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2009 Annual Scientific Report

2009-05-14T03:25:11Z

Sidd queens: /* Engineering Component (Hayes) */

Back to [[2009_Progress_Report]]

=Guidelines for preparation=

*[[2009_Progress_Report#Scientific Report Timeline]] - Main point is that May 15 is the date by which all sections below need to be completed. No extensions are possible.
*DBPs - If there is work outside of the roadmap projects that you would like to report, you are welcome to create a separate section for it under "Other".
*The outline for this report is similar to the 2008 and 2007 reports, which are provided here for reference: [[2008_Annual_Scientific_Report]], [[2007_Annual_Scientific_Report]].
*In preparing summaries for each of the 8 topics in this report, please leverage the detailed pages for projects provided here: [[NA-MIC_Internal_Collaborations]].
*Publications will be mined from the SPL publications database. All core PIs need to ensure that all NA-MIC publications are in the publications database by May 15.

=Introduction (Tannenbaum)=

The National Alliance for Medical Imaging Computing (NA-MIC) is now completing its fifth year. The 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, as well as to oversee the training and dissemination of these tools to the medical research community. We are currently in year two of our second set of Driving Biological Projects (DBP), three of which involve diseases of the brain: (a) brain lesion analysis in neuropsychiatric systemic lupus erythematosus; (b) a study of cortical thickness for autism; and (c) stochastic tractography for velocardiofacial syndrome (VCFS). The fourth DBP takes the Center in a very new direction, (d) the prostate: brachytherapy needle positioning robot integration.

Over the past five years, NA-MIC has made substantial progress toward the attainment of its major objectives. In year one, the Center focused on forging alliances amongst its various cores and constituent groups to assure that the efforts of the cores were well integrated toward the attainment of common and specific goals. To that end a great deal of effort went into defining the kinds of tools that would be needed for specific imaging applications. The second year emphasized the identification of key research thrusts that cut across all cores and were driven by the needs and requirements of the DBPs. This led to the formulation of the Center's four main technical themes: Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. The third year of Center activity was devoted to the continuation of collaborative work to develop solutions for the various brain-oriented DBPs. The fourth year was focused on translating collaborative knowledge and work to a new set of DBPs. In the current fifth year, a number of projects have made sufficient progress to warrant introduction as modules in Slicer, thereby making the Core 1 algorithms available to the general medical imaging community. Some of these algorithms are quite general and can be used for purposes far broader than the original DBPs. For example, a new point cloud registration algorithm developed for the prostate brachytherapy needle positioning project also can be used for DWI registration. Likewise, work on DTI/DWI tractography has been applied to the segmentation of blood vessels and soft plaque detection in the coronary arteries.

Year five progress with respect to the current DBPs is relevant to the scope of this Annual Progress Report. As mentioned, we currently have three projects in the area of neuropsychiatric disorders: Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill). A fourth project from Johns Hopkins and Queens Universities involves the application of core technologies to imaging/robotics-guided treatments in prostate cancer. A number of papers have been published that specifically acknowledge the NA-MIC, and significant software development is continuing as well.

Section 3 outlines specific aims fulfilled this year by the four roadmap projects: Section 3.1 describes the Stochastic Tractography Approach for Velocardiofacial Syndrome; Section 3.2 details the application of our work to Brachytherapy Needle Positioning for the Prostate; Section 3.3 outlines the Brain Lesion Analysis in Neuropsychiatric Systemic Lupus Erythematosus project; and Section 3.4 documents the Cortical Thickness for Autism project. For all of these projects, a synergism of effort has produced working computer modules that are user friendly and accessible to both medical researchers and clinicians.

Section 4 describes year five work on the four infrastructure topics. These include: Diffusion Image Analysis (Section 4.1), Structural Analysis (Section 4.2), Functional MRI Analysis (Section 4.3), and the NA-MIC Toolkit (Section 4.4). Many of the algorithms produced by Cores 1-3 have been integrated into ITK and Slicer, including those concerning shape analysis (e.g., spherical wavelets), new segmentation algorithms (for DTI/DWI tractography and the segmentation of the prostate), and new approaches to registration (e.g., based on particle filtering).

Finally, the last three sections of this Annual Progress Report highlight some of the work that the the Scientific Leadership believes is particularly significant to the the overall goals of the Center. Section 5 summarizes the benefits of several advanced algorithms, gives a description of the growing NAMIC-Toolkit, and documents the scope of our efforts in technology transfer and outreach. It is essential to emphasize that although the algorithms emanating from this Center were developed to solve specific clinical problems raised by the DBPs, in application, most of these algorithms have far more general utility and far greater potential impact on the medical imaging technical base. To this end, Section 6 draws attention to the impact and value of our work on biocomputing imaging at three different levels: within the Center, within the NIH-funded research community, and externally to the national and international community. To further illustrate the impact of our work, Section 7 provides some updated timelines with specific milestones achieved by the various NA-MIC cores. Section 8 lists publications pertinent to the current reporting period that acknowledge NA-MIC support, and Section 9 provides the External Advisory Report and our considered response.

=Clinical Roadmap Projects=
==Roadmap Project: Stochastic Tractography for VCFS (Kubicki)==
===Overview (Kubicki)===
The goal of this project is to create an end-to-end application that is useful in evaluating anatomical connectivity between segmented cortical regions of the brain. The ultimate goal of our program is to understand similarities and differences in anatomical connectivity between genetically related schizophrenia and velocardio-facial syndrome. Thus we plan to use the "stochastic tractography" tool for the analysis of abnormalities in integrity or connectivity in the arcuate fasciculus fiber bundle that is involved in language processing in schizophrenia and VCFS.

===Algorithm Component (Golland)===
The core science involved in this project is the Stochastic Tractography algorithm. This algorithm was developed and implemented collaboratively by MIT and BWH. Stochastic Tractography is a Bayesian approach to estimating nerve fiber tracts from images created by diffusion tensor imaging (DTI).

In this approach, the diffusion tensor is used at each voxel in the volume to
construct a local probability distribution for the fiber direction
around the principal direction of diffusion. The tracts then are sampled
between two user-selected regions of interest (ROIs), by simulating a random walk between
the regions, based the local transition probabilities inferred from
the DTI image.

The resulting collection of fibers and the associated FA values
provide useful statistics on the properties of connections between the
two regions. To constrain the sampling process to the relevant white
matter region, atlas-based segmentation is used to label ventricles and
gray matter and to exclude them from the search space. This latter
step relies heavily on the registration and segmentation functionality
of Slicer.

Over the last year, we have been working on applying several pre- and postprocessing steps to the algorithm pipeline. These steps include "eddy current" and "geometric distortion correction," which were made available to us by the Utah group, as well as "DTI filtering" (BWH). White matter masks now also can be created based on T2 thresholding within the Slicer Stochastic Tractography module. These masks are more precise, as they do not rely on MRI-to-DTI co-registration.

We also have been working on the datasets that apply to situations where fMRI activations as well as gray matter segmentations need to be registered to DTI data to permit seeding within predefined gray matter regions. Significant progress has been made in modality registration, and additional improvement is expected when "geometric distortion correction" becomes part of the analysis pipeline.

Finally, we have been working on improved ways to visualize and quantify Stochastic Tractography output, not only by parametrizing fiber tracts, but also by creating connection probability distribution maps.

===Engineering Component (Davis)===
This year, the Stochastic Tractography Slicer module was rewritten in Python. The new module was released in December, 2008, and presented at the All Hands Meeting in Salt Lake City. The module is now a functional component of Slicer3. Documentation for operating the module also has been created to facilitate user training. Current Engineering efforts are focused on maintaining the module, optimizing the module for use with other data formats, and adding new functionality, such as better registration, distortion correction, and ways of extracting and measuring functional anisotropy (FA) along nerve fiber tracts.

The datasets used with the Stochastic Tractography module are computationally demanding. They involve higher spatial resolutions and many more diffusion directions as compared with White Matter Tractography, which was used previously. As well, the cortical ROIs tend to be much larger than white matter ROIs. Hence, there is a pressing need for performance improvement. This need can be appreciated by examining the differences between Stochastic Tractography, where literally hundreds of tracts are generated from a single seed, and Deterministic Tractography, where only a single tract is generated. Thus, some effort has been made to economize by use of multi-threading and parallel processing. A version of the Stochastic Tractography algorithm that uses large computer clusters also has been developed and can be downloaded and installed by individual users with minimal knowledge of parallel computing.

===Clinical Component (Kubicki)===
In this reporting period, we have designed, implemented, or completed several studies to test the Stochastic Tractography algorithm on the newly released 3T NA-MIC data. These datasets consist of high resolution DTI, structural RM data, and automatic anatomical segmentations. Since these data already have been co-registered, cortical ROIs can be used as seeding points for Stochastic Tractography.

The first of these clinical studies was designed to analyze the connections between the inferior frontal and superior temporal lobes, which represent important sites of the language network. The connections between these two regions were measured via Stochastic Tractography in a group of 20 chronic schizophrenia patients and 20 controls and then subjected to comparative analysis. We also examined gray matter volume in destination regions and attempted to estimate the relationship between gray and white matter abnormalities in schizophrenia. The results of this study were presented at the World Psychiatry Congress in Florence, Italy in April, 2009, and later that same month at the Harvard Psychiatry MYSELL conference.

Another current endeavor is the application of Stochastic Tractography to define the connections involved in emotional processing. [For this study, we are using cortical segmentations of the anterior cingulated gyrus, orbital-frontal gyrus, and amygdala and trace as well as quantify connections between these regions in healthy controls versus schizophrenia patients.] The results of this preliminary study were presented at MYSELL in April, 2009. Another presentation will be made at the Biological Psychiatry conference later this year.

We have also been involved in two collaborative efforts. The first involves the use of DTI data acquired at UCI. In this study, we have used the stochastic method to segment and measure the arcuate fasciculus in subjects with schizophrenia and language impairment, as evidenced in ERP data. In another collaboration, we are combining resting state fMRI data with DTI to measure connectivity between regions that form a functional network. Both of these projects are currently under way.

Finally, a paper that discusses the qualitative use of Stochastic Tractography has been accepted for publication in Human Brain Mapping and is currently in press. Here, when we combined fMRI with DTI whole brain data analysis, we identified certain regions that expressed abnormal functional connectivity in schizophrenia. These regions were then assigned to certain anatomical structures (white matter tracts) based on their location and relationship to Stochastic Tractography output.

===Additional Information===
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:Harvard:Brain_Segmentation_Roadmap here on the NA-MIC wiki].
==Roadmap Project: MR-guided Prostate Biopsy Needle Positioning Robot Integration (Fichtinger)==
===Overview (Fichtinger)===
Numerous studies have demonstrated the efficacy of image-guided needle-based therapy and biopsy in the management of prostate cancer. However, the accuracy of traditional prostate interventions that rely on transrectal ultrasound (TRUS) is limited by image fidelity, needle template guides, needle deflection, and tissue deformation. Magnetic Resonance
Imaging (MRI) is an ideal modality for guiding and monitoring such interventions because it provides excellent visualization of the prostate, its sub-structure, and surrounding tissues.

