input parameters to the module. A variety of modules were created, ranging from simple image processing algorithms, to complex, multi-step segmentation procedures.

* To stress test Slicer3's architecture and demonstrate its capabilities, the EM Segment module (http://wiki.na-mic.org/Wiki/index.php/Slicer3:EM) was created and added to Slicer's library of modules. EM Segment is an automatic segmentation algorithm for medical images and represents a collaborative effort between the NAMIC engineering, algorithms, and biological problem cores. The EM Segment module enables users to quickly configure the algorithm to a variety of imaging protocols as well as anatomical structures through a wizard-style, workflow interface. The workflow tools have been integrated into the NAMIC Kit, and are now available to all other modules built on the Slicer3 framework.

=== 3.3 Outreach and Technology Transfer ===
Cores 4-5-6 continue to support, train and dissemniate to the NAMIC community, and the broader biomedical computing community.
* NAMIC continues to practice the best of collaborative science through its bi-annual Project Week events. These events, which gather key representatives from Cores 1-7 and external collaborators, are organized to gather experts from a variety of domains to address current research problems. [http://www.na-mic.org/Wiki/index.php/2007_Project_Half_Week This year's first Project Week] was held in January and hosted by the University of Utah. It saw several significant accomplishments including the first beta release of the next generation Slicer3 computing platform. [http://www.na-mic.org/Wiki/index.php/2007_Programming/Project_Week_MIT The second Project Week is scheduled for June in Boston, MA].
* Twelve NAMIC-supported papers were published in high-quality peer reviewed conference proceedings (four papers in MICCAI alone). Another paper on the NAMIC software process was published in ''IEEE Software.'' All three DTI papers presented at MICCAI last year were NAMIC associated.
* Several workshops through the year were held at various institutions. This includes the DTI workshop at UNC, the MICCAI Open Source Workshop, and the NA-MIC Training Workshop at the Harvard Center for Neurodegeneration and Repair.

== 4. Impact and Value to Biocomputing (Jim Miller)==

In NA-MIC's third year, it is evident that NA-MIC is developing a
culture, environment, and resources to foster and incite collaborative
research in medical image analysis that draws together mathematicians,
computer scientists, software engineers, and clinical
researchers. These artefacts of NA-MIC impact how NA-MIC
operates, make NA-MIC a fulcrum for NIH funded research,
and draws new collaborators from across the country
and around the world to NA-MIC.

=== 4.1 Impact within the Center ===

Within the center, the NA-MIC organization, NA-MIC processes, and the
NA-MIC calendar has permeated the research. The organization is
nimble, forming ad hoc distributed teams within and between cores to
address specific biocomputing tasks. Information is shared freely on
the NA-MIC Wiki, on the weekly Engineering telephone conferences, and
in the NA-MIC Subversion source code repository. The software
engineering tools of CMake, Dart 2 and CTest, CPack, and KWWidgets
facilitate a cross platform software environment for medical image
analysis that be easily built, tested, and distributed to
end-users. Core 2 has provided a platform, Slicer 3, that allows Core
1 to easily integrate new technology and deliver this technology in an
end user application to Core 3. Core 1 has developed a host of
techniques to apply to structural and diffusion analysis which are
under evaluation by Core 3. Major NA-MIC events, such as the annual
All Hands Meeting, the Summer Project Week, the Spring Algorithms
meeting, and Engineering Teleconferences are avidly attended by NA-MIC
researchers as opportunities to foster collaborations.

=== 4.2 Impact within NIH Funded Research ===

Within NIH funded research, NA-MIC continues to forge relationships
with other large NIH funded projects such as BIRN, caBIG, NAC, and
IGT. Here, we are sharing the NA-MIC culture, engineering practices,
and tools. BIRN hosts data for the NA-MIC researchers and NA-MIC hosts
BIRN wikis. caBIG lists the 3D Slicer among the applications available
on the National Cancer Imaging Archive. NAC and IGT use the NA-MIC
infrastructure and are involved in the development of the 3D
Slicer. BIRN recently held an event modeled after the NA-MIC Project
Week. NA-MIC has become a resource on open source licensing to the medical image analysis community.

NA-MIC is also attracting NIH funded collaborations. Two grants have
been funded under PAR-05-063 to collaborate with NA-MIC: ''Automated FE Mesh Development'' and ''Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI''. Five additional
applications to collaborate with NA-MIC via the NCBC collaborative
grant mechanism are under consideration. Additional grant applications
submitted under other calls are planning to use and extend the NA-MIC tools.

=== 4.3 National and International Impact ===

NA-MIC events and tools garner national and international interest.
There were nearly 100 participants at the NA-MIC All Hands Meeting in
January 2007, with many of these participants from outside of NA-MIC.
Several researchers from outside the NA-MIC community have attended
the Summer Project Weeks and the Winter Project Half-Weeks to gain
access to the NA-MIC tools and people. These external researchers are
contributing ideas and technology back into NA-MIC.

Components of the NA-MIC kit are used globally. The software
engineering tools of CMake, Dart 2 and CTest are used by many open
source projects and commercial applications. For example, the K Desktop Environment (KDE) for
Linux and Unix workstations uses CMake and Dart. KDE is one of the
largest open source projects in the world. Many open source projects and
commercial products are benefiting from the NA-MIC related
contributions to ITK and VTK. Finally, Slicer 3 is being used as an
image analysis platform in several fields outside of medical image
analysis, in particular, biological image analysis, astronomy, and industrial inspection.

NA-MIC co-sponsored the ''Workshop on Open Science'' at the Medical
Image Computing and Computer-Assisted Intervention (MICCAI) 2006
conference. The proceedings of the workshop are published on the
electronic Insight Journal, another NIH-funded activity.

Over 50 NA-MIC related publications have been produced since the inception of the center.

== 5.NA-MIC Timeline (Ross Whitaker)==

This section provides a table of NAMIC timelines from the original proposal that graphically depicts completed tasks/goals in years 1, 2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines have also been described.

[[2007_Scientific_Report_Timeline|2007 Scientific Report Timeline]]

== Appendix A Publications ==

http://www.na-mic.org/Wiki/index.php/Publications

== Appendix B EAB Report ==

The NA-MIC External Advisory Board (EAB), chaired by Prof. Chris Johnson of the University of Utah, met at the annual All-Hands Meeting. After individual presentations by NA-MIC investigators and open as well as closed-door EAB discussion, the Board provided its independent expert assessment of the Center (Appendix 2).

= Logistics =

== Schedule and process for preparation of this report ==

* March 30 - Assign section/theme leads (Ron). Last year: introduction (marty), structural analysis (allen, martin), dti (guido, marty/marek), fmri (polina, andy), namic kit(bill), timeline (ross), highlights (will), impact (bill).
*April 7,10 - tcons with Marty, Ross, Will, Tina to finalize the layout, process, and timeline of report.
* April 13 - update projects list using last year's [[NA-MIC_Collaborations]] and [[2007_Project_Half_Week#Projects|projects pursued at the half week in SLC]]. Remind investigators to update individual pages. (Tina)
* April 23- complete project description pages in updated list: [[NA-MIC_Collaborations]] (all project owners).
* April 30 - complete section summaries, introduction, highlights, impact, timeline (owners of these topics)
* May 3 - submit wiki report to NA-MIC editor, Ann (Tina)
* May 17 - submit Edited report to Rachana (Ann)
* May 31 - ship final package to NIH (Rachana)

== Guidelines from NIH Program Officer ==

The following guidelines were provided by Grace Peng, NA-MIC program officer, in Feb 2006.

The key is to synthesize all the individual elements into bigger picture stories that really speak of each area’s impact to the community.

The specialized scientific report should have the following format:

# Introductory page describing the new grouping of NAMIC project themes.
# A description of progress in each NAMIC project theme (not to exceed 2 pages each), tying together relevant activities from participating subcomponents and referencing cores in parentheses.
# A table of NAMIC timelines (from original proposal), graphically depicting completed tasks/goals in years 1,2, and 3 and tasks/goals to be completed in years 4-5. Changes to the original timelines should be described.
# A description of 3 highlights selected from all NAMIC projects to showcase NAMIC.
# A discussion of NAMIC’s impact and value to the biocomputing community this year.

2007 Annual Scientific Report

2007-04-21T17:07:35Z

Shenton: /* Key Investigators */

2007 Annual Scientific Report

2007-04-21T17:07:15Z

Shenton: /* Key Investigators */

NAC-Group

2007-01-15T03:29:31Z

Shenton: /* Funding */

The NAC group is a group of scientists at '''BWH''' in Boston who are doing research in neuroimaging and neuroimage analysis. This group is open to all interested participants.

=The purpose of this group is to advance our individual research agendas by=

*exchanging know-how about image acquisition and image analysis tools and capabilities
*coordinating our efforts to improve infrastructure at the institution

=Current members:=
*[http://www.spl.harvard.edu/~jolesz Ferenc Jolesz] [http://www.ncigt.org National Center for Image Guided Therapy]
*[http://pnl.bwh.harvard.edu/people/profiles/shenton.html Martha Shenton] [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~kikinis Ron Kikinis] [http://www.spl.harvard.edu Surgical Planning Laboratory]
*Jill Goldstein
*[http://www.spl.harvard.edu/pages/ppl/sw/homepage.html William Wells]
*[http://lmi.bwh.harvard.edu/~westin/ Carl Fredrik Westin] [http://lmi.bwh.harvard.edu Laboratory of Mathematics in Imaging]
*[http://cni.bwh.harvard.edu/personnel/charles.html Charles Guttmann] [http://cni.bwh.harvard.edu Center for Neurologic Imaging ]
*[http://www.spl.harvard.edu/~reisa Reisa Sperling]
*[http://golbylab.bwh.harvard.edu/people_alex.html Alex Golby] [http://golbylab.bwh.harvard.edu Golbylab]
*[http://pnl.bwh.harvard.edu/people/profiles/kubicki.html Marek Kubicki] [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~cindy Cindy Wible]
Please feel free to add yourself to this list.