We have designed a comprehensive robotic assistant system that permits prostate biopsy and brachytherapy procedures to be performed entirely inside a 3T closed MRI scanner. The current system applies the transrectal approach to the prostate. With this approach, an endorectal coil and steerable needle guide, both tuned to 3T magnets and invariably to any particular scanner, are integrated into the MRI compatible manipulator.

Under the NA-MIC initiative, the interface between image computing, visualization, intervention planning, and kinematic planning is being managed by an open-source system built on the NA-MIC toolkit and its components, that is, Slicer3 and ITK. These tools are complemented by a collection of unsupervised prostate segmentation and registration methods that are important to the clinical performance of the interventional system as a whole.

===Algorithm Component (Tannenbaum)===

Our group (GaTech) has worked on both the segmentation and registration of the prostate from MRI and ultrasound data. This process is described below.

'''Prostate Segmentation'''

The first step is to "extract" the prostate. We have provided two methods: a shape-based method and a semi-automatic method. More details are given below and images and further details may be found [http://www.na-mic.org/Wiki/index.php/Projects:ProstateSegmentation here]

# ''A shape-based algorithm''. This process begins by learning a group of shapes, obtained by manually segmenting a set of prostate 3D images. With the shapes represented as the hyperbolic tangent of the signed distance functions, principle component analysis is employed to learn the shapes. Further, given a new prostate image, we search the learned shape space in order to find one shape best segment the given image. The fitness of one shape to segment the image is evaluated by an energy functional measuring the discrepancy of the statistical characteristics inside and outside the current segmentation boundary. Such method is robust to the noise in the images. Moreover, the whole algorithm pipeline has been integrated into the Slicer3 through the command line module.
# ''Semi-automatic method''. This method is based on a random walk segmentation algorithm. With user provided initial seed regions inside and out side the object (prostate), the algorithm computes a probability distribution over the image domain by solving a boundary value partial differential equation where the value at seed regions are fixed at 1.0 or 0.0, depending or whether they are object or background seeds. The resulting distribution indicates the probability of each voxel belonging to the object. Simply threshold by 0.5 gives the segmentation of the object. Moreover, if the result is not suitable, the user can edit the seed regions, and the new result is computed based on this previous result. This algorithm has been integrated into the transrectal prostate MRI module of Slier3.

'''Prostate Registration'''

We developed a nonlinear (affine) prostate registration method by treating prostate images as point sets. Then the iterative closest point algorithm is improved to register the point sets generated by the two images to be registered. The proposed method shows robustness to long distance transition and partial image structure. Moreover, such representation is much sparser than sampling image on the uniform grid thus the registration is very fast comparing two 3D volumetric
image registration.

Furthermore, the registration is viewed as a posterior estimation problem, in which the distributions of the affine and translation parameters are to be estimated. This can naturally be estimated using a particle filter framework. Through this, the method can handle the otherwise difficult cases where the two prostates are one supine and
one prone.

More details are given [[Projects:pfPtSetImgReg|here...]]

===Engineering Component (Hayes)===

An end-to-end slicer loadable module that interfaces with MRI-compatible robotic device to perform MRI guided prostate biopsy has been developed. A complete end-user tutorial documentation has been uploaded on the project wiki-page together with sample tutorial dataset (phantom) to facilitate user training. This is one of the first modules that uses Slicer as an interventional tool contrary to traditional usage as a post-processing tool.

This has been an year of ideas to implementation. The design hatched, was realized during this year. Specifically speaking, following functionality was implemented.

An intuitive workflow based GUI, that clearly identifies four phases of the intervention (Device calibration/registration, prostate segmentation, targeting, and verification) guides the user through the process.

The robotic device calibration/registration to scanner coordinates is achieved by means of segmenting fiducial markers in images. The registration parameters are used in targeting step for calculation of targeting parameters and needle tarjectory. The robotic device's optical encoders are interfaced, these sensors continuously sense the device rotation and needle angle, and are sent over USB interface, our module reads these on a 500 msec timer event.

The prostate segmentation algorithm developed by our algorithm core collaborators at Georgia Tech was integrated during NAMIC programming at Utah this year. The details are provided in previous sub-section.

The targeting step enables user to pick anatomic locations of interest by just clicking in any of 3 slice views. 3D Slicer's arbitrary reformat plane widget is an attractive feature which enables user/clinician to visualize and pick target in any desired plane. When a target is picked, the targeting parameters (device rotation and needle angle) for the device to hit the intended target are calculated and updated in the list of targets. Multiple targets can be picked and associated with a particular type of needle (e.g. biopsy or seed). Once a particular target is selected from the list of targets, the selected target is brought into view in all three slice views and highlighted in the 3D view, the information about the target and targeting parameters is displayed. Further, the robot's needle trajectory to the target is also visualized in 3D; this is a very crucial feedback for the clinician.

The biopsy is performed, and a validation volume is acquired while needle still in the prostate; this validation volume is then used to perform validation analysis, to find out how accurately did the device hit the target.

We've sought and received timely help from the engineering core. A couple of times, some functionality in Slicer 'core' was implemented for our specific requirements. The current engineering efforts are focussed on testing module at various levels, detecting and fixing bugs. We are in the process of designing a test protocol for functional and clinical evaluation of the software. Efforts are also on to add more functionality e.g. additional dedicated MR room display window which will display the chosen 2D image view for a particular target, the required robot targeting paramaters, and the sensed robot parameters; load previously saved experiment for post-op analysis; visualize robot anatomical coverage at calibration step itself, which can be used to reposition device if necessary.

===Clinical Component (Fichtinger)===
<Note Progress in the last year>

===Additional Information===
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:JHU:Roadmap here on the NA-MIC wiki].
==Roadmap Project: Brain Lesion Analysis in Neuropsychiatric Systemic Lupus Erythematosus (Bockholt)==
===Overview (Bockholt)===
The primary goal of the MIND DPB is to examine changes in white matter lesions in adults with Neuropsychiatric Systemic Lupus Erythematosus (SLE). We want to be able to characterize lesion location, size, and intensity, and would also like to examine longitudinal changes of lesions in an SLE cohort. To accomplish this goal, we will create an end-to-end application entirely within NA-MIC Kit allowing individual analysis of white matter lesions. Such a workflow will then be applied to a clinical sample in the process of being collected.

===Algorithm Component (Whitaker)===
The basic steps necessary for the white matter lesion analysis application entail first registration of T1, T2, and FLAIR images, second tissue classification into gray, white, csf, or lesion, thirdly clustering lesion for anatomical localization, and finally a summarization of lesion size and image intensity parameters within each unique lesion.

<Note Progress in the last year>

===Engineering Component (Pieper)===

At the [http://www.na-mic.org/Wiki/index.php/2009_Winter_Project_Week January 2009 NA-MIC project week] a first pass of a lesion segmentation tutorial was provided to the community. This was the first end-to-end workflow for this project and represented a significant step in the project. Based on feedback from the community and the target clinical users of these tools, we identified additional steps to improve the system.

The primary engineering effort has been directed to the following projects:

'''Interface Improvements'''

We have begun to look at the creation of a [[2009_Winter_Project_Week:HighLevelWizard| high level wizard]] as a front end to the processing task. This interface would allow users to go through the steps without directly navigating the slicer modules and can also provide state management that will simplify the visualization efforts.

'''Modularity and Deployment'''

The current tutorial has been difficult for some users to implement due to the requirement that the lesion detection module be compiled locally on the user's machine. Non-developers understandably find this to be a difficult requirement, so we are integrating the lesion segmentation code into the [http://www.slicer.org/slicerWiki/index.php/Slicer3:Loadable_Modules:Status Slicer3 loadable module project] so that pre-compiled versions of the module are available for users. To implement this, we are following the templates provided by the [http://www.nitrc.org/projects/slicer3examples/ slicer example modules] provided on the nitrc.org website. This infrastructure was created via a supplement to NA-MIC provided by the NITRC project. The [http://www.nitrc.org/projects/lupuslesion/ project page on nitrc.org] is being updated as new features are added to the modules.

'''Core Implementation Support'''

During this period we have also worked on [[2009_Winter_Project_Week:LesionSegmentationEfficiency|optimizing the implementation]] of the core ITK code. This effort has primarily been accomplished by the MIND group, with interactions as needed with the rest of the NA-MIC community.

In addition, ongoing discussions with the rest of the NA-MIC community are encouraging code sharing among projects through modularization of common processing tasks and development of 'best of breed' routines for lesion detection and quantification. These tools are then embodied as slicer modules for use in other applications such as brain tumor change tracking.

===Clinical Component (Bockholt)===
<Note Progress in the last year>
===Additional Information===
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:MIND:Roadmap here on the NA-MIC wiki].
==Roadmap Project: Cortical Thickness for Autism(Hazlett)==
===Overview (Hazlett)===

A primary goal of the UNC DPB is to examine changes in cortical thicknes in children with autism compared to typical controls. We want to examine group differences in both local and regional cortical thickness, and would also like to examine longitudinal changes in the cortex from ages 2-4 years. To accomplish this goal, this project will create an end-to-end application within Slicer3 allowing individual and group analysis of regional and local cortical thickness. Such a workflow will then be applied to our study data (already collected).

===Algorithm Component (Styner)===

The basic steps necessary for the cortical thickness application entail first tissue segmentation in order to separate white and gray matter regions, second cortical thickness measurement, thirdly cortical correspondence to compare measurements across subjects and finally a statistical analysis to locally compute group differences.

<Note Progress in the last year>

===Engineering Component (Miller, Vachet)===

<Note Progress in the last year>

===Clinical Component (Hazlett)===
<Note Progress in the last year>

===Additional Information===
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:UNC:Cortical_Thickness_Roadmap here on the NA-MIC wiki].

=Four Infrastructure Topics=
==Diffusion Image Analysis (Gerig)==
<Note Progress in the last year>
===Key Investigators===

<Need to update the list below>

* BWH: Marek Kubicki, Martha Shenton, Sylvain Bouix, Julien von Siebenthal, Thomas Whitford, Jennifer Fitzsimmons, Doug Terry, Jorge Alverado, Eric Melonakos, Carl-Fredrik Westin.
* MIT: Lauren O'Donnell, Polina Golland
* UCI: James Fallon, Judi Ford
* Utah I: Tom Fletcher, Ross Whitaker, Ran Tao, Yongsheng Pan
* Utah II: Casey Goodlett, Sylvain Gouttard, Guido Gerig
* GA Tech: John Melonakos, Vandana Mohan, Shawn Lankton, Allen Tannenbaum
* GE: Xiaodong Tao, Jim Miller, Mahnaz Mandah
* UCLA: Nathan Hageman
* Isomics: Steve Pieper
* Kitware: Luis Ibanez

===Additional Information===
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:DiffusionImageAnalysis here on the NA-MIC wiki].