=Funding=
*This group has funding from many different agencies, both federal and private.
**'''F. Jolesz'''
***MR Guided Therapy Jolesz 09/30/95-04/30/07 NCI P01CA067165 PI
***Multidisciplinary Training in Image Guided Therapy Jolesz 09/24/01-06/30/07 NCI R25CA089017 PI
**'''M. Shenton'''
***Computerized Image Analyses of MR Scans in Schizophrenia Shenton 05/01/94-08/31/10 NIH/NIMH R01 MH 50740 PI
***Supplement to Promote Reentry into Biomedical and Behavioral Research Careers Shenton 07/01/06-06/30/09 NIH/NIMH R01 MH 50740 PI
***Clinical Symptoms & Brain Abnormalities in Schizophrenia Shenton 09/16/04-08/31/09 NIH/NIMH K05 MH 70047 PI
***MR Brain Diffusion Tensor Imaging in Schizophrenia Shenton 10/01/03-09/30/08 VA Merit PI
***Biological Basis of Schizotypal Personality Disorder McCarley/Shenton 08/01/94-03/01/10 NIH/NIMH R01 MH 52807 Co-PI
***Neuroimaging Studies of Schizophrenia McCarley/Shenton 04/01/03-03/31/09 VA Research Enhancement Award Program Co-PI
***VA Center Grant in Schizophrenia McCarley/Shenton 01/01/07-12/31/12 Co-PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/07 NIH/NIBIB U54 EB005149 Shenton Site-PI
***Vulnerability to Progression in Schizophrenia McCarley / /07-/ /12 NIH/NIMH P50 MH80272 Shenton Core PI & Project PI
**'''R. Kikinis'''
***Neuroimaging Analysis Center Kikinis 09/30/98-07/31/08 NIH/NCRR P41 RR13218 PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/09 NIH/NIBIB U54 EB005149 PI
**'''Jill Goldstein'''
***Shared Fetal Antecedents to Depression and Risk to CVD Goldstein 12/01/06-11/30/11 NIMH & NHLBI R01 MH074835 PI
***Gender and Brain Abnormalities in Schizophrenia Goldstein 07/15/97-06/30/11 NIMH R01MH056956 PI
***Sex & Brain Abnormalities in Schizophrenia Phase III Goldstein 07/01/06-06/30/11 NIMH RO1MH56956 PI
***Hormones/Genes in Women's Health: From Bench to Bedside Goldstein 09/01/05-07/31/10 NICHHD K12HD051959 PI
**'''Carl-Fredrik Westin'''
***Novel DT-MRI Analyses of White Matter in Schizophrenia Westin 11/01/06-10/31/11 NIH R01 MH074794 PI
***Real-time Control of 3D-Slicer Visualization Using Coordinates from Electrophysiological Catheter Tracking System Westin 10/01/05-09/30/07 CIMIT Application Development Award PI
**'''Charles Guttmann'''
***MRI Characterization of Cortical Lesions in MS Guttmann 10/01/04-09/30/07 National Multiple Sclerosis Society PI
**'''Reisa Sperling'''
***Alzheimer’s Association Sperling 11/01/06-10/31/09 Investigator Initiated Research Grant PI
***American Federation in Aging Research Sperling 07/01/03-06/30/08 Beeson Award PI
***FMRI assessment of hippocampal response to treatment with memantine Sperling 03/01/04-06/30/07 Investigator ***Initiated-Corporate Sponsored/Forest Pharmaceuticals PI
***Evolution of memory-related fMRI activation over the course of MCI and AD Sperling 05/01/06-01/31/11 NIA R01AG027435 PI
**'''Alex Golby'''
***Brain basis of memory studied by fMRI & intercranial EEG Golby 07/01/04-06/30/09 NIDS K08NS048063 PI

=Upcoming Events=
# [[NACG:MR-Protocols| Working Session on Acquisition Protocols for Neuroresearch]] Jan 25th at 9.30-10.30 in Neurosurgery conference room on the fourth floor of Carrie Hall.

NAC-Group

2007-01-15T03:26:11Z

Shenton: /* Funding */

The NAC group is a group of scientists at '''BWH''' in Boston who are doing research in neuroimaging and neuroimage analysis. This group is open to all interested participants.

=The purpose of this group is to advance our individual research agendas by=

*exchanging know-how about image acquisition and image analysis tools and capabilities
*coordinating our efforts to improve infrastructure at the institution

=Current members:=
*[http://www.spl.harvard.edu/~jolesz Ferenc Jolesz] [http://www.ncigt.org National Center for Image Guided Therapy]
*[http://pnl.bwh.harvard.edu/people/profiles/shenton.html Martha Shenton] [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~kikinis Ron Kikinis] [http://www.spl.harvard.edu Surgical Planning Laboratory]
*Jill Goldstein
*[http://www.spl.harvard.edu/pages/ppl/sw/homepage.html William Wells]
*[http://lmi.bwh.harvard.edu/~westin/ Carl Fredrik Westin] [http://lmi.bwh.harvard.edu Laboratory of Mathematics in Imaging]
*[http://cni.bwh.harvard.edu/personnel/charles.html Charles Guttmann] [http://cni.bwh.harvard.edu Center for Neurologic Imaging ]
*[http://www.spl.harvard.edu/~reisa Reisa Sperling]
*[http://golbylab.bwh.harvard.edu/people_alex.html Alex Golby] [http://golbylab.bwh.harvard.edu Golbylab]
*[http://pnl.bwh.harvard.edu/people/profiles/kubicki.html Marek Kubicki] [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~cindy Cindy Wible]
Please feel free to add yourself to this list.

=Funding=
*This group has funding from many different agencies, both federal and private.
**'''F. Jolesz'''
***MR Guided Therapy Jolesz 09/30/95-04/30/07 NCI P01CA067165 PI
***Multidisciplinary Training in Image Guided Therapy Jolesz 09/24/01-06/30/07 NCI R25CA089017 PI
**'''M. Shenton'''
***Computerized Image Analyses of MR Scans in Schizophrenia Shenton 05/01/94-08/31/10 NIH/NIMH R01 MH 50740 PI
***Supplement to Promote Reentry into Biomedical and Behavioral Research Careers Shenton 07/01/06-06/30/09 NIH/NIMH R01 MH 50740 PI
***Clinical Symptoms & Brain Abnormalities in Schizophrenia Shenton 09/16/04-08/31/09 NIH/NIMH K05 MH 70047 PI
***MR Brain Diffusion Tensor Imaging in Schizophrenia Shenton 10/01/03-09/30/08 VA Merit PI
***Biological Basis of Schizotypal Personality Disorder McCarley/Shenton 08/01/94-03/01/10 NIH/NIMH R01 MH 52807 Co-PI
***Neuroimaging Studies of Schizophrenia McCarley/Shenton 04/01/03-03/31/09 VA Research Enhancement Award Program Co-PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/07 NIH/NIBIB U54 EB005149 Shenton Site-PI
***Vulnerability to Progression in Schizophrenia McCarley / /07-/ /12 NIH/NIMH P50 MH80272 Shenton Core PI & Project PI
**'''R. Kikinis'''
***Neuroimaging Analysis Center Kikinis 09/30/98-07/31/08 NIH/NCRR P41 RR13218 PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/09 NIH/NIBIB U54 EB005149 PI
**'''Jill Goldstein'''
***Shared Fetal Antecedents to Depression and Risk to CVD Goldstein 12/01/06-11/30/11 NIMH & NHLBI R01 MH074835 PI
***Gender and Brain Abnormalities in Schizophrenia Goldstein 07/15/97-06/30/11 NIMH R01MH056956 PI
***Sex & Brain Abnormalities in Schizophrenia Phase III Goldstein 07/01/06-06/30/11 NIMH RO1MH56956 PI
***Hormones/Genes in Women's Health: From Bench to Bedside Goldstein 09/01/05-07/31/10 NICHHD K12HD051959 PI
**'''Carl-Fredrik Westin'''
***Novel DT-MRI Analyses of White Matter in Schizophrenia Westin 11/01/06-10/31/11 NIH R01 MH074794 PI
***Real-time Control of 3D-Slicer Visualization Using Coordinates from Electrophysiological Catheter Tracking System Westin 10/01/05-09/30/07 CIMIT Application Development Award PI
**'''Charles Guttmann'''
***MRI Characterization of Cortical Lesions in MS Guttmann 10/01/04-09/30/07 National Multiple Sclerosis Society PI
**'''Reisa Sperling'''
***Alzheimer’s Association Sperling 11/01/06-10/31/09 Investigator Initiated Research Grant PI
***American Federation in Aging Research Sperling 07/01/03-06/30/08 Beeson Award PI
***FMRI assessment of hippocampal response to treatment with memantine Sperling 03/01/04-06/30/07 Investigator ***Initiated-Corporate Sponsored/Forest Pharmaceuticals PI
***Evolution of memory-related fMRI activation over the course of MCI and AD Sperling 05/01/06-01/31/11 NIA R01AG027435 PI
**'''Alex Golby'''
***Brain basis of memory studied by fMRI & intercranial EEG Golby 07/01/04-06/30/09 NIDS K08NS048063 PI

=Upcoming Events=
# [[NACG:MR-Protocols| Working Session on Acquisition Protocols for Neuroresearch]] Jan 25th at 9.30-10.30 in Neurosurgery conference room on the fourth floor of Carrie Hall.

NAC-Group

2007-01-15T03:25:35Z

Shenton: /* Funding */

The NAC group is a group of scientists at '''BWH''' in Boston who are doing research in neuroimaging and neuroimage analysis. This group is open to all interested participants.

=The purpose of this group is to advance our individual research agendas by=

*exchanging know-how about image acquisition and image analysis tools and capabilities
*coordinating our efforts to improve infrastructure at the institution

=Current members:=
*[http://www.spl.harvard.edu/~jolesz Ferenc Jolesz] [http://www.ncigt.org National Center for Image Guided Therapy]
*[http://pnl.bwh.harvard.edu/people/profiles/shenton.html Martha Shenton] [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~kikinis Ron Kikinis] [http://www.spl.harvard.edu Surgical Planning Laboratory]
*Jill Goldstein
*[http://www.spl.harvard.edu/pages/ppl/sw/homepage.html William Wells]
*[http://lmi.bwh.harvard.edu/~westin/ Carl Fredrik Westin] [http://lmi.bwh.harvard.edu Laboratory of Mathematics in Imaging]
*[http://cni.bwh.harvard.edu/personnel/charles.html Charles Guttmann] [http://cni.bwh.harvard.edu Center for Neurologic Imaging ]
*[http://www.spl.harvard.edu/~reisa Reisa Sperling]
*[http://golbylab.bwh.harvard.edu/people_alex.html Alex Golby] [http://golbylab.bwh.harvard.edu Golbylab]
*[http://pnl.bwh.harvard.edu/people/profiles/kubicki.html Marek Kubicki] [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~cindy Cindy Wible]
Please feel free to add yourself to this list.