==Structural Analysis(Tannenbaum)==
===Progress===
Under Structural Analysis, the main topics of research for NAMIC are structural segmentation, registration techniques and shape analysis. These topics are correlated and hence research in one often finds application in another. For example, shape analysis can yield useful priors for segmentation, or segmentation and registration can provide structural correspondences for use in shape analysis and so on.

An overview of selected progress highlights under these broad topics follows:

Segmentation

* Geodesic Tractography Segmentation: We proposed an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. This has been applied successfully to the segmentation of neural fiber bundles such as the Cingulum Bundle. This framework has now been integrated into Slicer and is being tested on a population of brain data sets.

* Tubular Surface Segmentation: We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. We have also developed the moving end points implementation of this framework wherein the required input is only a few points in the interior of the structure of interest. This yields the additional advantage that the framework simulatenously returns both the 3D segmentation and the 3D skeleton of the structure eliminating the need for apriori knowledge of end points, and an expensive skeletonization step. The framework is applicable to different tubular anatomical structures in the body. We have so far applied it successfully to the Cingulum Bundle, and blood vessels.

* Local-global Segmentation: We have proposed a novel segmentation approach that combines the advantages of local and global approaches to segmentation, by using statistics over regions that are local to each point on the evolving countour. This makes it well suited to applications with contrast differences within the structure of interest such as in blood vessel segmentation, as well as applications like the neural fiber bundles where the diffusion profiles of voxels within the structure are locally similar but vary along the length of the fiber bundle itself.

* Shape-based segmentation: 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 presented 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. We have applied the algorithm successfully to real MRI images, and we have also implemented it into 3D Slicer.

* Re-Orientation Approach for Segmentation of DW-MRI: This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. Segmentation is achieved through simple global statistical modeling of diffusion orientation which allows for a convex optimization formulation of the segmentation problem, combining orientation statistics and spatial regularization. The approach compares very favorably with segmentation by full-brain streamline tractography.

Registration

* Optimal Mass Transport based Registration: We have provided a computationaly efficient non-rigid/elastic image registration algorithm based on the Optimal Mass Transport theory. We use the Monge-Kantorovich formulation of the Optimal Mass Transport problem and implement the solution proposed by Haker et al. using multi-resolution and multigrid techniques to speed up the convergence. We also leverage the computation power of general purpose graphics processing units available on standard desktop computing machines to exploit the inherent parallelism in our algorithm. We extend the work by Haker et al. who compute the optimal warp from a first order partial differential equation, an improvement over earlier proposed higher order methods and those based on linear programming, and further implement the algorithm using a coarse-to-fine strategy resulting in phenomenol improvement in convergence. We have applied it successfully to the registration of 3D brain MRI datasets (preoperative and intra-operative), and are currently extending it to the non-rigid registration of baseline DWI to brain MRI data.

* Atlas Regularization for Image Segmentation: Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies.

* Point-set Rigid Registration: We have proposed a particle filtering scheme for the registration of 2D and 3D point set undergoing a rigid body transformation. Moreover, we incorporate stochastic dynamics to model the uncertainity of the registration process. We treat motion as a local variation in the pose parameters obatined from running a few iterations of the standard Iterative Closest Point (ICP) algorithm. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence as well as provide a dynamical model of uncertainity. In contrast with other techniques, our approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temoral coherency of the state (no loss of information). Also, unlike most alternative approaches for point set registration, we make no geometric assumptions on the two data sets.We applied the algorithm to different alignments of point clouds and it successfully found the correct optimal transformation that aligns two given point clouds despite the differing geometry around the local neighborhood of a point within their respective sets.

* Regularization for Optimal Mass Transport: To extend the flexibility of the existing OMT algorithm, we added a regularization term to the functional being minimized. This term controls the tradeoff between how well two images match after registration versus how warped the transformation map can become. A weighted sum of squared differences is used to penalize having to move mass over long distances; this addition also helps to keep the transformation physically accurate by reducing the likelihood that the transformation grid will fold over itself and keeping the grid smooth.

* Registration of DW-MRI to structural MRI: Optimal Mass Transport was applied to the problem of correcting EPI distortion in DW-MRI. A mask for white matter in DW-MRI was registered to the white matter mask extracted from the structural MRI for the same patient. Prior to registration, it is important to normalize intensities in the two masks; this was done by dividing the images into regions and uniformly normalizing over each region to assure the sum of the intensities is equal. Then, once a transformation between the white matter masks was calculated, this transformation was applied to the original DW-MRI image.

Shape Analysis

* Shape Analysis Framework using SPHARM-PDM: We have provided an analysis framework of objects with spherical topology, described by sampled spherical harmonics SPHARM-PDM. The input is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are first processed to fill any interior holes. The processed binary segmentations are converted to surface meshes, and a spherical parametrization is computed for the surface meshes using a area-preserving, distortion minimizing spherical mapping. The SPHARM description is computed from the mesh and its spherical parametrization. Using the first order ellipsoid from the spherical harmonic coefficients, the spherical parametrizations are aligned to establish correspondence across all surfaces. The SPHARM description is then sampled into a triangulated surfaces (SPHARM-PDM) via icosahedron subdivision of the spherical parametrization. These SPHARM-PDM surfaces are all spatially aligned using rigid Procrustes alignment. Group differences between groups of surfaces are computed using the standard robust Hotelling T 2 two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. We provide additional visualization of the group tests via mean difference magnitude and vector maps, as well as maps of the group covariance information. We have a stable implementation, and current development focuses on integrating the current command line tools into Slicer (v3) via the Slicer execution model.

* Population studies using Tubular Surface Model: We have proposed a tubular shape model for the Cingulum Bundle which models a tubular surface as a center-line coupled with a radius function at every point along the center-line. This model shows potential for population studies on the Cingulum Bundle which is believed to be involved in Schizophrenia, since it provides a natural way of sampling the structure to build a feature representation of it. We are currently segmenting the Cingulum Bundle from a population of brain data sets, towards performing this population analysis using the Pott's Model.

* Automatic Outlining of Sulci on a Brain Surface: We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain, where the data is taken to be a 3D triangulated mesh formed from the segmentation of MR image slices. The problem is posed as energy minimization using penalizing the arc-length of segmenting curve using conformal factor involving the mean curvature of the underlying surface. The computation is made practical for dense meshes via the use of a sparse-field method to track the level set interfaces and regularized least-squares estimation of geometric quantities.

===Key Investigators===

Needs to be updated:

* MIT: Polina Golland, Kilian Pohl, Sandy Wells, Eric Grimson, Mert R. Sabuncu
* UNC: Martin Styner, Ipek Oguz, Xavier Barbero
* Utah: Ross Whitaker, Guido Gerig, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer
* GaTech: Allen Tannenbaum, John Melonakos, Vandana Mohan, Tauseef ur Rehman, Shawn Lankton, Samuel Dambreville, Yi Gao, Romeil Sandhu, Xavier Le Faucheur, James Malcolm, Ivan Kolosev
* Isomics: Steve Pieper
* GE: Bill Lorensen, Jim Miller
* Kitware: Luis Ibanez, Karthik Krishnan
* UCLA: Arthur Toga, Michael J. Pan, Jagadeeswaran Rajendiran
* BWH: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt, Yogesh Rathi, Marek Kubicki, Steven Haker

===Additional Information===
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:StructuralImageAnalysis here on the NA-MIC wiki].
==fMRI Analysis (Golland)==
===Progress===

* Connectivity Analysis:

One of the major goals in analysis of fMRI data is the detection of functionally homogeneous networks in the brain. We developed a new method for characterizing functional connectivity patterns from fMRI. In contrast to the seed-based analysis typically employed to identify networks of co-activation, we propose to use clustering to simultaneously estimate the networks and their representative time courses, which effectively replace user-specified seeds.
During this year, we validated this method for characterizing functional connectivity patterns from fMRI. To investigate the sensitivity of the analysis to the generative model of the signal, we implemented and compared two distinct algorithms, the mixture-model clustering and the spectral clustering, in application to this problem. We validated our approach in a rest state fMRI scans of 45 healthy subjects. Our results demonstrate that the detected networks are stable across subjects and across methods.
At the same time, we worked with the Harvard DBP to identify relevant clinical data sets in which our approach promises to identify effect of a disorder. We have started a collaboration to apply the method to a group of schizophrenia patients and normal controls.

* Distortion Correction for EPI-Based Functiona Imaging

We developed and demonstrated a method for correcting the distortions present in echo planar images (EPI) and registering the EPI image to structural MRI scans. Our approach does not require acquiring fieldmaps, modifying EPI acquisition parameters, or having detailed knowledge of the shim system. The technique consists of two steps. First, a classifier is used to segment structural MR into an air/tissue susceptibility model. The resulting tissue map serves as input to a first order perturbation field model to compute a subject-specific fieldmap. The classifier is trained based on MR-CT image pairs, using MR intensities as features and exploiting air segmentation in the CT images to construct labels. Second, a simultaneous shim estimation and registration algorithm is employed to solve for the lower order field perturbations (shim parameters) needed to accurately unwarp and register the EPI data.

===Key Investigators===

* MIT: Polina Golland, Danial Lashkari,Archana Venkataraman, Clare Poynton
* Harvard/BWH: Sylvain Bouix, Martha Shenton, Marek Kubicki, Carl Frederick Westin, Sandy Wells

===Additional Information===
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:fMRIAnalysis here on the NA-MIC wiki].