=Funding=
*This group has funding from many different agencies, both federal and private.
**'''F. Jolesz'''
***MR Guided Therapy Jolesz 09/30/95-04/30/07 NCI P01CA067165 PI
***Multidisciplinary Training in Image Guided Therapy Jolesz 09/24/01-06/30/07 NCI R25CA089017 PI
**'''M. Shenton'''
***Computerized Image Analyses of MR Scans in Schizophrenia Shenton 05/01/94-08/31/10 NIH/NIMH R01 MH 50740 PI
***Supplement to Promote Reentry into Biomedical and Behavioral Research Careers Shenton 07/01/06-06/30/09 NIH/NIMH R01 MH 50740 PI
***Clinical Symptoms & Brain Abnormalities in Schizophrenia Shenton 09/16/04-08/31/09 NIH/NIMH K05 MH 70047 PI
***MR Brain Diffusion Tensor Imaging in Schizophrenia Shenton 10/01/03-09/30/08 VA Merit PI
***Biological Basis of Schizotypal Personality Disorder McCarley/Shenton 08/01/94-03/01/10 NIH/NIMH R01 MH 52807 Co-PI
***Neuroimaging Studies of Schizophrenia McCarley/Shenton 04/01/03-03/31/09 VA Research Enhancement Award Program Co-PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/07 NIH/NIBIB U54 EB005149 Shenton Site-PI
***Vulnerability to Progression in Schizophrenia McCarley / /07-/ /12 NIH/NIMH P50 MH80272 Shenton Core & Project PI
**'''R. Kikinis'''
***Neuroimaging Analysis Center Kikinis 09/30/98-07/31/08 NIH/NCRR P41 RR13218 PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/09 NIH/NIBIB U54 EB005149 PI
**'''Jill Goldstein'''
***Shared Fetal Antecedents to Depression and Risk to CVD Goldstein 12/01/06-11/30/11 NIMH & NHLBI R01 MH074835 PI
***Gender and Brain Abnormalities in Schizophrenia Goldstein 07/15/97-06/30/11 NIMH R01MH056956 PI
***Sex & Brain Abnormalities in Schizophrenia Phase III Goldstein 07/01/06-06/30/11 NIMH RO1MH56956 PI
***Hormones/Genes in Women's Health: From Bench to Bedside Goldstein 09/01/05-07/31/10 NICHHD K12HD051959 PI
**'''Carl-Fredrik Westin'''
***Novel DT-MRI Analyses of White Matter in Schizophrenia Westin 11/01/06-10/31/11 NIH R01 MH074794 PI
***Real-time Control of 3D-Slicer Visualization Using Coordinates from Electrophysiological Catheter Tracking System Westin 10/01/05-09/30/07 CIMIT Application Development Award PI
**'''Charles Guttmann'''
***MRI Characterization of Cortical Lesions in MS Guttmann 10/01/04-09/30/07 National Multiple Sclerosis Society PI
**'''Reisa Sperling'''
***Alzheimer’s Association Sperling 11/01/06-10/31/09 Investigator Initiated Research Grant PI
***American Federation in Aging Research Sperling 07/01/03-06/30/08 Beeson Award PI
***FMRI assessment of hippocampal response to treatment with memantine Sperling 03/01/04-06/30/07 Investigator ***Initiated-Corporate Sponsored/Forest Pharmaceuticals PI
***Evolution of memory-related fMRI activation over the course of MCI and AD Sperling 05/01/06-01/31/11 NIA R01AG027435 PI
**'''Alex Golby'''
***Brain basis of memory studied by fMRI & intercranial EEG Golby 07/01/04-06/30/09 NIDS K08NS048063 PI

=Upcoming Events=
# [[NACG:MR-Protocols| Working Session on Acquisition Protocols for Neuroresearch]] Jan 25th at 9.30-10.30 in Neurosurgery conference room on the fourth floor of Carrie Hall.

NAC-Group

2007-01-15T03:25:01Z

Shenton: /* Funding */

The NAC group is a group of scientists at '''BWH''' in Boston who are doing research in neuroimaging and neuroimage analysis. This group is open to all interested participants.

=The purpose of this group is to advance our individual research agendas by=

*exchanging know-how about image acquisition and image analysis tools and capabilities
*coordinating our efforts to improve infrastructure at the institution

=Current members:=
*[http://www.spl.harvard.edu/~jolesz Ferenc Jolesz] [http://www.ncigt.org National Center for Image Guided Therapy]
*[http://pnl.bwh.harvard.edu/people/profiles/shenton.html Martha Shenton] [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~kikinis Ron Kikinis] [http://www.spl.harvard.edu Surgical Planning Laboratory]
*Jill Goldstein
*[http://www.spl.harvard.edu/pages/ppl/sw/homepage.html William Wells]
*[http://lmi.bwh.harvard.edu/~westin/ Carl Fredrik Westin] [http://lmi.bwh.harvard.edu Laboratory of Mathematics in Imaging]
*[http://cni.bwh.harvard.edu/personnel/charles.html Charles Guttmann] [http://cni.bwh.harvard.edu Center for Neurologic Imaging ]
*[http://www.spl.harvard.edu/~reisa Reisa Sperling]
*[http://golbylab.bwh.harvard.edu/people_alex.html Alex Golby] [http://golbylab.bwh.harvard.edu Golbylab]
*[http://pnl.bwh.harvard.edu/people/profiles/kubicki.html Marek Kubicki] [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~cindy Cindy Wible]
Please feel free to add yourself to this list.

=Funding=
*This group has funding from many different agencies, both federal and private.
**'''F. Jolesz'''
***MR Guided Therapy Jolesz 09/30/95-04/30/07 NCI P01CA067165 PI
***Multidisciplinary Training in Image Guided Therapy Jolesz 09/24/01-06/30/07 NCI R25CA089017 PI
**'''M. Shenton'''
***Computerized Image Analyses of MR Scans in Schizophrenia Shenton 05/01/94-08/31/10 NIH/NIMH R01 MH 50740 PI
***Supplement to Promote Reentry into Biomedical and Behavioral Research Careers 07/01/06-06/30/09 NIH/NIMH R01 MH 50740 PI
***Clinical Symptoms & Brain Abnormalities in Schizophrenia Shenton 09/16/04-08/31/09 NIH/NIMH K05 MH 70047 PI
***MR Brain Diffusion Tensor Imaging in Schizophrenia Shenton 10/01/03-09/30/08 VA Merit PI
***Biological Basis of Schizotypal Personality Disorder McCarley/Shenton 08/01/94-03/01/10 NIH/NIMH R01 MH 52807 Co-PI
***Neuroimaging Studies of Schizophrenia McCarley/Shenton 04/01/03-03/31/09 VA Research Enhancement Award Program Co-PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/07 NIH/NIBIB U54 EB005149 Shenton Site-PI
***Vulnerability to Progression in Schizophrenia McCarley / /07-/ /12 NIH/NIMH P50 MH80272 Shenton Core & Project PI
**'''R. Kikinis'''
***Neuroimaging Analysis Center Kikinis 09/30/98-07/31/08 NIH/NCRR P41 RR13218 PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/09 NIH/NIBIB U54 EB005149 PI
**'''Jill Goldstein'''
***Shared Fetal Antecedents to Depression and Risk to CVD Goldstein 12/01/06-11/30/11 NIMH & NHLBI R01 MH074835 PI
***Gender and Brain Abnormalities in Schizophrenia Goldstein 07/15/97-06/30/11 NIMH R01MH056956 PI
***Sex & Brain Abnormalities in Schizophrenia Phase III Goldstein 07/01/06-06/30/11 NIMH RO1MH56956 PI
***Hormones/Genes in Women's Health: From Bench to Bedside Goldstein 09/01/05-07/31/10 NICHHD K12HD051959 PI
**'''Carl-Fredrik Westin'''
***Novel DT-MRI Analyses of White Matter in Schizophrenia Westin 11/01/06-10/31/11 NIH R01 MH074794 PI
***Real-time Control of 3D-Slicer Visualization Using Coordinates from Electrophysiological Catheter Tracking System Westin 10/01/05-09/30/07 CIMIT Application Development Award PI
**'''Charles Guttmann'''
***MRI Characterization of Cortical Lesions in MS Guttmann 10/01/04-09/30/07 National Multiple Sclerosis Society PI
**'''Reisa Sperling'''
***Alzheimer’s Association Sperling 11/01/06-10/31/09 Investigator Initiated Research Grant PI
***American Federation in Aging Research Sperling 07/01/03-06/30/08 Beeson Award PI
***FMRI assessment of hippocampal response to treatment with memantine Sperling 03/01/04-06/30/07 Investigator ***Initiated-Corporate Sponsored/Forest Pharmaceuticals PI
***Evolution of memory-related fMRI activation over the course of MCI and AD Sperling 05/01/06-01/31/11 NIA R01AG027435 PI
**'''Alex Golby'''
***Brain basis of memory studied by fMRI & intercranial EEG Golby 07/01/04-06/30/09 NIDS K08NS048063 PI

=Upcoming Events=
# [[NACG:MR-Protocols| Working Session on Acquisition Protocols for Neuroresearch]] Jan 25th at 9.30-10.30 in Neurosurgery conference room on the fourth floor of Carrie Hall.

NAC-Group

2007-01-15T03:22:11Z

Shenton: /* Current members: */

The NAC group is a group of scientists at '''BWH''' in Boston who are doing research in neuroimaging and neuroimage analysis. This group is open to all interested participants.

=The purpose of this group is to advance our individual research agendas by=

*exchanging know-how about image acquisition and image analysis tools and capabilities
*coordinating our efforts to improve infrastructure at the institution

=Current members:=
*[http://www.spl.harvard.edu/~jolesz Ferenc Jolesz] [http://www.ncigt.org National Center for Image Guided Therapy]
*[http://pnl.bwh.harvard.edu/people/profiles/shenton.html Martha Shenton] [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~kikinis Ron Kikinis] [http://www.spl.harvard.edu Surgical Planning Laboratory]
*Jill Goldstein
*[http://www.spl.harvard.edu/pages/ppl/sw/homepage.html William Wells]
*[http://lmi.bwh.harvard.edu/~westin/ Carl Fredrik Westin] [http://lmi.bwh.harvard.edu Laboratory of Mathematics in Imaging]
*[http://cni.bwh.harvard.edu/personnel/charles.html Charles Guttmann] [http://cni.bwh.harvard.edu Center for Neurologic Imaging ]
*[http://www.spl.harvard.edu/~reisa Reisa Sperling]
*[http://golbylab.bwh.harvard.edu/people_alex.html Alex Golby] [http://golbylab.bwh.harvard.edu Golbylab]
*[http://pnl.bwh.harvard.edu/people/profiles/kubicki.html Marek Kubicki] [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~cindy Cindy Wible]
Please feel free to add yourself to this list.

=Funding=
*This group has funding from many different agencies, both federal and private.
**'''F. Jolesz'''
***MR Guided Therapy Jolesz 09/30/95-04/30/07 NCI P01CA067165 PI
***Multidisciplinary Training in Image Guided Therapy Jolesz 09/24/01-06/30/07 NCI R25CA089017 PI
**'''M. Shenton'''
***Computerized Image Analyses of MR Scans in Schizophrenia Shenton 05/01/94-08/31/10 NIH/NIMH R01 MH 50740 PI
***Supplement to Promote Reentry into Biomedical and Behavioral Research Careers 07/01/06-06/30/09 NIH/NIMH R01 MH 50740 PI
***Clinical Symptoms & Brain Abnormalities in Schizophrenia Shenton 09/16/04-08/31/09 NIH/NIMH K05 MH 70047 PI
***MR Brain Diffusion Tensor Imaging in Schizophrenia Shenton 10/01/03-09/30/08 VA Merit PI
***Biological Basis of Schizotypal Personality Disorder McCarley/Shenton 08/01/94-03/01/10 NIH/NIMH R01 MH 52807 Co-PI
***Neuroimaging Studies of Schizophrenia McCarley/Shenton 04/01/03-03/31/09 VA Research Enhancement Award Program Co-PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/07 NIH/NIBIB U54 EB005149 Site-PI
***Vulnerability to Progression in Schizophrenia McCarley / /07-/ /12 NIH/NIMH P50 MH80272 Core & Project PI
**'''R. Kikinis'''
***Neuroimaging Analysis Center Kikinis 09/30/98-07/31/08 NIH/NCRR P41 RR13218 PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/09 NIH/NIBIB U54 EB005149 PI
**'''Jill Goldstein'''
***Shared Fetal Antecedents to Depression and Risk to CVD Goldstein 12/01/06-11/30/11 NIMH & NHLBI R01 MH074835 PI
***Gender and Brain Abnormalities in Schizophrenia Goldstein 07/15/97-06/30/11 NIMH R01MH056956 PI
***Sex & Brain Abnormalities in Schizophrenia Phase III Goldstein 07/01/06-06/30/11 NIMH RO1MH56956 PI
***Hormones/Genes in Women's Health: From Bench to Bedside Goldstein 09/01/05-07/31/10 NICHHD K12HD051959 PI
**'''Carl-Fredrik Westin'''
***Novel DT-MRI Analyses of White Matter in Schizophrenia Westin 11/01/06-10/31/11 NIH R01 MH074794 PI
***Real-time Control of 3D-Slicer Visualization Using Coordinates from Electrophysiological Catheter Tracking System Westin 10/01/05-09/30/07 CIMIT Application Development Award PI
**'''Charles Guttmann'''
***MRI Characterization of Cortical Lesions in MS Guttmann 10/01/04-09/30/07 National Multiple Sclerosis Society PI
**'''Reisa Sperling'''
***Alzheimer’s Association Sperling 11/01/06-10/31/09 Investigator Initiated Research Grant PI
***American Federation in Aging Research Sperling 07/01/03-06/30/08 Beeson Award PI
***FMRI assessment of hippocampal response to treatment with memantine Sperling 03/01/04-06/30/07 Investigator ***Initiated-Corporate Sponsored/Forest Pharmaceuticals PI
***Evolution of memory-related fMRI activation over the course of MCI and AD Sperling 05/01/06-01/31/11 NIA R01AG027435 PI
**'''Alex Golby'''
***Brain basis of memory studied by fMRI & intercranial EEG Golby 07/01/04-06/30/09 NIDS K08NS048063 PI

=Upcoming Events=
# [[NACG:MR-Protocols| Working Session on Acquisition Protocols for Neuroresearch]] Jan 25th at 9.30-10.30 in Neurosurgery conference room on the fourth floor of Carrie Hall.

NAC-Group

2007-01-15T03:21:21Z

Shenton: /* Current members: */

The NAC group is a group of scientists at '''BWH''' in Boston who are doing research in neuroimaging and neuroimage analysis. This group is open to all interested participants.

=The purpose of this group is to advance our individual research agendas by=

*exchanging know-how about image acquisition and image analysis tools and capabilities
*coordinating our efforts to improve infrastructure at the institution

=Current members:=
*[http://www.spl.harvard.edu/~jolesz Ferenc Jolesz] [http://www.ncigt.org National Center for Image Guided Therapy]
*[http://pnl.bwh.harvard.edu/people/profiles/shenton.html Martha Shenton] [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~kikinis Ron Kikinis] [http://www.spl.harvard.edu Surgical Planning Laboratory]
*Jill Goldstein
*[http://www.spl.harvard.edu/pages/ppl/sw/homepage.html William Wells]
*[http://lmi.bwh.harvard.edu/~westin/ Carl Fredrik Westin] [http://lmi.bwh.harvard.edu Laboratory of Mathematics in Imaging]
*Charles Guttmann [http://cni.bwh.harvard.edu Center for Neurologic Imaging ]
*[http://www.spl.harvard.edu/~reisa Reisa Sperling]
*[http://golbylab.bwh.harvard.edu/people_alex.html Alex Golby] [http://golbylab.bwh.harvard.edu Golbylab]
*[http://pnl.bwh.harvard.edu/people/profiles/kubicki.html Marek Kubicki] [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~cindy Cindy Wible]
Please feel free to add yourself to this list.

=Funding=
*This group has funding from many different agencies, both federal and private.
**'''F. Jolesz'''
***MR Guided Therapy Jolesz 09/30/95-04/30/07 NCI P01CA067165 PI
***Multidisciplinary Training in Image Guided Therapy Jolesz 09/24/01-06/30/07 NCI R25CA089017 PI
**'''M. Shenton'''
***Computerized Image Analyses of MR Scans in Schizophrenia Shenton 05/01/94-08/31/10 NIH/NIMH R01 MH 50740 PI
***Supplement to Promote Reentry into Biomedical and Behavioral Research Careers 07/01/06-06/30/09 NIH/NIMH R01 MH 50740 PI
***Clinical Symptoms & Brain Abnormalities in Schizophrenia Shenton 09/16/04-08/31/09 NIH/NIMH K05 MH 70047 PI
***MR Brain Diffusion Tensor Imaging in Schizophrenia Shenton 10/01/03-09/30/08 VA Merit PI
***Biological Basis of Schizotypal Personality Disorder McCarley/Shenton 08/01/94-03/01/10 NIH/NIMH R01 MH 52807 Co-PI
***Neuroimaging Studies of Schizophrenia McCarley/Shenton 04/01/03-03/31/09 VA Research Enhancement Award Program Co-PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/07 NIH/NIBIB U54 EB005149 Site-PI
***Vulnerability to Progression in Schizophrenia McCarley / /07-/ /12 NIH/NIMH P50 MH80272 Core & Project PI
**'''R. Kikinis'''
***Neuroimaging Analysis Center Kikinis 09/30/98-07/31/08 NIH/NCRR P41 RR13218 PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/09 NIH/NIBIB U54 EB005149 PI
**'''Jill Goldstein'''
***Shared Fetal Antecedents to Depression and Risk to CVD Goldstein 12/01/06-11/30/11 NIMH & NHLBI R01 MH074835 PI
***Gender and Brain Abnormalities in Schizophrenia Goldstein 07/15/97-06/30/11 NIMH R01MH056956 PI
***Sex & Brain Abnormalities in Schizophrenia Phase III Goldstein 07/01/06-06/30/11 NIMH RO1MH56956 PI
***Hormones/Genes in Women's Health: From Bench to Bedside Goldstein 09/01/05-07/31/10 NICHHD K12HD051959 PI
**'''Carl-Fredrik Westin'''
***Novel DT-MRI Analyses of White Matter in Schizophrenia Westin 11/01/06-10/31/11 NIH R01 MH074794 PI
***Real-time Control of 3D-Slicer Visualization Using Coordinates from Electrophysiological Catheter Tracking System Westin 10/01/05-09/30/07 CIMIT Application Development Award PI
**'''Charles Guttmann'''
***MRI Characterization of Cortical Lesions in MS Guttmann 10/01/04-09/30/07 National Multiple Sclerosis Society PI
**'''Reisa Sperling'''
***Alzheimer’s Association Sperling 11/01/06-10/31/09 Investigator Initiated Research Grant PI
***American Federation in Aging Research Sperling 07/01/03-06/30/08 Beeson Award PI
***FMRI assessment of hippocampal response to treatment with memantine Sperling 03/01/04-06/30/07 Investigator ***Initiated-Corporate Sponsored/Forest Pharmaceuticals PI
***Evolution of memory-related fMRI activation over the course of MCI and AD Sperling 05/01/06-01/31/11 NIA R01AG027435 PI
**'''Alex Golby'''
***Brain basis of memory studied by fMRI & intercranial EEG Golby 07/01/04-06/30/09 NIDS K08NS048063 PI

=Upcoming Events=
# [[NACG:MR-Protocols| Working Session on Acquisition Protocols for Neuroresearch]] Jan 25th at 9.30-10.30 in Neurosurgery conference room on the fourth floor of Carrie Hall.

NAC-Group

2007-01-15T03:20:09Z

Shenton: /* Current members: */

The NAC group is a group of scientists at '''BWH''' in Boston who are doing research in neuroimaging and neuroimage analysis. This group is open to all interested participants.