==NA-MIC Kit Theme (Schroeder)==
===Progress===
The NAMIC-Kit consists of a framework of advanced computational components, as well as the support infrastructure for testing, documenting, and deploying leading edge medical imaging algorithms and software tools. The framework has been carefully constructed to provide low-level access to libraries and modules for advanced users, plus high-level application access that non-computer professionals can use to address a variety of problems in biomedical computing. In this fifth year of the NA-MIC projects <summary of progress>

===Software Releases===
The NAMIC-Kit can be represented as a pyramid of capabilities, with the base consisting of toolkits and libraries, and the apex standing in for the Slicer3 user application. In between, Slicer modules are stand-alone executables that can be integrated directly into the Slicer3 application, including GUI integration, while work-flows are groups of modules that are integrated together to manifest sophisticated segmentation, registration and biomedical computing algorithms. In a coordinated NAMIC effort, major releases of these many components were realized over the past year. This includes, but is not limited to:
*
*

===Slicer3 and the Software Framework===
One of the major achievements of the past year has been the release of [http://www.slicer.org/slicerWiki/index.php/Documentation-3.4 version 3.4 of 3D Slicer] in May of 2009. A number of important improvements have been made by the Engineering Core and significant new functionality has been added throug other NA-MIC cores and collaborators since the release of version 3.2 in August of 2008. A few notable examples include:

* [http://www.slicer.org/slicerWiki/index.php/Modules:Saving-Documentation-3.4 An Integrated Data Save Dialog]
* [http://www.slicer.org/slicerWiki/index.php/Modules:Fiducials-Documentation-3.4 Significant Rework of the Fiducials Interface]
* [http://www.slicer.org/slicerWiki/index.php/Modules:Slices-Documentation-3.4 A Slices Module to Support Advanced Visualization Modes]
* [http://www.slicer.org/slicerWiki/index.php/Modules:Editor-Documentation Significant Improvements to the Interactive Label Map Editor]
* [http://www.slicer.org/slicerWiki/index.php/Modules:ChangeTracker-Documentation-3.4 Integration of a Brain Tumor Change Tracking Module in collaboration with the Brain Science Foundation]
* [http://www.slicer.org/slicerWiki/index.php/Modules:IA_FEMesh-Documentation-3.4 Integration of a Finite Element Meshing Module as a deliverable of the NA-MIC Collaboration Grant at the University of Iowa]
* [http://www.slicer.org/slicerWiki/index.php/Modules:FetchMI-Documentation-3.4 A Medical Informatics Interface to XNAT in collaboration with BIRN]
* [http://www.slicer.org/slicerWiki/index.php/Slicer3:Python The Ability to Interactively Script Slicer in Python as well as Tcl]

In addition, there have been major extensions to the diffusion imaging tools, registration tools, filters, image guided therapy, and other core changes that enhance the utility and applicability of the software.

===Software Process===
One of the challenges facing developers has been the requirement to implement, test and deploy software systems across multiple computing platforms. NAMIC continues to push the state of the art with further development of the CMake, CTest, and CPack tools for cross-platform development, testing, and packaging, respectively...

===Key Investigators===
THis list needs to be updated:

* Kitware - Will Schroeder (Core 2 PI), Sebastien Barre, Luis Ibanez, Bill Hoffman
* GE - Jim Miller, Xiaodong Tao
* Isomics - Steve Pieper, Alex Yarmarkovich, Curt Lisle, Terry Lorber

===Additional Information===
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC-Kit here on the NA-MIC wiki].

=Highlights(Schroeder)=
===Advanced Algorithms===

===NAMIC-Kit===

===Outreach and Technology Transfer===
Cores 4-5-6 continue to support, train and dissemniate to the NAMIC community, and the broader biomedical computing community.
* The Slicer community held several workshops and tutorials. In xxx a satellite event was held for the international Organization for Human Brain Mapping at the annual meeting in xxx. The xx workshop on xx hosted xx participants representing xx countries from around the world, xx states within the US and xxdifferent laboratories including xx NIH institutes. In addition, <note how many slicer tutorials were held and where etc>
* Project Week continues to be a successful NAMIC venue. These semi-annual events are held in Boston in June, and January in Salt Lake City. These events are well attended with approximately 100 participants, of which about a third are outside collaborators. At the last Project Week in Salt Lake City, approximately xx projects were realized.
* NAMIC continues to participate in conferences and other technical venues. For example, NAMIC hosted xxx

=Impact and Value to Biocomputing (Miller)=
NA-MIC impacts Biocomputing through a variety of mechanisms. First,
NA-MIC produces scientific results, methodologies, workflows,
algorithms, imaging platforms, and software engineering tools and
paradigms in an open enviroment that contributes directly to the body of
knowledge available to the field. Second, NA-MIC science and
technology enables the entire medical imaging community to build on
NA-MIC results, methods, and techniques, to concentrate on the new
science instead of developing supporting infrastructure, to leverage
NA-MIC scientists and engineers to adapt NA-MIC technology to new
problem domains, and to leverage NA-MIC infrastructure to distribute
their own technology to a larger community.

===Impact within the Center===

===Impact within NIH Funded Research===

===National and International Impact===

= Timeline (Ross)=

<The table needs to be updated>

This section of the report gives the milestones for years 1 through 5 that are associated with the timelines in the original proposal. We have organized the milestones by core. For each milestone we have indicated the proposed year of completion and a very brief description of the current status. In some cases the milestones include ongoing work, and we have try to indicate that in the status. We have also included tables that list any significant changes to the proposed timelines. On the wiki page, we have links to the notes from the various PIs that give more details on their progress and the status of the milestones.

'''These tables demonstrate that the project is, on the whole, proceeding according to the originally planned schedule.'''

== Core 1: Algorithms ==

=== Timelines and Milestones ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Proposed time of completion'''
| '''Status'''
|-
| '''MIT'''
| 1
| '''Shape-based segmentation'''
|
|
|-
| '''MIT'''
| 1.1
| Methods to learn shape representations
| Year 2
| Completed
|-
| '''MIT'''
| 1.2
| Shape in atlas-driven segmentation
| Year 4
| Completed
|-
| '''MIT'''
| 1.3
| Validate and refine approach
| Year 5
| In Progress
|-
| '''MIT'''
| 2
| '''Shape analysis'''
|
|
|-
| '''MIT'''
| 2.1
| Methods to compute statistics of shapes
| Year 4
| Completed
|-
| '''MIT'''
| 2.3
| Validation of shape methods on application data
| Year 5
| Completed, refinements ongoing
|-
| '''MIT'''
| 3
| '''Analysis of DTI data'''
|
|
|-
| '''MIT'''
| 3.1
| Fiber geometry
| Year 3
| Completed
|-
| '''MIT'''
| 3.2
| Fiber statistics
| Year 5
| Completed, new developments ongoing
|-
| '''MIT'''
| 3.3
| Validation on real data
| Year 5
| Completed, refinements ongoing
|-
| '''Utah'''
| 1
| '''Processing of DTI data'''
|
|
|-
| '''Utah'''
| 1.1
| Filtering of DTI
| Year 2
| Completed
|-
| '''Utah'''
| 1.2
| Quantitative analysis of DTI
| Year 3
| Completed, refinements ongoing
|-
| '''Utah'''
| 1.3
| Segmentation of cortex/WM
| Year 3
| Completed partially, modified below
|-
| '''Utah'''
| 1.4
| Segmentation analysis of white matter tracts
| Year 3
| Completed, applications ongoing
|-
| '''Utah'''
| 1.5
| Joint analysis of DTI and functional data
| Year 5
| Initiated
|-
| '''Utah'''
| 2
| Nonparametric Shape Analysis
| Year 5
| Completed
|-
| '''Utah'''
| 2.1
| Framework in place
| Year 3
| Complete
|-
| '''Utah'''
| 2.2
| Demonstration on shape of neuranatomy (from Core 3)
| Year 4
| Complete
|-
| '''Utah'''
| 2.3
| Development for multiobject complexes
| Year 4
| Complete
|-
| '''Utah'''
| 2.4
| Demonstration of NP shape representations on clinical hypotheses from Core 3
| Year 5
| Complete, publications in progress
|-
| '''Utah'''
| 2.6
| Integration into NAMIC-kit
| Year 5
| Incomplete (initiated)
|-
| '''Utah'''
| 2.7
| Shape regression
| Year 5
| Incomplete
|-

|-
| '''UNC'''
| 1
| '''Statistical shape analysis'''
|
|
|-
| '''UNC'''
| 1.1
| Comparative anal. of shape anal. schemes
| Year 2
| Completed
|-
| '''UNC'''
| 1.3
| Statistical shape analysis incl. patient variable
| Year 5
| Complete, refinements ongoing
|-
| '''UNC'''
| 2
| '''Structural analysis of DW-MRI'''
|
|
|-
| '''UNC'''
| 2.1
| DTI tractography tools
| Year 4
| Completed
|-
| '''UNC'''
| 2.2
| Geometric characterization of fiber tracts
| Year 5
| Completed
|-
| '''UNC'''
| 2.3
| Quant. anal. of diffusion along fiber tracts
| Year 5
| Completed.
|-
| '''GaTech'''
| 1.1
| ITK Implementation of PDEs
| Year 2
| Completed
|-
| '''GaTech'''
| 1.1
| Applications to Core 3 data
| Year 4
| Completed
|-
| '''GaTech'''
| 1.2
| New statistic models
| Year 4
| Completed
|-
| '''GaTech'''
| 1.2
| Shape anaylsis
| Year 4
| Completed, refinements ongoing
|-
| '''GaTech'''
| 2.0
| Integration in to Slicer
| Year 4-5
| Preliminary results and ongoing
|-
| '''MGH'''
| 1
| '''Registration'''
|
|
|-
| '''MGH'''
| 1.1
| Collect DTI/QBALL data
| Year 2
| Completed
|-
| '''MGH'''
| 1.2
| Develop registration method
| Year 2
| Completed
|-
| '''MGH'''
| 1.3
| Test/optimize registration method
| Year 3
| In Progress
|-
| '''MGH'''
| 1.4
| Apply registration on core 3 data
| Year 5
| In Queue
|-
| '''MGH'''
| 2
| '''Group DTI Statistics'''
|
|
|-
| '''MGH'''
| 2.1
| Develop group statistic method
| Year 2
| Partially Complete
|-
| '''MGH'''
| 2.2
| Apply on core 3 data
| Year 5
| In Queue
|-
| '''MGH'''
| 3
| '''Diffusion Segmentation '''
|
|
|-
| '''MGH'''
| 3.1
| Collect DTI/QBALL data
| Year 2
| Completed
|-
| '''MGH'''
| 3.2
| Develop/optimize segmentation algorithm
| Year 3
| Modified
|-
| '''MGH'''
| 3.3
| Integrate w/ tractography
| Year 4
| Modified
|-
| '''MGH'''
| 3.4
| Apply on core 3 data
| Year 5
| Modified
|-
| '''MGH'''
| 4
| '''Group Morphometry Statistics'''
|
|
|-
| '''MGH'''
| 4.1
| Develop/optimize statistics algorithms
| Year 3
| Modified
|-
| '''MGH'''
| 4.2
| Develop GUI for Linux
| Year 3
| Modified
|-
| '''MGH'''
| 4.3
| Slicer integration
| Year 3
| Modified
|-
| '''MGH'''
| 4.4
| Compile application on Windows
| Year 4
| Modified
|-
| '''MGH'''
| 5
| XNAT Desktop
| Years 4-5
|
|-
| '''MGH'''
| 5.1
| Establish requirements for desktop version of XNAT
| Years 4-5
| Complete
|-
| '''MGH'''
| 5.2
| Develop implementation plan for prototype
| Years 4-5
| Complete
|-
| '''MGH'''
| 5.3
| Implement prototype version
| Years 4-5
| Incomplete (in progress)
|-
| '''MGH'''
| 5.4
| Implement alpha version
| Year 5
| Incomplete
|-
| '''MGH'''
| 6
| XNAT Central
| Years 4-5
|
|-
| '''MGH'''
| 6.1
| Deploy XNAT Central, a public access XNAT host
| Years 4-5
| Complete
|-
| '''MGH'''
| 6.2
| Coordinate with NAMIC sites to upload project data
| Years 4-5
| Incomplete (ongoing)
|-
| '''MGH'''
| 6.3
| Continue developing XNAT Central based on feedback from NAMIC sites
| Years 4-5
| Incomplete (ongoing)
|-
| '''MGH'''
| 7
| NAMIC Kit integration
| Years 4-5
|
|-
| '''MGH'''
| 7.1
| Implement web services to exchange data with Slicer, Batchmake, and other client applications
| Years 4-5
| Incomplete (ongoing)
|-
| '''MGH'''
| 7.2
| Add XNAT Desktop to standard NAMIC kit distribution
| Year 5
| Incomplete
|-
|}