=The purpose of this group is to advance our individual research agendas by=

*exchanging know-how about image acquisition and image analysis tools and capabilities
*coordinating our efforts to improve infrastructure at the institution

=Current members:=
*[http://www.spl.harvard.edu/~jolesz Ferenc Jolesz] [http://www.ncigt.org National Center for Image Guided Therapy]
*[http://pnl.bwh.harvard.edu/people/profiles/shenton.html Martha Shenton] [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~kikinis Ron Kikinis] [http://www.spl.harvard.edu Surgical Planning Laboratory]
*Jill Goldstein
*[http://www.spl.harvard.edu/pages/ppl/sw/homepage.html William Wells]
*Carl Fredrik Westin [http://lmi.bwh.harvard.edu Laboratory of Mathematics in Imaging]
*Charles Guttmann [http://cni.bwh.harvard.edu Center for Neurologic Imaging ]
*[http://www.spl.harvard.edu/~reisa Reisa Sperling]
*[http://golbylab.bwh.harvard.edu/people_alex.html Alex Golby] [http://golbylab.bwh.harvard.edu Golbylab]
*[http://pnl.bwh.harvard.edu/people/profiles/kubicki.html Marek Kubicki] [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~cindy Cindy Wible]
Please feel free to add yourself to this list.

=Funding=
*This group has funding from many different agencies, both federal and private.
**'''F. Jolesz'''
***MR Guided Therapy Jolesz 09/30/95-04/30/07 NCI P01CA067165 PI
***Multidisciplinary Training in Image Guided Therapy Jolesz 09/24/01-06/30/07 NCI R25CA089017 PI
**'''M. Shenton'''
***Computerized Image Analyses of MR Scans in Schizophrenia Shenton 05/01/94-08/31/10 NIH/NIMH R01 MH 50740 PI
***Supplement to Promote Reentry into Biomedical and Behavioral Research Careers 07/01/06-06/30/09 NIH/NIMH R01 MH 50740 PI
***Clinical Symptoms & Brain Abnormalities in Schizophrenia Shenton 09/16/04-08/31/09 NIH/NIMH K05 MH 70047 PI
***MR Brain Diffusion Tensor Imaging in Schizophrenia Shenton 10/01/03-09/30/08 VA Merit PI
***Biological Basis of Schizotypal Personality Disorder McCarley/Shenton 08/01/94-03/01/10 NIH/NIMH R01 MH 52807 Co-PI
***Neuroimaging Studies of Schizophrenia McCarley/Shenton 04/01/03-03/31/09 VA Research Enhancement Award Program Co-PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/07 NIH/NIBIB U54 EB005149 Site-PI
***Vulnerability to Progression in Schizophrenia McCarley / /07-/ /12 NIH/NIMH P50 MH80272 Core & Project PI
**'''R. Kikinis'''
***Neuroimaging Analysis Center Kikinis 09/30/98-07/31/08 NIH/NCRR P41 RR13218 PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/09 NIH/NIBIB U54 EB005149 PI
**'''Jill Goldstein'''
***Shared Fetal Antecedents to Depression and Risk to CVD Goldstein 12/01/06-11/30/11 NIMH & NHLBI R01 MH074835 PI
***Gender and Brain Abnormalities in Schizophrenia Goldstein 07/15/97-06/30/11 NIMH R01MH056956 PI
***Sex & Brain Abnormalities in Schizophrenia Phase III Goldstein 07/01/06-06/30/11 NIMH RO1MH56956 PI
***Hormones/Genes in Women's Health: From Bench to Bedside Goldstein 09/01/05-07/31/10 NICHHD K12HD051959 PI
**'''Carl-Fredrik Westin'''
***Novel DT-MRI Analyses of White Matter in Schizophrenia Westin 11/01/06-10/31/11 NIH R01 MH074794 PI
***Real-time Control of 3D-Slicer Visualization Using Coordinates from Electrophysiological Catheter Tracking System Westin 10/01/05-09/30/07 CIMIT Application Development Award PI
**'''Charles Guttmann'''
***MRI Characterization of Cortical Lesions in MS Guttmann 10/01/04-09/30/07 National Multiple Sclerosis Society PI
**'''Reisa Sperling'''
***Alzheimer’s Association Sperling 11/01/06-10/31/09 Investigator Initiated Research Grant PI
***American Federation in Aging Research Sperling 07/01/03-06/30/08 Beeson Award PI
***FMRI assessment of hippocampal response to treatment with memantine Sperling 03/01/04-06/30/07 Investigator ***Initiated-Corporate Sponsored/Forest Pharmaceuticals PI
***Evolution of memory-related fMRI activation over the course of MCI and AD Sperling 05/01/06-01/31/11 NIA R01AG027435 PI
**'''Alex Golby'''
***Brain basis of memory studied by fMRI & intercranial EEG Golby 07/01/04-06/30/09 NIDS K08NS048063 PI

=Upcoming Events=
# [[NACG:MR-Protocols| Working Session on Acquisition Protocols for Neuroresearch]] Jan 25th at 9.30-10.30 in Neurosurgery conference room on the fourth floor of Carrie Hall.

NAC-Group

2007-01-15T03:19:12Z

Shenton: /* Current members: */

The NAC group is a group of scientists at '''BWH''' in Boston who are doing research in neuroimaging and neuroimage analysis. This group is open to all interested participants.

=The purpose of this group is to advance our individual research agendas by=

*exchanging know-how about image acquisition and image analysis tools and capabilities
*coordinating our efforts to improve infrastructure at the institution

=Current members:=
*[http://www.spl.harvard.edu/~jolesz Ferenc Jolesz] [http://www.ncigt.org National Center for Image Guided Therapy]
*[http://pnl.bwh.harvard.edu/people/profiles/shenton.html Martha Shenton] [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~kikinis Ron Kikinis] [http://www.spl.harvard.edu Surgical Planning Laboratory]
*Jill Goldstein
*[http://www.spl.harvard.edu/pages/ppl/sw/homepage.html William Wells]
*Carl Fredrik Westin [http://lmi.bwh.harvard.edu Laboratory of Mathematics in Imaging]
*Charles Guttmann [http://cni.bwh.harvard.edu Center for Neurologic Imaging ]
*[http://www.spl.harvard.edu/~reisa Reisa Sperling]
*[http://golbylab.bwh.harvard.edu/people_alex.html Alex Golby] [http://golbylab.bwh.harvard.edu Golbylab]
*Marek Kubicki [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~cindy Cindy Wible]
Please feel free to add yourself to this list.

=Funding=
*This group has funding from many different agencies, both federal and private.
**'''F. Jolesz'''
***MR Guided Therapy Jolesz 09/30/95-04/30/07 NCI P01CA067165 PI
***Multidisciplinary Training in Image Guided Therapy Jolesz 09/24/01-06/30/07 NCI R25CA089017 PI
**'''M. Shenton'''
***Computerized Image Analyses of MR Scans in Schizophrenia Shenton 05/01/94-08/31/10 NIH/NIMH R01 MH 50740 PI
***Supplement to Promote Reentry into Biomedical and Behavioral Research Careers 07/01/06-06/30/09 NIH/NIMH R01 MH 50740 PI
***Clinical Symptoms & Brain Abnormalities in Schizophrenia Shenton 09/16/04-08/31/09 NIH/NIMH K05 MH 70047 PI
***MR Brain Diffusion Tensor Imaging in Schizophrenia Shenton 10/01/03-09/30/08 VA Merit PI
***Biological Basis of Schizotypal Personality Disorder McCarley/Shenton 08/01/94-03/01/10 NIH/NIMH R01 MH 52807 Co-PI
***Neuroimaging Studies of Schizophrenia McCarley/Shenton 04/01/03-03/31/09 VA Research Enhancement Award Program Co-PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/07 NIH/NIBIB U54 EB005149 Site-PI
***Vulnerability to Progression in Schizophrenia McCarley / /07-/ /12 NIH/NIMH P50 MH80272 Core & Project PI
**'''R. Kikinis'''
***Neuroimaging Analysis Center Kikinis 09/30/98-07/31/08 NIH/NCRR P41 RR13218 PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/09 NIH/NIBIB U54 EB005149 PI
**'''Jill Goldstein'''
***Shared Fetal Antecedents to Depression and Risk to CVD Goldstein 12/01/06-11/30/11 NIMH & NHLBI R01 MH074835 PI
***Gender and Brain Abnormalities in Schizophrenia Goldstein 07/15/97-06/30/11 NIMH R01MH056956 PI
***Sex & Brain Abnormalities in Schizophrenia Phase III Goldstein 07/01/06-06/30/11 NIMH RO1MH56956 PI
***Hormones/Genes in Women's Health: From Bench to Bedside Goldstein 09/01/05-07/31/10 NICHHD K12HD051959 PI
**'''Carl-Fredrik Westin'''
***Novel DT-MRI Analyses of White Matter in Schizophrenia Westin 11/01/06-10/31/11 NIH R01 MH074794 PI
***Real-time Control of 3D-Slicer Visualization Using Coordinates from Electrophysiological Catheter Tracking System Westin 10/01/05-09/30/07 CIMIT Application Development Award PI
**'''Charles Guttmann'''
***MRI Characterization of Cortical Lesions in MS Guttmann 10/01/04-09/30/07 National Multiple Sclerosis Society PI
**'''Reisa Sperling'''
***Alzheimer’s Association Sperling 11/01/06-10/31/09 Investigator Initiated Research Grant PI
***American Federation in Aging Research Sperling 07/01/03-06/30/08 Beeson Award PI
***FMRI assessment of hippocampal response to treatment with memantine Sperling 03/01/04-06/30/07 Investigator ***Initiated-Corporate Sponsored/Forest Pharmaceuticals PI
***Evolution of memory-related fMRI activation over the course of MCI and AD Sperling 05/01/06-01/31/11 NIA R01AG027435 PI
**'''Alex Golby'''
***Brain basis of memory studied by fMRI & intercranial EEG Golby 07/01/04-06/30/09 NIDS K08NS048063 PI

=Upcoming Events=
# [[NACG:MR-Protocols| Working Session on Acquisition Protocols for Neuroresearch]] Jan 25th at 9.30-10.30 in Neurosurgery conference room on the fourth floor of Carrie Hall.

NAC-Group

2007-01-15T03:18:21Z

Shenton: /* Current members: */

The NAC group is a group of scientists at '''BWH''' in Boston who are doing research in neuroimaging and neuroimage analysis. This group is open to all interested participants.

=The purpose of this group is to advance our individual research agendas by=

*exchanging know-how about image acquisition and image analysis tools and capabilities
*coordinating our efforts to improve infrastructure at the institution

=Current members:=
*[http://www.spl.harvard.edu/~jolesz Ferenc Jolesz] [http://www.ncigt.org National Center for Image Guided Therapy]
*http://pnl.bwh.harvard.edu/people/profiles/shenton.html Martha Shenton] [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~kikinis Ron Kikinis] [http://www.spl.harvard.edu Surgical Planning Laboratory]
*Jill Goldstein
*[http://www.spl.harvard.edu/pages/ppl/sw/homepage.html William Wells]
*Carl Fredrik Westin [http://lmi.bwh.harvard.edu Laboratory of Mathematics in Imaging]
*Charles Guttmann [http://cni.bwh.harvard.edu Center for Neurologic Imaging ]
*[http://www.spl.harvard.edu/~reisa Reisa Sperling]
*[http://golbylab.bwh.harvard.edu/people_alex.html Alex Golby] [http://golbylab.bwh.harvard.edu Golbylab]
*Marek Kubicki [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~cindy Cindy Wible]
Please feel free to add yourself to this list.

=Funding=
*This group has funding from many different agencies, both federal and private.
**'''F. Jolesz'''
***MR Guided Therapy Jolesz 09/30/95-04/30/07 NCI P01CA067165 PI
***Multidisciplinary Training in Image Guided Therapy Jolesz 09/24/01-06/30/07 NCI R25CA089017 PI
**'''M. Shenton'''
***Computerized Image Analyses of MR Scans in Schizophrenia Shenton 05/01/94-08/31/10 NIH/NIMH R01 MH 50740 PI
***Supplement to Promote Reentry into Biomedical and Behavioral Research Careers 07/01/06-06/30/09 NIH/NIMH R01 MH 50740 PI
***Clinical Symptoms & Brain Abnormalities in Schizophrenia Shenton 09/16/04-08/31/09 NIH/NIMH K05 MH 70047 PI
***MR Brain Diffusion Tensor Imaging in Schizophrenia Shenton 10/01/03-09/30/08 VA Merit PI
***Biological Basis of Schizotypal Personality Disorder McCarley/Shenton 08/01/94-03/01/10 NIH/NIMH R01 MH 52807 Co-PI
***Neuroimaging Studies of Schizophrenia McCarley/Shenton 04/01/03-03/31/09 VA Research Enhancement Award Program Co-PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/07 NIH/NIBIB U54 EB005149 Site-PI
***Vulnerability to Progression in Schizophrenia McCarley / /07-/ /12 NIH/NIMH P50 MH80272 Core & Project PI
**'''R. Kikinis'''
***Neuroimaging Analysis Center Kikinis 09/30/98-07/31/08 NIH/NCRR P41 RR13218 PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/09 NIH/NIBIB U54 EB005149 PI
**'''Jill Goldstein'''
***Shared Fetal Antecedents to Depression and Risk to CVD Goldstein 12/01/06-11/30/11 NIMH & NHLBI R01 MH074835 PI
***Gender and Brain Abnormalities in Schizophrenia Goldstein 07/15/97-06/30/11 NIMH R01MH056956 PI
***Sex & Brain Abnormalities in Schizophrenia Phase III Goldstein 07/01/06-06/30/11 NIMH RO1MH56956 PI
***Hormones/Genes in Women's Health: From Bench to Bedside Goldstein 09/01/05-07/31/10 NICHHD K12HD051959 PI
**'''Carl-Fredrik Westin'''
***Novel DT-MRI Analyses of White Matter in Schizophrenia Westin 11/01/06-10/31/11 NIH R01 MH074794 PI
***Real-time Control of 3D-Slicer Visualization Using Coordinates from Electrophysiological Catheter Tracking System Westin 10/01/05-09/30/07 CIMIT Application Development Award PI
**'''Charles Guttmann'''
***MRI Characterization of Cortical Lesions in MS Guttmann 10/01/04-09/30/07 National Multiple Sclerosis Society PI
**'''Reisa Sperling'''
***Alzheimer’s Association Sperling 11/01/06-10/31/09 Investigator Initiated Research Grant PI
***American Federation in Aging Research Sperling 07/01/03-06/30/08 Beeson Award PI
***FMRI assessment of hippocampal response to treatment with memantine Sperling 03/01/04-06/30/07 Investigator ***Initiated-Corporate Sponsored/Forest Pharmaceuticals PI
***Evolution of memory-related fMRI activation over the course of MCI and AD Sperling 05/01/06-01/31/11 NIA R01AG027435 PI
**'''Alex Golby'''
***Brain basis of memory studied by fMRI & intercranial EEG Golby 07/01/04-06/30/09 NIDS K08NS048063 PI

=Upcoming Events=
# [[NACG:MR-Protocols| Working Session on Acquisition Protocols for Neuroresearch]] Jan 25th at 9.30-10.30 in Neurosurgery conference room on the fourth floor of Carrie Hall.

NAC-Group

2007-01-15T03:17:53Z

Shenton: /* Current members: */

The NAC group is a group of scientists at '''BWH''' in Boston who are doing research in neuroimaging and neuroimage analysis. This group is open to all interested participants.

=The purpose of this group is to advance our individual research agendas by=

*exchanging know-how about image acquisition and image analysis tools and capabilities
*coordinating our efforts to improve infrastructure at the institution

=Current members:=
*[http://www.spl.harvard.edu/~jolesz Ferenc Jolesz] [http://www.ncigt.org National Center for Image Guided Therapy]
*http://pnl.bwh.harvard.edu/people/profiles/shenton.html~Shenton Martha Shenton] [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~kikinis Ron Kikinis] [http://www.spl.harvard.edu Surgical Planning Laboratory]
*Jill Goldstein
*[http://www.spl.harvard.edu/pages/ppl/sw/homepage.html William Wells]
*Carl Fredrik Westin [http://lmi.bwh.harvard.edu Laboratory of Mathematics in Imaging]
*Charles Guttmann [http://cni.bwh.harvard.edu Center for Neurologic Imaging ]
*[http://www.spl.harvard.edu/~reisa Reisa Sperling]
*[http://golbylab.bwh.harvard.edu/people_alex.html Alex Golby] [http://golbylab.bwh.harvard.edu Golbylab]
*Marek Kubicki [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~cindy Cindy Wible]
Please feel free to add yourself to this list.

=Funding=
*This group has funding from many different agencies, both federal and private.
**'''F. Jolesz'''
***MR Guided Therapy Jolesz 09/30/95-04/30/07 NCI P01CA067165 PI
***Multidisciplinary Training in Image Guided Therapy Jolesz 09/24/01-06/30/07 NCI R25CA089017 PI
**'''M. Shenton'''
***Computerized Image Analyses of MR Scans in Schizophrenia Shenton 05/01/94-08/31/10 NIH/NIMH R01 MH 50740 PI
***Supplement to Promote Reentry into Biomedical and Behavioral Research Careers 07/01/06-06/30/09 NIH/NIMH R01 MH 50740 PI
***Clinical Symptoms & Brain Abnormalities in Schizophrenia Shenton 09/16/04-08/31/09 NIH/NIMH K05 MH 70047 PI
***MR Brain Diffusion Tensor Imaging in Schizophrenia Shenton 10/01/03-09/30/08 VA Merit PI
***Biological Basis of Schizotypal Personality Disorder McCarley/Shenton 08/01/94-03/01/10 NIH/NIMH R01 MH 52807 Co-PI
***Neuroimaging Studies of Schizophrenia McCarley/Shenton 04/01/03-03/31/09 VA Research Enhancement Award Program Co-PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/07 NIH/NIBIB U54 EB005149 Site-PI
***Vulnerability to Progression in Schizophrenia McCarley / /07-/ /12 NIH/NIMH P50 MH80272 Core & Project PI
**'''R. Kikinis'''
***Neuroimaging Analysis Center Kikinis 09/30/98-07/31/08 NIH/NCRR P41 RR13218 PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/09 NIH/NIBIB U54 EB005149 PI
**'''Jill Goldstein'''
***Shared Fetal Antecedents to Depression and Risk to CVD Goldstein 12/01/06-11/30/11 NIMH & NHLBI R01 MH074835 PI
***Gender and Brain Abnormalities in Schizophrenia Goldstein 07/15/97-06/30/11 NIMH R01MH056956 PI
***Sex & Brain Abnormalities in Schizophrenia Phase III Goldstein 07/01/06-06/30/11 NIMH RO1MH56956 PI
***Hormones/Genes in Women's Health: From Bench to Bedside Goldstein 09/01/05-07/31/10 NICHHD K12HD051959 PI
**'''Carl-Fredrik Westin'''
***Novel DT-MRI Analyses of White Matter in Schizophrenia Westin 11/01/06-10/31/11 NIH R01 MH074794 PI
***Real-time Control of 3D-Slicer Visualization Using Coordinates from Electrophysiological Catheter Tracking System Westin 10/01/05-09/30/07 CIMIT Application Development Award PI
**'''Charles Guttmann'''
***MRI Characterization of Cortical Lesions in MS Guttmann 10/01/04-09/30/07 National Multiple Sclerosis Society PI
**'''Reisa Sperling'''
***Alzheimer’s Association Sperling 11/01/06-10/31/09 Investigator Initiated Research Grant PI
***American Federation in Aging Research Sperling 07/01/03-06/30/08 Beeson Award PI
***FMRI assessment of hippocampal response to treatment with memantine Sperling 03/01/04-06/30/07 Investigator ***Initiated-Corporate Sponsored/Forest Pharmaceuticals PI
***Evolution of memory-related fMRI activation over the course of MCI and AD Sperling 05/01/06-01/31/11 NIA R01AG027435 PI
**'''Alex Golby'''
***Brain basis of memory studied by fMRI & intercranial EEG Golby 07/01/04-06/30/09 NIDS K08NS048063 PI

=Upcoming Events=
# [[NACG:MR-Protocols| Working Session on Acquisition Protocols for Neuroresearch]] Jan 25th at 9.30-10.30 in Neurosurgery conference room on the fourth floor of Carrie Hall.

NAC-Group

2007-01-15T03:17:20Z

Shenton: /* Current members: */

The NAC group is a group of scientists at '''BWH''' in Boston who are doing research in neuroimaging and neuroimage analysis. This group is open to all interested participants.