=== Timeline Modifications ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Modification'''
|-
| '''MIT'''
| 2.2
| Methods to compare shape statistics
| Removed, the effort refocused on registration necessary for population studies
|-
| '''MIT'''
| 2.4
| Software infrastructure to integrate shape analysis tools into the pipeline for population studies.
| New, morphed into collaboration with XNAT to provide more general population analysis tools. Partially completed.
|-
| '''MIT'''
| 4
| fMRI analysis including local and atlas-based priors for quantifying activation.
| New, partially completed. Refinements in progress. Clinical study with Core 1 is in progress.
|-
| '''Utah'''
| 2.2 (removed)
| Feature-based brain image registration.
| Shift emphasis to shape-based analysis/registration
|-
| '''Utah'''
| 2.1 (removed)
| Cortical filtering and feature detection
| Effort is subsumed by other Core 1 partners (e.g. see MGH/Freesurfer)
|-
| '''Utah'''
| 1.3 (removed)
| Segmentation of cortex/WM
| Effort is subsumed by other Core 1-2 partners (e.g. see EM-Segmenter)
|-
| '''Utah'''
| 3.0 (removed)
| Fast implmentations of PDEs
| Real-time filtering is demphasized in favor of shape/DTI analysis
|-
| '''Utah'''
| 1.5 (added)
| Joint analysis of DTI and functional data
| Opportunities/needs within various collaborations
|-
| '''Utah'''
| 2.1-2.3 (added, in place of cortical analysis)
| Shape analysis
| Nonparametric shape analysis added to address needs of core 3.
|-
| '''Utah'''
| 2.7
| Shape regression
| Extension/completion of framework. Opportunities/needs within various collaborations.
|-
| '''UNC'''
| 1.2
| Develop medially-based shape representation
| Remove
|-
| '''UNC'''
| 1.4
| Develop generic cortical correspondence framework (Years 3-5)
| New
|-
| '''UNC'''
| 2.4
| DTI Atlas Building (Years 2--4)
| New
|-
| '''GaTech'''
| 2.1
| FA analysis
| New
|-
| '''MGH'''
| 4.1 - 4.4
| Group Morphometry Statistics
| Added and then removed, based on personnel changes
|-
| '''MGH'''
| 5-7
| XNAT
| Added to support remote image database capabilities
|}

=== [[Core_1_Timeline_Notes|Core 1 Timeline Notes ]] ===

== Core 2: Engineering ==

=== Core 2 Timelines and Milestones ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Proposed time of completion'''
| '''Status'''
|-
| '''GE'''
| 1
| '''Define software architecture'''
|
|
|-
| '''GE'''
| 1
| Object design
| Yr 1
| Completed
|-
| '''GE'''
| 1
| Identify patterns
| Yr 3
| Patterns for processing scalar and vector images, models, fiducials complete. Patterns for diffusion weighted completed, fMRI ongoing.
|-
| '''GE'''
| 1
| Create frameworks
| Yr 3
| Frameworks for processing scalar and vector images, models, fiducials complete. Frameworks for diffusion weighted completed, fMRI ongoing.
|-
| '''GE'''
| 2
| '''Software engineering process'''
|
|
|-
| '''GE'''
| 2
| Extreme programming
| Yr 1-5
| On schedule, ongoing
|-
| '''GE'''
| 2
| Process automatiion
| Yr 3
| On schedule, ongoing
|-
| '''GE'''
| 2
| Refactoring
| Yr 3
| Complete
|-
| '''GE'''
| 3
| '''Automated quality system'''
|
|
|-
| '''GE'''
| 3
| DART deployment
| Yr 2
| Complete
|-
| '''GE'''
| 3
| Persistent testing system
| Yr 5
| Incomplete
|-
| '''GE'''
| 3
| Automatic defect detection
| Yr 5
| Incomplete
|-
| '''Kitware'''
| 1
| '''Cross-platform development'''
|
|
|-
| '''Kitware'''
| 1
| Deploy environment (CMake, CTest)
| Yr 1
| Complete
|-
| '''Kitware'''
| 1
| DART Integration and testing
| Yr 1
| Complete
|-
| '''Kitware'''
| 1
| Documentation tools
| Yr 2
| Complete
|-
| '''Kitware'''
| 2
| '''Integration tools'''
|
|
|-
| '''Kitware'''
| 2
| File Formats/IO facilities
| Yr 2
| Complete
|-
| '''Kitware'''
| 2
| CableSWIG deployment
| Yr 3
| Complete (integration ongoing)
|-
| '''Kitware'''
| 2
| Establish XML schema
| Yr 4
| Complete, refinements ongoing
|-
| '''Kitware'''
| 3
| '''Technology delivery'''
|
|
|-
| '''Kitware'''
| 3
| Deploy applications
| Yr 1
| Complete (ongoing)
|-
| '''Kitware'''
| 3
| Establish plug-in repository
| Yr 2
| Incomplete
|-
| '''Kitware'''
| 3
| Cpack
| Yr 4-5
| Incomplete
|-
| '''Isomics'''
| 1
| NAMIC builds of slicer
| Years 2--5
| Complete
|-
| '''Isomics'''
| 1
| Schizophrenia and DBP intefaces
| Year 3---5
| Completed (refinements ongoing)
|-
| '''Isomics'''
| 2
| ITK Integration tools
| Year 1---3
| Completed
|-
| '''Isomics'''
| 2
| Experiment Control Interfaces
| Year 2---5
| Migration from LONI to BatchMake Underway
|-
| '''Isomics'''
| 2
| fMRI/DTI algorithm support
| Year 2---5
| Completed DTI, fMRI Ongoing
|-
| '''Isomics'''
| 2
| New DBP algorithm support
| Year 2---5
| Ongoing
|-
| '''Isomics'''
| 3
| Compatible build process
| Year 1---3
| Completed
|-
| '''Isomics'''
| 3
| Dart Integration
| Year 1---2
| Completed (upgrades ongoing)
|-
| '''Isomics'''
| 3
| Test scripts for new code
| Year 2---5
| Ongoing
|-
| '''UCSD'''
| 1
| Grid computing---base
| Year 1
| Completed
|-
| '''UCSD'''
| 1
| Grid enabled algorithms
| Year 3
| First version (GWiz alpha) available - initial integration with Slicer3 and execution model.
|-
| '''UCSD'''
| 1
| Testing infrastructure
| Year 4
| Initiated
|-
| '''UCSD'''
| 2
| Data grid --- compatibility
| Year 2
| Completed
|-
| '''UCSD'''
| 2
| Data grid --- slicer access
| Year 2
| Completed for version 2.6. In progress for Slicer3
|-
| '''UCSD'''
| 3
| Data mediation --- deploy
| Year 1
| Incomplete (modfication below)
|-
| '''UCLA'''
| 1
| Debabeler functionality
| Year 1
| Continued Progress
|-
| '''UCLA'''
| 2
| SLIPIE Interpretation (Layer 1)
| Year 1--Year2
| In Progress
|-
| '''UCLA'''
| 3
| SLIPIE Interpretation (Layer 2)
| Year 1--Year2
| On Schedule
|-
| '''UCLA'''
| 3
| Developing ITK Modules
| Year2
| In Progress
|-
| '''UCLA'''
| 4
| Integrating SRB (GSI-enabled)
| Year2
| Completed
|-
| '''UCLA'''
| 5
| Integrating IDA
| Year2
| Completed
|-
| '''UCLA'''
| 5
| Integrating External Visualization Applications
| Year2
| Completed
|}

=== Core 2 Timeline Modifications ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Modification'''
|-
| '''Isomics'''
| 3
| Data mediation
| Delayed pending integration of databases into NAMIC infractructure
|}

=== [[Core_2_Timeline_Notes|Core 2 Timeline Notes ]] ===

== Core 3: Driving Biological Problems ==

The Core 3 projects submitted R01 style proposals, as specified in the RFA, and did not submit timelines.

== Core 4: Service ==

=== Core 4 Timelines and Milestones ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Proposed time of completion'''
| '''Status'''
|-
| '''Kitware'''
| 1
| '''Implement Development Farms'''
|
|
|-
| '''Kitware'''
| 1
| Deploy platforms
| Yrs 1
| Complete
|-
| '''Kitware'''
| 1
| Communications
| Yrs 1
| Complete, ongoing
|-
| '''Kitware'''
| 2
| '''Establish software process'''
|
|
|-
| '''Kitware'''
| 2
| Secure developer database
| Yr 1
| Complete, ongoing
|-
| '''Kitware'''
| 2
| Collect guidelines
| Yr 1
| Complete
|-
| '''Kitware'''
| 2
| Manage software submission process
| Yr 1
| Complete
|-
| '''Kitware'''
| 2
| Configure process tools
| Yr 1
| Complete
|-
| '''Kitware'''
| 2
| Survey community
| Yr 1
| Complete
|-
| '''Kitware'''
| 3
| '''Deploy NAMIC Tools'''
|
|
|-
| '''Kitware'''
| 3
| Toolkits
| Yr 1
| Complete
|-
| '''Kitware'''
| 3
| Integration tools
| Yr 1
| Complete
|-
| '''Kitware'''
| 3
| Applications
| Yr 1
| Complete
|-
| '''Kitware'''
| 3
| Integrate new computing resources
| Yr 1
| Complete
|-
| '''Kitware'''
| 4
| '''Provide support'''
|
|
|-
| '''Kitware'''
| 4
| Esablish support infrastructure
| Yrs 1--5
| On schedule, ongoing
|-
| '''Kitware'''
| 4
| NAMIC support
| Yr 1
| Complete
|-
| '''Kitware'''
| 5
| Manage NAMIC Software Releases
| Yrs 1--5
| On schedule, ongoing
|}

=== Core 4 Timeline Modifications ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Modification'''
|-
| Kitware
| 2-5
| Various
| Refined/modified the sub aims
|}

=== [[Core_4_Timeline_Notes|Core 4 Timeline Notes ]] ===

== Core 5: Training ==

=== Core 5 Timelines and Milestones ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Proposed time of completion'''
| '''Status'''
|-
| '''Harvard'''
| 1
| '''Formal Training Guidllines'''
|
|
|-
| '''Harvard'''
| 1
| Functional neuroanatomy
| Yr 1
| Complete
|-
| '''Harvard'''
| 1
| Clinical correlations
| Yr 1
| Complete
|-
| '''Harvard'''
| 2
| '''Mentoring'''
|
|
|-
| '''Harvard'''
| 2
| Programming workshops
| Yrs 1-5
| On schedule, ongoing
|-
| '''Harvard'''
| 2
| One-on-one mentoring, Cores 1, 2, 3
| Yrs 1-5
| On schedule, ongoing
|-
| '''Harvard'''
| 3
| '''Collaborative work environment'''
|
|
|-
| '''Harvard'''
| 3
| Wiki
| Yrs 1
| Complete
|-
| '''Harvard'''
| 3
| Mailing lists
| Yrs 1
| Complete
|-
| '''Harvard'''
| 3
| Regular telephone conferences
| Yrs 1-5
| On schedule, ongoing
|-
| '''Harvard'''
| 4
| '''Educational component for tools'''
|
|
|-
| '''Harvard'''
| 4
| Slicer training modules
| Yr 2-5
| Slicer 2.x tutorials complete, Two Slicer 3 tutorials complete, translation of 2.x tutorials to 3 is ongoing and on schedule
|-
| '''Harvard'''
| 5
| '''Demonstrations and hands-on training'''
|
|
|-
| '''Harvard'''
| 5
| Various workshops and conferences
| Yrs 1--5
| On schedule, ongoing
|}

=== Core 5 Timeline Modifications ===

None.