=The purpose of this group is to advance our individual research agendas by=

*exchanging know-how about image acquisition and image analysis tools and capabilities
*coordinating our efforts to improve infrastructure at the institution

=Current members:=
*[http://www.spl.harvard.edu/~jolesz Ferenc Jolesz] [http://www.ncigt.org National Center for Image Guided Therapy]
*http://pnl.bwh.harvard.edu/people/profiles/shenton.html/~ Martha Shenton] [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~kikinis Ron Kikinis] [http://www.spl.harvard.edu Surgical Planning Laboratory]
*Jill Goldstein
*[http://www.spl.harvard.edu/pages/ppl/sw/homepage.html William Wells]
*Carl Fredrik Westin [http://lmi.bwh.harvard.edu Laboratory of Mathematics in Imaging]
*Charles Guttmann [http://cni.bwh.harvard.edu Center for Neurologic Imaging ]
*[http://www.spl.harvard.edu/~reisa Reisa Sperling]
*[http://golbylab.bwh.harvard.edu/people_alex.html Alex Golby] [http://golbylab.bwh.harvard.edu Golbylab]
*Marek Kubicki [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~cindy Cindy Wible]
Please feel free to add yourself to this list.

=Funding=
*This group has funding from many different agencies, both federal and private.
**'''F. Jolesz'''
***MR Guided Therapy Jolesz 09/30/95-04/30/07 NCI P01CA067165 PI
***Multidisciplinary Training in Image Guided Therapy Jolesz 09/24/01-06/30/07 NCI R25CA089017 PI
**'''M. Shenton'''
***Computerized Image Analyses of MR Scans in Schizophrenia Shenton 05/01/94-08/31/10 NIH/NIMH R01 MH 50740 PI
***Supplement to Promote Reentry into Biomedical and Behavioral Research Careers 07/01/06-06/30/09 NIH/NIMH R01 MH 50740 PI
***Clinical Symptoms & Brain Abnormalities in Schizophrenia Shenton 09/16/04-08/31/09 NIH/NIMH K05 MH 70047 PI
***MR Brain Diffusion Tensor Imaging in Schizophrenia Shenton 10/01/03-09/30/08 VA Merit PI
***Biological Basis of Schizotypal Personality Disorder McCarley/Shenton 08/01/94-03/01/10 NIH/NIMH R01 MH 52807 Co-PI
***Neuroimaging Studies of Schizophrenia McCarley/Shenton 04/01/03-03/31/09 VA Research Enhancement Award Program Co-PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/07 NIH/NIBIB U54 EB005149 Site-PI
***Vulnerability to Progression in Schizophrenia McCarley / /07-/ /12 NIH/NIMH P50 MH80272 Core & Project PI
**'''R. Kikinis'''
***Neuroimaging Analysis Center Kikinis 09/30/98-07/31/08 NIH/NCRR P41 RR13218 PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/09 NIH/NIBIB U54 EB005149 PI
**'''Jill Goldstein'''
***Shared Fetal Antecedents to Depression and Risk to CVD Goldstein 12/01/06-11/30/11 NIMH & NHLBI R01 MH074835 PI
***Gender and Brain Abnormalities in Schizophrenia Goldstein 07/15/97-06/30/11 NIMH R01MH056956 PI
***Sex & Brain Abnormalities in Schizophrenia Phase III Goldstein 07/01/06-06/30/11 NIMH RO1MH56956 PI
***Hormones/Genes in Women's Health: From Bench to Bedside Goldstein 09/01/05-07/31/10 NICHHD K12HD051959 PI
**'''Carl-Fredrik Westin'''
***Novel DT-MRI Analyses of White Matter in Schizophrenia Westin 11/01/06-10/31/11 NIH R01 MH074794 PI
***Real-time Control of 3D-Slicer Visualization Using Coordinates from Electrophysiological Catheter Tracking System Westin 10/01/05-09/30/07 CIMIT Application Development Award PI
**'''Charles Guttmann'''
***MRI Characterization of Cortical Lesions in MS Guttmann 10/01/04-09/30/07 National Multiple Sclerosis Society PI
**'''Reisa Sperling'''
***Alzheimer’s Association Sperling 11/01/06-10/31/09 Investigator Initiated Research Grant PI
***American Federation in Aging Research Sperling 07/01/03-06/30/08 Beeson Award PI
***FMRI assessment of hippocampal response to treatment with memantine Sperling 03/01/04-06/30/07 Investigator ***Initiated-Corporate Sponsored/Forest Pharmaceuticals PI
***Evolution of memory-related fMRI activation over the course of MCI and AD Sperling 05/01/06-01/31/11 NIA R01AG027435 PI
**'''Alex Golby'''
***Brain basis of memory studied by fMRI & intercranial EEG Golby 07/01/04-06/30/09 NIDS K08NS048063 PI

=Upcoming Events=
# [[NACG:MR-Protocols| Working Session on Acquisition Protocols for Neuroresearch]] Jan 25th at 9.30-10.30 in Neurosurgery conference room on the fourth floor of Carrie Hall.

NAC-Group

2007-01-15T03:16:50Z

Shenton: /* Current members: */

The NAC group is a group of scientists at '''BWH''' in Boston who are doing research in neuroimaging and neuroimage analysis. This group is open to all interested participants.

=The purpose of this group is to advance our individual research agendas by=

*exchanging know-how about image acquisition and image analysis tools and capabilities
*coordinating our efforts to improve infrastructure at the institution

=Current members:=
*[http://www.spl.harvard.edu/~jolesz Ferenc Jolesz] [http://www.ncigt.org National Center for Image Guided Therapy]
*http://pnl.bwh.harvard.edu/people/profiles/shenton.html~Martha Shenton [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~kikinis Ron Kikinis] [http://www.spl.harvard.edu Surgical Planning Laboratory]
*Jill Goldstein
*[http://www.spl.harvard.edu/pages/ppl/sw/homepage.html William Wells]
*Carl Fredrik Westin [http://lmi.bwh.harvard.edu Laboratory of Mathematics in Imaging]
*Charles Guttmann [http://cni.bwh.harvard.edu Center for Neurologic Imaging ]
*[http://www.spl.harvard.edu/~reisa Reisa Sperling]
*[http://golbylab.bwh.harvard.edu/people_alex.html Alex Golby] [http://golbylab.bwh.harvard.edu Golbylab]
*Marek Kubicki [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~cindy Cindy Wible]
Please feel free to add yourself to this list.

=Funding=
*This group has funding from many different agencies, both federal and private.
**'''F. Jolesz'''
***MR Guided Therapy Jolesz 09/30/95-04/30/07 NCI P01CA067165 PI
***Multidisciplinary Training in Image Guided Therapy Jolesz 09/24/01-06/30/07 NCI R25CA089017 PI
**'''M. Shenton'''
***Computerized Image Analyses of MR Scans in Schizophrenia Shenton 05/01/94-08/31/10 NIH/NIMH R01 MH 50740 PI
***Supplement to Promote Reentry into Biomedical and Behavioral Research Careers 07/01/06-06/30/09 NIH/NIMH R01 MH 50740 PI
***Clinical Symptoms & Brain Abnormalities in Schizophrenia Shenton 09/16/04-08/31/09 NIH/NIMH K05 MH 70047 PI
***MR Brain Diffusion Tensor Imaging in Schizophrenia Shenton 10/01/03-09/30/08 VA Merit PI
***Biological Basis of Schizotypal Personality Disorder McCarley/Shenton 08/01/94-03/01/10 NIH/NIMH R01 MH 52807 Co-PI
***Neuroimaging Studies of Schizophrenia McCarley/Shenton 04/01/03-03/31/09 VA Research Enhancement Award Program Co-PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/07 NIH/NIBIB U54 EB005149 Site-PI
***Vulnerability to Progression in Schizophrenia McCarley / /07-/ /12 NIH/NIMH P50 MH80272 Core & Project PI
**'''R. Kikinis'''
***Neuroimaging Analysis Center Kikinis 09/30/98-07/31/08 NIH/NCRR P41 RR13218 PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/09 NIH/NIBIB U54 EB005149 PI
**'''Jill Goldstein'''
***Shared Fetal Antecedents to Depression and Risk to CVD Goldstein 12/01/06-11/30/11 NIMH & NHLBI R01 MH074835 PI
***Gender and Brain Abnormalities in Schizophrenia Goldstein 07/15/97-06/30/11 NIMH R01MH056956 PI
***Sex & Brain Abnormalities in Schizophrenia Phase III Goldstein 07/01/06-06/30/11 NIMH RO1MH56956 PI
***Hormones/Genes in Women's Health: From Bench to Bedside Goldstein 09/01/05-07/31/10 NICHHD K12HD051959 PI
**'''Carl-Fredrik Westin'''
***Novel DT-MRI Analyses of White Matter in Schizophrenia Westin 11/01/06-10/31/11 NIH R01 MH074794 PI
***Real-time Control of 3D-Slicer Visualization Using Coordinates from Electrophysiological Catheter Tracking System Westin 10/01/05-09/30/07 CIMIT Application Development Award PI
**'''Charles Guttmann'''
***MRI Characterization of Cortical Lesions in MS Guttmann 10/01/04-09/30/07 National Multiple Sclerosis Society PI
**'''Reisa Sperling'''
***Alzheimer’s Association Sperling 11/01/06-10/31/09 Investigator Initiated Research Grant PI
***American Federation in Aging Research Sperling 07/01/03-06/30/08 Beeson Award PI
***FMRI assessment of hippocampal response to treatment with memantine Sperling 03/01/04-06/30/07 Investigator ***Initiated-Corporate Sponsored/Forest Pharmaceuticals PI
***Evolution of memory-related fMRI activation over the course of MCI and AD Sperling 05/01/06-01/31/11 NIA R01AG027435 PI
**'''Alex Golby'''
***Brain basis of memory studied by fMRI & intercranial EEG Golby 07/01/04-06/30/09 NIDS K08NS048063 PI

=Upcoming Events=
# [[NACG:MR-Protocols| Working Session on Acquisition Protocols for Neuroresearch]] Jan 25th at 9.30-10.30 in Neurosurgery conference room on the fourth floor of Carrie Hall.

NAC-Group

2007-01-15T03:16:03Z

Shenton: /* Current members: */

The NAC group is a group of scientists at '''BWH''' in Boston who are doing research in neuroimaging and neuroimage analysis. This group is open to all interested participants.

=The purpose of this group is to advance our individual research agendas by=

*exchanging know-how about image acquisition and image analysis tools and capabilities
*coordinating our efforts to improve infrastructure at the institution

=Current members:=
*[http://www.spl.harvard.edu/~jolesz Ferenc Jolesz] [http://www.ncigt.org National Center for Image Guided Therapy]
*http://pnl.bwh.harvard.edu/people/profiles/shenton.html Martha Shenton [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~kikinis Ron Kikinis] [http://www.spl.harvard.edu Surgical Planning Laboratory]
*Jill Goldstein
*[http://www.spl.harvard.edu/pages/ppl/sw/homepage.html William Wells]
*Carl Fredrik Westin [http://lmi.bwh.harvard.edu Laboratory of Mathematics in Imaging]
*Charles Guttmann [http://cni.bwh.harvard.edu Center for Neurologic Imaging ]
*[http://www.spl.harvard.edu/~reisa Reisa Sperling]
*[http://golbylab.bwh.harvard.edu/people_alex.html Alex Golby] [http://golbylab.bwh.harvard.edu Golbylab]
*Marek Kubicki [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~cindy Cindy Wible]
Please feel free to add yourself to this list.

=Funding=
*This group has funding from many different agencies, both federal and private.
**'''F. Jolesz'''
***MR Guided Therapy Jolesz 09/30/95-04/30/07 NCI P01CA067165 PI
***Multidisciplinary Training in Image Guided Therapy Jolesz 09/24/01-06/30/07 NCI R25CA089017 PI
**'''M. Shenton'''
***Computerized Image Analyses of MR Scans in Schizophrenia Shenton 05/01/94-08/31/10 NIH/NIMH R01 MH 50740 PI
***Supplement to Promote Reentry into Biomedical and Behavioral Research Careers 07/01/06-06/30/09 NIH/NIMH R01 MH 50740 PI
***Clinical Symptoms & Brain Abnormalities in Schizophrenia Shenton 09/16/04-08/31/09 NIH/NIMH K05 MH 70047 PI
***MR Brain Diffusion Tensor Imaging in Schizophrenia Shenton 10/01/03-09/30/08 VA Merit PI
***Biological Basis of Schizotypal Personality Disorder McCarley/Shenton 08/01/94-03/01/10 NIH/NIMH R01 MH 52807 Co-PI
***Neuroimaging Studies of Schizophrenia McCarley/Shenton 04/01/03-03/31/09 VA Research Enhancement Award Program Co-PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/07 NIH/NIBIB U54 EB005149 Site-PI
***Vulnerability to Progression in Schizophrenia McCarley / /07-/ /12 NIH/NIMH P50 MH80272 Core & Project PI
**'''R. Kikinis'''
***Neuroimaging Analysis Center Kikinis 09/30/98-07/31/08 NIH/NCRR P41 RR13218 PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/09 NIH/NIBIB U54 EB005149 PI
**'''Jill Goldstein'''
***Shared Fetal Antecedents to Depression and Risk to CVD Goldstein 12/01/06-11/30/11 NIMH & NHLBI R01 MH074835 PI
***Gender and Brain Abnormalities in Schizophrenia Goldstein 07/15/97-06/30/11 NIMH R01MH056956 PI
***Sex & Brain Abnormalities in Schizophrenia Phase III Goldstein 07/01/06-06/30/11 NIMH RO1MH56956 PI
***Hormones/Genes in Women's Health: From Bench to Bedside Goldstein 09/01/05-07/31/10 NICHHD K12HD051959 PI
**'''Carl-Fredrik Westin'''
***Novel DT-MRI Analyses of White Matter in Schizophrenia Westin 11/01/06-10/31/11 NIH R01 MH074794 PI
***Real-time Control of 3D-Slicer Visualization Using Coordinates from Electrophysiological Catheter Tracking System Westin 10/01/05-09/30/07 CIMIT Application Development Award PI
**'''Charles Guttmann'''
***MRI Characterization of Cortical Lesions in MS Guttmann 10/01/04-09/30/07 National Multiple Sclerosis Society PI
**'''Reisa Sperling'''
***Alzheimer’s Association Sperling 11/01/06-10/31/09 Investigator Initiated Research Grant PI
***American Federation in Aging Research Sperling 07/01/03-06/30/08 Beeson Award PI
***FMRI assessment of hippocampal response to treatment with memantine Sperling 03/01/04-06/30/07 Investigator ***Initiated-Corporate Sponsored/Forest Pharmaceuticals PI
***Evolution of memory-related fMRI activation over the course of MCI and AD Sperling 05/01/06-01/31/11 NIA R01AG027435 PI
**'''Alex Golby'''
***Brain basis of memory studied by fMRI & intercranial EEG Golby 07/01/04-06/30/09 NIDS K08NS048063 PI

=Upcoming Events=
# [[NACG:MR-Protocols| Working Session on Acquisition Protocols for Neuroresearch]] Jan 25th at 9.30-10.30 in Neurosurgery conference room on the fourth floor of Carrie Hall.

NAC-Group

2007-01-15T03:15:47Z

Shenton: /* Current members: */

The NAC group is a group of scientists at '''BWH''' in Boston who are doing research in neuroimaging and neuroimage analysis. This group is open to all interested participants.

=The purpose of this group is to advance our individual research agendas by=

*exchanging know-how about image acquisition and image analysis tools and capabilities
*coordinating our efforts to improve infrastructure at the institution

=Current members:=
*[http://www.spl.harvard.edu/~jolesz Ferenc Jolesz] [http://www.ncigt.org National Center for Image Guided Therapy]
*http://pnl.bwh.harvard.edu/people/profiles/shenton.html Martha Shenton] [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~kikinis Ron Kikinis] [http://www.spl.harvard.edu Surgical Planning Laboratory]
*Jill Goldstein
*[http://www.spl.harvard.edu/pages/ppl/sw/homepage.html William Wells]
*Carl Fredrik Westin [http://lmi.bwh.harvard.edu Laboratory of Mathematics in Imaging]
*Charles Guttmann [http://cni.bwh.harvard.edu Center for Neurologic Imaging ]
*[http://www.spl.harvard.edu/~reisa Reisa Sperling]
*[http://golbylab.bwh.harvard.edu/people_alex.html Alex Golby] [http://golbylab.bwh.harvard.edu Golbylab]
*Marek Kubicki [http://pnl.bwh.harvard.edu Psychiatry Neuroimaging Laboratory]
*[http://www.spl.harvard.edu/~cindy Cindy Wible]
Please feel free to add yourself to this list.

=Funding=
*This group has funding from many different agencies, both federal and private.
**'''F. Jolesz'''
***MR Guided Therapy Jolesz 09/30/95-04/30/07 NCI P01CA067165 PI
***Multidisciplinary Training in Image Guided Therapy Jolesz 09/24/01-06/30/07 NCI R25CA089017 PI
**'''M. Shenton'''
***Computerized Image Analyses of MR Scans in Schizophrenia Shenton 05/01/94-08/31/10 NIH/NIMH R01 MH 50740 PI
***Supplement to Promote Reentry into Biomedical and Behavioral Research Careers 07/01/06-06/30/09 NIH/NIMH R01 MH 50740 PI
***Clinical Symptoms & Brain Abnormalities in Schizophrenia Shenton 09/16/04-08/31/09 NIH/NIMH K05 MH 70047 PI
***MR Brain Diffusion Tensor Imaging in Schizophrenia Shenton 10/01/03-09/30/08 VA Merit PI
***Biological Basis of Schizotypal Personality Disorder McCarley/Shenton 08/01/94-03/01/10 NIH/NIMH R01 MH 52807 Co-PI
***Neuroimaging Studies of Schizophrenia McCarley/Shenton 04/01/03-03/31/09 VA Research Enhancement Award Program Co-PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/07 NIH/NIBIB U54 EB005149 Site-PI
***Vulnerability to Progression in Schizophrenia McCarley / /07-/ /12 NIH/NIMH P50 MH80272 Core & Project PI
**'''R. Kikinis'''
***Neuroimaging Analysis Center Kikinis 09/30/98-07/31/08 NIH/NCRR P41 RR13218 PI
***National Alliance for Medical Imaging Computing (NAMIC) Kikinis 09/17/04-07/31/09 NIH/NIBIB U54 EB005149 PI
**'''Jill Goldstein'''
***Shared Fetal Antecedents to Depression and Risk to CVD Goldstein 12/01/06-11/30/11 NIMH & NHLBI R01 MH074835 PI
***Gender and Brain Abnormalities in Schizophrenia Goldstein 07/15/97-06/30/11 NIMH R01MH056956 PI
***Sex & Brain Abnormalities in Schizophrenia Phase III Goldstein 07/01/06-06/30/11 NIMH RO1MH56956 PI
***Hormones/Genes in Women's Health: From Bench to Bedside Goldstein 09/01/05-07/31/10 NICHHD K12HD051959 PI
**'''Carl-Fredrik Westin'''
***Novel DT-MRI Analyses of White Matter in Schizophrenia Westin 11/01/06-10/31/11 NIH R01 MH074794 PI
***Real-time Control of 3D-Slicer Visualization Using Coordinates from Electrophysiological Catheter Tracking System Westin 10/01/05-09/30/07 CIMIT Application Development Award PI
**'''Charles Guttmann'''
***MRI Characterization of Cortical Lesions in MS Guttmann 10/01/04-09/30/07 National Multiple Sclerosis Society PI
**'''Reisa Sperling'''
***Alzheimer’s Association Sperling 11/01/06-10/31/09 Investigator Initiated Research Grant PI
***American Federation in Aging Research Sperling 07/01/03-06/30/08 Beeson Award PI
***FMRI assessment of hippocampal response to treatment with memantine Sperling 03/01/04-06/30/07 Investigator ***Initiated-Corporate Sponsored/Forest Pharmaceuticals PI
***Evolution of memory-related fMRI activation over the course of MCI and AD Sperling 05/01/06-01/31/11 NIA R01AG027435 PI
**'''Alex Golby'''
***Brain basis of memory studied by fMRI & intercranial EEG Golby 07/01/04-06/30/09 NIDS K08NS048063 PI

=Upcoming Events=
# [[NACG:MR-Protocols| Working Session on Acquisition Protocols for Neuroresearch]] Jan 25th at 9.30-10.30 in Neurosurgery conference room on the fourth floor of Carrie Hall.