=== [[Core_5_Timeline_Notes|Core 5 Timeline Notes ]] ===

== Core 6: Dissemination ==

=== Core 6 Timelines and Milestones ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Proposed time of completion'''
| '''Status'''
|-
| Isomics
| 1
| Create a collaboration metholdology for NA-MIC
|
|
|-
| Isomics
| 1.1
| develop a selection process
| Yr 1
| Complete
|-
| Isomics
| 1.2
| guidelines to govern the collaborations
| Yr 1-2
| Complete
|-
| Isomics
| 1.3
| Provide on-site training
| Yr 1-5
| Complete for current tools (ongoing for tool refinement)
|-
| Isomics
| 1.4
| develop a web site infrastructure
| Yr 1
| Complete
|-
| Isomics
| 2
| Facilitate communication between NA-MIC developers and wider research community
|
|
|-
| Isomics
| 2.1
| develop materials describing NAMIC technology
| Yr 1-5
| On Schedule
|-
| Isomics
| 2.2
| participate in scientific meetings
| Yr 2-5
| On Schedule
|-
| Isomics
| 2.3
| Document interactions with external researchers
| Yr 2-5
| On Schedule
|-
| Isomics
| 2.4
| Coordinate publication strategies
| Yr 3-5
| On Schedule
|-
| Isomics
| 3
| Develop a publicly accessible internet resource of data, software, documentation, and publication of new discoveries
|
|
|-
| Isomics
| 3.1
| On-line repository of NAMIC related publications and presentations
| Yr 1-5
| On Schedule
|-
| Isomics
| 3.2
| On-line repository of NAMIC tutorial and training material
| Yr 1-5
| On Schedule
|-
| Isomics
| 3.3
| Index and a searchable database
| Yr 1-2
| Done
|-
| Isomics
| 3.4
| Automated feedback systems that track software downloads
| Yr 3
| Done
|}

=== Core 6 Timeline Modifications ===

None.

=== [[Core_6_Timeline_Notes|Core 6 Timeline Notes ]] ===

=Appendix A Publications (Mastrogiacomo)=
A list should be mined from the publications database and attached here in MS word format.

=Appendix B EAB Report and Response (Kapur)=
===EAB Report===

===Response to EAB Report===

2009 Annual Scientific Report

2009-05-14T03:19:26Z

Sidd queens: /* Engineering Component (Hayes) */

Back to [[2009_Progress_Report]]

=Guidelines for preparation=

*[[2009_Progress_Report#Scientific Report Timeline]] - Main point is that May 15 is the date by which all sections below need to be completed. No extensions are possible.
*DBPs - If there is work outside of the roadmap projects that you would like to report, you are welcome to create a separate section for it under "Other".
*The outline for this report is similar to the 2008 and 2007 reports, which are provided here for reference: [[2008_Annual_Scientific_Report]], [[2007_Annual_Scientific_Report]].
*In preparing summaries for each of the 8 topics in this report, please leverage the detailed pages for projects provided here: [[NA-MIC_Internal_Collaborations]].
*Publications will be mined from the SPL publications database. All core PIs need to ensure that all NA-MIC publications are in the publications database by May 15.

=Introduction (Tannenbaum)=

The National Alliance for Medical Imaging Computing (NA-MIC) is now completing its fifth year. The 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, as well as to oversee the training and dissemination of these tools to the medical research community. We are currently in year two of our second set of Driving Biological Projects (DBP), three of which involve diseases of the brain: (a) brain lesion analysis in neuropsychiatric systemic lupus erythematosus; (b) a study of cortical thickness for autism; and (c) stochastic tractography for velocardiofacial syndrome (VCFS). The fourth DBP takes the Center in a very new direction, (d) the prostate: brachytherapy needle positioning robot integration.

Over the past five years, NA-MIC has made substantial progress toward the attainment of its major objectives. In year one, the Center focused on forging alliances amongst its various cores and constituent groups to assure that the efforts of the cores were well integrated toward the attainment of common and specific goals. To that end a great deal of effort went into defining the kinds of tools that would be needed for specific imaging applications. The second year emphasized the identification of key research thrusts that cut across all cores and were driven by the needs and requirements of the DBPs. This led to the formulation of the Center's four main technical themes: Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. The third year of Center activity was devoted to the continuation of collaborative work to develop solutions for the various brain-oriented DBPs. The fourth year was focused on translating collaborative knowledge and work to a new set of DBPs. In the current fifth year, a number of projects have made sufficient progress to warrant introduction as modules in Slicer, thereby making the Core 1 algorithms available to the general medical imaging community. Some of these algorithms are quite general and can be used for purposes far broader than the original DBPs. For example, a new point cloud registration algorithm developed for the prostate brachytherapy needle positioning project also can be used for DWI registration. Likewise, work on DTI/DWI tractography has been applied to the segmentation of blood vessels and soft plaque detection in the coronary arteries.

Year five progress with respect to the current DBPs is relevant to the scope of this Annual Progress Report. As mentioned, we currently have three projects in the area of neuropsychiatric disorders: Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill). A fourth project from Johns Hopkins and Queens Universities involves the application of core technologies to imaging/robotics-guided treatments in prostate cancer. A number of papers have been published that specifically acknowledge the NA-MIC, and significant software development is continuing as well.

Section 3 outlines specific aims fulfilled this year by the four roadmap projects: Section 3.1 describes the Stochastic Tractography Approach for Velocardiofacial Syndrome; Section 3.2 details the application of our work to Brachytherapy Needle Positioning for the Prostate; Section 3.3 outlines the Brain Lesion Analysis in Neuropsychiatric Systemic Lupus Erythematosus project; and Section 3.4 documents the Cortical Thickness for Autism project. For all of these projects, a synergism of effort has produced working computer modules that are user friendly and accessible to both medical researchers and clinicians.

Section 4 describes year five work on the four infrastructure topics. These include: Diffusion Image Analysis (Section 4.1), Structural Analysis (Section 4.2), Functional MRI Analysis (Section 4.3), and the NA-MIC Toolkit (Section 4.4). Many of the algorithms produced by Cores 1-3 have been integrated into ITK and Slicer, including those concerning shape analysis (e.g., spherical wavelets), new segmentation algorithms (for DTI/DWI tractography and the segmentation of the prostate), and new approaches to registration (e.g., based on particle filtering).

Finally, the last three sections of this Annual Progress Report highlight some of the work that the the Scientific Leadership believes is particularly significant to the the overall goals of the Center. Section 5 summarizes the benefits of several advanced algorithms, gives a description of the growing NAMIC-Toolkit, and documents the scope of our efforts in technology transfer and outreach. It is essential to emphasize that although the algorithms emanating from this Center were developed to solve specific clinical problems raised by the DBPs, in application, most of these algorithms have far more general utility and far greater potential impact on the medical imaging technical base. To this end, Section 6 draws attention to the impact and value of our work on biocomputing imaging at three different levels: within the Center, within the NIH-funded research community, and externally to the national and international community. To further illustrate the impact of our work, Section 7 provides some updated timelines with specific milestones achieved by the various NA-MIC cores. Section 8 lists publications pertinent to the current reporting period that acknowledge NA-MIC support, and Section 9 provides the External Advisory Report and our considered response.

=Clinical Roadmap Projects=
==Roadmap Project: Stochastic Tractography for VCFS (Kubicki)==
===Overview (Kubicki)===
The goal of this project is to create an end-to-end application that is useful in evaluating anatomical connectivity between segmented cortical regions of the brain. The ultimate goal of our program is to understand similarities and differences in anatomical connectivity between genetically related schizophrenia and velocardio-facial syndrome. Thus we plan to use the "stochastic tractography" tool for the analysis of abnormalities in integrity or connectivity in the arcuate fasciculus fiber bundle that is involved in language processing in schizophrenia and VCFS.

===Algorithm Component (Golland)===
The core science involved in this project is the Stochastic Tractography algorithm. This algorithm was developed and implemented collaboratively by MIT and BWH. Stochastic Tractography is a Bayesian approach to estimating nerve fiber tracts from images created by diffusion tensor imaging (DTI).

In this approach, the diffusion tensor is used at each voxel in the volume to
construct a local probability distribution for the fiber direction
around the principal direction of diffusion. The tracts then are sampled
between two user-selected regions of interest (ROIs), by simulating a random walk between
the regions, based the local transition probabilities inferred from
the DTI image.

The resulting collection of fibers and the associated FA values
provide useful statistics on the properties of connections between the
two regions. To constrain the sampling process to the relevant white
matter region, atlas-based segmentation is used to label ventricles and
gray matter and to exclude them from the search space. This latter
step relies heavily on the registration and segmentation functionality
of Slicer.

Over the last year, we have been working on applying several pre- and postprocessing steps to the algorithm pipeline. These steps include "eddy current" and "geometric distortion correction," which were made available to us by the Utah group, as well as "DTI filtering" (BWH). White matter masks now also can be created based on T2 thresholding within the Slicer Stochastic Tractography module. These masks are more precise, as they do not rely on MRI-to-DTI co-registration.

We also have been working on the datasets that apply to situations where fMRI activations as well as gray matter segmentations need to be registered to DTI data to permit seeding within predefined gray matter regions. Significant progress has been made in modality registration, and additional improvement is expected when "geometric distortion correction" becomes part of the analysis pipeline.

Finally, we have been working on improved ways to visualize and quantify Stochastic Tractography output, not only by parametrizing fiber tracts, but also by creating connection probability distribution maps.

===Engineering Component (Davis)===
This year, the Stochastic Tractography Slicer module was rewritten in Python. The new module was released in December, 2008, and presented at the All Hands Meeting in Salt Lake City. The module is now a functional component of Slicer3. Documentation for operating the module also has been created to facilitate user training. Current Engineering efforts are focused on maintaining the module, optimizing the module for use with other data formats, and adding new functionality, such as better registration, distortion correction, and ways of extracting and measuring functional anisotropy (FA) along nerve fiber tracts.

The datasets used with the Stochastic Tractography module are computationally demanding. They involve higher spatial resolutions and many more diffusion directions as compared with White Matter Tractography, which was used previously. As well, the cortical ROIs tend to be much larger than white matter ROIs. Hence, there is a pressing need for performance improvement. This need can be appreciated by examining the differences between Stochastic Tractography, where literally hundreds of tracts are generated from a single seed, and Deterministic Tractography, where only a single tract is generated. Thus, some effort has been made to economize by use of multi-threading and parallel processing. A version of the Stochastic Tractography algorithm that uses large computer clusters also has been developed and can be downloaded and installed by individual users with minimal knowledge of parallel computing.

===Clinical Component (Kubicki)===
In this reporting period, we have designed, implemented, or completed several studies to test the Stochastic Tractography algorithm on the newly released 3T NA-MIC data. These datasets consist of high resolution DTI, structural RM data, and automatic anatomical segmentations. Since these data already have been co-registered, cortical ROIs can be used as seeding points for Stochastic Tractography.

The first of these clinical studies was designed to analyze the connections between the inferior frontal and superior temporal lobes, which represent important sites of the language network. The connections between these two regions were measured via Stochastic Tractography in a group of 20 chronic schizophrenia patients and 20 controls and then subjected to comparative analysis. We also examined gray matter volume in destination regions and attempted to estimate the relationship between gray and white matter abnormalities in schizophrenia. The results of this study were presented at the World Psychiatry Congress in Florence, Italy in April, 2009, and later that same month at the Harvard Psychiatry MYSELL conference.

Another current endeavor is the application of Stochastic Tractography to define the connections involved in emotional processing. [For this study, we are using cortical segmentations of the anterior cingulated gyrus, orbital-frontal gyrus, and amygdala and trace as well as quantify connections between these regions in healthy controls versus schizophrenia patients.] The results of this preliminary study were presented at MYSELL in April, 2009. Another presentation will be made at the Biological Psychiatry conference later this year.

We have also been involved in two collaborative efforts. The first involves the use of DTI data acquired at UCI. In this study, we have used the stochastic method to segment and measure the arcuate fasciculus in subjects with schizophrenia and language impairment, as evidenced in ERP data. In another collaboration, we are combining resting state fMRI data with DTI to measure connectivity between regions that form a functional network. Both of these projects are currently under way.

Finally, a paper that discusses the qualitative use of Stochastic Tractography has been accepted for publication in Human Brain Mapping and is currently in press. Here, when we combined fMRI with DTI whole brain data analysis, we identified certain regions that expressed abnormal functional connectivity in schizophrenia. These regions were then assigned to certain anatomical structures (white matter tracts) based on their location and relationship to Stochastic Tractography output.

===Additional Information===
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:Harvard:Brain_Segmentation_Roadmap here on the NA-MIC wiki].
==Roadmap Project: MR-guided Prostate Biopsy Needle Positioning Robot Integration (Fichtinger)==
===Overview (Fichtinger)===
Numerous studies have demonstrated the efficacy of image-guided needle-based therapy and biopsy in the management of prostate cancer. However, the accuracy of traditional prostate interventions that rely on transrectal ultrasound (TRUS) is limited by image fidelity, needle template guides, needle deflection, and tissue deformation. Magnetic Resonance
Imaging (MRI) is an ideal modality for guiding and monitoring such interventions because it provides excellent visualization of the prostate, its sub-structure, and surrounding tissues.

We have designed a comprehensive robotic assistant system that permits prostate biopsy and brachytherapy procedures to be performed entirely inside a 3T closed MRI scanner. The current system applies the transrectal approach to the prostate. With this approach, an endorectal coil and steerable needle guide, both tuned to 3T magnets and invariably to any particular scanner, are integrated into the MRI compatible manipulator.

Under the NA-MIC initiative, the interface between image computing, visualization, intervention planning, and kinematic planning is being managed by an open-source system built on the NA-MIC toolkit and its components, that is, Slicer3 and ITK. These tools are complemented by a collection of unsupervised prostate segmentation and registration methods that are important to the clinical performance of the interventional system as a whole.

===Algorithm Component (Tannenbaum)===

Our group (GaTech) has worked on both the segmentation and registration of the prostate from MRI and ultrasound data. This process is described below.

'''Prostate Segmentation'''

The first step is to "extract" the prostate. We have provided two methods: a shape-based method and a semi-automatic method. More details are given below and images and further details may be found [http://www.na-mic.org/Wiki/index.php/Projects:ProstateSegmentation here]

# ''A shape-based algorithm''. This process begins by learning a group of shapes, obtained by manually segmenting a set of prostate 3D images. With the shapes represented as the hyperbolic tangent of the signed distance functions, principle component analysis is employed to learn the shapes. Further, given a new prostate image, we search the learned shape space in order to find one shape best segment the given image. The fitness of one shape to segment the image is evaluated by an energy functional measuring the discrepancy of the statistical characteristics inside and outside the current segmentation boundary. Such method is robust to the noise in the images. Moreover, the whole algorithm pipeline has been integrated into the Slicer3 through the command line module.
# ''Semi-automatic method''. This method is based on a random walk segmentation algorithm. With user provided initial seed regions inside and out side the object (prostate), the algorithm computes a probability distribution over the image domain by solving a boundary value partial differential equation where the value at seed regions are fixed at 1.0 or 0.0, depending or whether they are object or background seeds. The resulting distribution indicates the probability of each voxel belonging to the object. Simply threshold by 0.5 gives the segmentation of the object. Moreover, if the result is not suitable, the user can edit the seed regions, and the new result is computed based on this previous result. This algorithm has been integrated into the transrectal prostate MRI module of Slier3.

'''Prostate Registration'''

We developed a nonlinear (affine) prostate registration method by treating prostate images as point sets. Then the iterative closest point algorithm is improved to register the point sets generated by the two images to be registered. The proposed method shows robustness to long distance transition and partial image structure. Moreover, such representation is much sparser than sampling image on the uniform grid thus the registration is very fast comparing two 3D volumetric
image registration.

Furthermore, the registration is viewed as a posterior estimation problem, in which the distributions of the affine and translation parameters are to be estimated. This can naturally be estimated using a particle filter framework. Through this, the method can handle the otherwise difficult cases where the two prostates are one supine and
one prone.

More details are given [[Projects:pfPtSetImgReg|here...]]

===Engineering Component (Hayes)===

An end-to-end slicer loadable module that interfaces with MRI-compatible robotic device to perform MRI guided prostate biopsy has been developed. A complete end-user tutorial documentation has been uploaded on the project wiki-page together with sample tutorial dataset (phantom) to facilitate user training. This is one of the first modules that uses Slicer as an interventional tool contrary to traditional usage as a post-processing tool.

This has been an year of ideas to implementation. The design hatched, was realized during this year. Specifically speaking, following functionality was implemented.

An intuitive workflow based GUI, that clearly identifies four phases of the intervention (Device calibration/registration, prostate segmentation, targeting, and verification) guides the user through the process.

The robotic device calibration/registration to scanner coordinates is achieved by means of segmenting fiducial markers in images. The registration parameters are used in targeting step for calculation of targeting parameters and needle tarjectory. The robotic device's optical encoders are interfaced, these sensors continuously sense the device rotation and needle angle, and are sent over USB interface, our module reads these on a 500 msec timer event.

The prostate segmentation algorithm developed by our algorithm core collaborators at Georgia Tech was integrated during NAMIC programming at Utah this year. The details are provided in previous sub-section.

The targeting step enables user to pick anatomic locations of interest by just clicking in any of 3 slice views. 3D Slicer's arbitrary reformat plane widget is an attractive feature which enables user/clinician to visualize and pick target in any desired plane. When a target is picked, the targeting parameters (device rotation and needle angle) for the device to hit the intended target are calculated and updated in the list of targets. Multiple targets can be picked and associated with a particular type of needle (e.g. biopsy or seed). Once a particular target is selected from the list of targets, the selected target is brought into view in all three slice views and highlighted in the 3D view, the information about the target and targeting parameters is displayed. Further, the robot's needle trajectory to the target is also visualized in 3D; this is a very crucial feedback for the clinician.

The biopsy is performed, and a validation volume is acquired while needle still in the prostate; this validation volume is then used to perform validation analysis, to find out how accurately did the device hit the target.

We've sought and received timely help from the engineering core. A couple of times, some functionality in Slicer 'core' was implemented for our specific requirements. The current engineering efforts are focussed on testing module at various levels, detecting and fixing bugs. We are in the process of designing a test protocol for functional and clinical evaluation of the software. Efforts are also on to add functionality e.g. additional dedicated MR room display window which will display the chosen 2D image view for particular target, the required robot targeting paramaters, and the sensed robot parameters, load previously saved experiment for post-op analysis, visualizing probe coverage.

===Clinical Component (Fichtinger)===
<Note Progress in the last year>

===Additional Information===
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:JHU:Roadmap here on the NA-MIC wiki].
==Roadmap Project: Brain Lesion Analysis in Neuropsychiatric Systemic Lupus Erythematosus (Bockholt)==
===Overview (Bockholt)===
The primary goal of the MIND DPB is to examine changes in white matter lesions in adults with Neuropsychiatric Systemic Lupus Erythematosus (SLE). We want to be able to characterize lesion location, size, and intensity, and would also like to examine longitudinal changes of lesions in an SLE cohort. To accomplish this goal, we will create an end-to-end application entirely within NA-MIC Kit allowing individual analysis of white matter lesions. Such a workflow will then be applied to a clinical sample in the process of being collected.

===Algorithm Component (Whitaker)===
The basic steps necessary for the white matter lesion analysis application entail first registration of T1, T2, and FLAIR images, second tissue classification into gray, white, csf, or lesion, thirdly clustering lesion for anatomical localization, and finally a summarization of lesion size and image intensity parameters within each unique lesion.

<Note Progress in the last year>

===Engineering Component (Pieper)===

At the [http://www.na-mic.org/Wiki/index.php/2009_Winter_Project_Week January 2009 NA-MIC project week] a first pass of a lesion segmentation tutorial was provided to the community. This was the first end-to-end workflow for this project and represented a significant step in the project. Based on feedback from the community and the target clinical users of these tools, we identified additional steps to improve the system.

The primary engineering effort has been directed to the following projects:

'''Interface Improvements'''

We have begun to look at the creation of a [[2009_Winter_Project_Week:HighLevelWizard| high level wizard]] as a front end to the processing task. This interface would allow users to go through the steps without directly navigating the slicer modules and can also provide state management that will simplify the visualization efforts.

'''Modularity and Deployment'''

The current tutorial has been difficult for some users to implement due to the requirement that the lesion detection module be compiled locally on the user's machine. Non-developers understandably find this to be a difficult requirement, so we are integrating the lesion segmentation code into the [http://www.slicer.org/slicerWiki/index.php/Slicer3:Loadable_Modules:Status Slicer3 loadable module project] so that pre-compiled versions of the module are available for users. To implement this, we are following the templates provided by the [http://www.nitrc.org/projects/slicer3examples/ slicer example modules] provided on the nitrc.org website. This infrastructure was created via a supplement to NA-MIC provided by the NITRC project. The [http://www.nitrc.org/projects/lupuslesion/ project page on nitrc.org] is being updated as new features are added to the modules.

'''Core Implementation Support'''

During this period we have also worked on [[2009_Winter_Project_Week:LesionSegmentationEfficiency|optimizing the implementation]] of the core ITK code. This effort has primarily been accomplished by the MIND group, with interactions as needed with the rest of the NA-MIC community.

In addition, ongoing discussions with the rest of the NA-MIC community are encouraging code sharing among projects through modularization of common processing tasks and development of 'best of breed' routines for lesion detection and quantification. These tools are then embodied as slicer modules for use in other applications such as brain tumor change tracking.

===Clinical Component (Bockholt)===
<Note Progress in the last year>
===Additional Information===
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:MIND:Roadmap here on the NA-MIC wiki].
==Roadmap Project: Cortical Thickness for Autism(Hazlett)==
===Overview (Hazlett)===

A primary goal of the UNC DPB is to examine changes in cortical thicknes in children with autism compared to typical controls. We want to examine group differences in both local and regional cortical thickness, and would also like to examine longitudinal changes in the cortex from ages 2-4 years. To accomplish this goal, this project will create an end-to-end application within Slicer3 allowing individual and group analysis of regional and local cortical thickness. Such a workflow will then be applied to our study data (already collected).

===Algorithm Component (Styner)===

The basic steps necessary for the cortical thickness application entail first tissue segmentation in order to separate white and gray matter regions, second cortical thickness measurement, thirdly cortical correspondence to compare measurements across subjects and finally a statistical analysis to locally compute group differences.

<Note Progress in the last year>

===Engineering Component (Miller, Vachet)===

<Note Progress in the last year>

===Clinical Component (Hazlett)===
<Note Progress in the last year>

===Additional Information===
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:UNC:Cortical_Thickness_Roadmap here on the NA-MIC wiki].

=Four Infrastructure Topics=
==Diffusion Image Analysis (Gerig)==
<Note Progress in the last year>
===Key Investigators===

<Need to update the list below>

* BWH: Marek Kubicki, Martha Shenton, Sylvain Bouix, Julien von Siebenthal, Thomas Whitford, Jennifer Fitzsimmons, Doug Terry, Jorge Alverado, Eric Melonakos, Carl-Fredrik Westin.
* MIT: Lauren O'Donnell, Polina Golland
* UCI: James Fallon, Judi Ford
* Utah I: Tom Fletcher, Ross Whitaker, Ran Tao, Yongsheng Pan
* Utah II: Casey Goodlett, Sylvain Gouttard, Guido Gerig
* GA Tech: John Melonakos, Vandana Mohan, Shawn Lankton, Allen Tannenbaum
* GE: Xiaodong Tao, Jim Miller, Mahnaz Mandah
* UCLA: Nathan Hageman
* Isomics: Steve Pieper
* Kitware: Luis Ibanez

===Additional Information===
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:DiffusionImageAnalysis here on the NA-MIC wiki].

==Structural Analysis(Tannenbaum)==
===Progress===
Under Structural Analysis, the main topics of research for NAMIC are structural segmentation, registration techniques and shape analysis. These topics are correlated and hence research in one often finds application in another. For example, shape analysis can yield useful priors for segmentation, or segmentation and registration can provide structural correspondences for use in shape analysis and so on.

An overview of selected progress highlights under these broad topics follows:

Segmentation

* Geodesic Tractography Segmentation: We proposed an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. This has been applied successfully to the segmentation of neural fiber bundles such as the Cingulum Bundle. This framework has now been integrated into Slicer and is being tested on a population of brain data sets.

* Tubular Surface Segmentation: We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. We have also developed the moving end points implementation of this framework wherein the required input is only a few points in the interior of the structure of interest. This yields the additional advantage that the framework simulatenously returns both the 3D segmentation and the 3D skeleton of the structure eliminating the need for apriori knowledge of end points, and an expensive skeletonization step. The framework is applicable to different tubular anatomical structures in the body. We have so far applied it successfully to the Cingulum Bundle, and blood vessels.

* Local-global Segmentation: We have proposed a novel segmentation approach that combines the advantages of local and global approaches to segmentation, by using statistics over regions that are local to each point on the evolving countour. This makes it well suited to applications with contrast differences within the structure of interest such as in blood vessel segmentation, as well as applications like the neural fiber bundles where the diffusion profiles of voxels within the structure are locally similar but vary along the length of the fiber bundle itself.

* Shape-based segmentation: 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 presented 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. We have applied the algorithm successfully to real MRI images, and we have also implemented it into 3D Slicer.

* Re-Orientation Approach for Segmentation of DW-MRI: This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. Segmentation is achieved through simple global statistical modeling of diffusion orientation which allows for a convex optimization formulation of the segmentation problem, combining orientation statistics and spatial regularization. The approach compares very favorably with segmentation by full-brain streamline tractography.

Registration

* Optimal Mass Transport based Registration: We have provided a computationaly efficient non-rigid/elastic image registration algorithm based on the Optimal Mass Transport theory. We use the Monge-Kantorovich formulation of the Optimal Mass Transport problem and implement the solution proposed by Haker et al. using multi-resolution and multigrid techniques to speed up the convergence. We also leverage the computation power of general purpose graphics processing units available on standard desktop computing machines to exploit the inherent parallelism in our algorithm. We extend the work by Haker et al. who compute the optimal warp from a first order partial differential equation, an improvement over earlier proposed higher order methods and those based on linear programming, and further implement the algorithm using a coarse-to-fine strategy resulting in phenomenol improvement in convergence. We have applied it successfully to the registration of 3D brain MRI datasets (preoperative and intra-operative), and are currently extending it to the non-rigid registration of baseline DWI to brain MRI data.

* Atlas Regularization for Image Segmentation: Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies.

* Point-set Rigid Registration: We have proposed a particle filtering scheme for the registration of 2D and 3D point set undergoing a rigid body transformation. Moreover, we incorporate stochastic dynamics to model the uncertainity of the registration process. We treat motion as a local variation in the pose parameters obatined from running a few iterations of the standard Iterative Closest Point (ICP) algorithm. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence as well as provide a dynamical model of uncertainity. In contrast with other techniques, our approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temoral coherency of the state (no loss of information). Also, unlike most alternative approaches for point set registration, we make no geometric assumptions on the two data sets.We applied the algorithm to different alignments of point clouds and it successfully found the correct optimal transformation that aligns two given point clouds despite the differing geometry around the local neighborhood of a point within their respective sets.

* Regularization for Optimal Mass Transport: To extend the flexibility of the existing OMT algorithm, we added a regularization term to the functional being minimized. This term controls the tradeoff between how well two images match after registration versus how warped the transformation map can become. A weighted sum of squared differences is used to penalize having to move mass over long distances; this addition also helps to keep the transformation physically accurate by reducing the likelihood that the transformation grid will fold over itself and keeping the grid smooth.

* Registration of DW-MRI to structural MRI: Optimal Mass Transport was applied to the problem of correcting EPI distortion in DW-MRI. A mask for white matter in DW-MRI was registered to the white matter mask extracted from the structural MRI for the same patient. Prior to registration, it is important to normalize intensities in the two masks; this was done by dividing the images into regions and uniformly normalizing over each region to assure the sum of the intensities is equal. Then, once a transformation between the white matter masks was calculated, this transformation was applied to the original DW-MRI image.

Shape Analysis

* Shape Analysis Framework using SPHARM-PDM: We have provided an analysis framework of objects with spherical topology, described by sampled spherical harmonics SPHARM-PDM. The input is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are first processed to fill any interior holes. The processed binary segmentations are converted to surface meshes, and a spherical parametrization is computed for the surface meshes using a area-preserving, distortion minimizing spherical mapping. The SPHARM description is computed from the mesh and its spherical parametrization. Using the first order ellipsoid from the spherical harmonic coefficients, the spherical parametrizations are aligned to establish correspondence across all surfaces. The SPHARM description is then sampled into a triangulated surfaces (SPHARM-PDM) via icosahedron subdivision of the spherical parametrization. These SPHARM-PDM surfaces are all spatially aligned using rigid Procrustes alignment. Group differences between groups of surfaces are computed using the standard robust Hotelling T 2 two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. We provide additional visualization of the group tests via mean difference magnitude and vector maps, as well as maps of the group covariance information. We have a stable implementation, and current development focuses on integrating the current command line tools into Slicer (v3) via the Slicer execution model.

* Population studies using Tubular Surface Model: We have proposed a tubular shape model for the Cingulum Bundle which models a tubular surface as a center-line coupled with a radius function at every point along the center-line. This model shows potential for population studies on the Cingulum Bundle which is believed to be involved in Schizophrenia, since it provides a natural way of sampling the structure to build a feature representation of it. We are currently segmenting the Cingulum Bundle from a population of brain data sets, towards performing this population analysis using the Pott's Model.

* Automatic Outlining of Sulci on a Brain Surface: We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain, where the data is taken to be a 3D triangulated mesh formed from the segmentation of MR image slices. The problem is posed as energy minimization using penalizing the arc-length of segmenting curve using conformal factor involving the mean curvature of the underlying surface. The computation is made practical for dense meshes via the use of a sparse-field method to track the level set interfaces and regularized least-squares estimation of geometric quantities.

===Key Investigators===

Needs to be updated:

* MIT: Polina Golland, Kilian Pohl, Sandy Wells, Eric Grimson, Mert R. Sabuncu
* UNC: Martin Styner, Ipek Oguz, Xavier Barbero
* Utah: Ross Whitaker, Guido Gerig, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer
* GaTech: Allen Tannenbaum, John Melonakos, Vandana Mohan, Tauseef ur Rehman, Shawn Lankton, Samuel Dambreville, Yi Gao, Romeil Sandhu, Xavier Le Faucheur, James Malcolm, Ivan Kolosev
* Isomics: Steve Pieper
* GE: Bill Lorensen, Jim Miller
* Kitware: Luis Ibanez, Karthik Krishnan
* UCLA: Arthur Toga, Michael J. Pan, Jagadeeswaran Rajendiran
* BWH: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt, Yogesh Rathi, Marek Kubicki, Steven Haker

===Additional Information===
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:StructuralImageAnalysis here on the NA-MIC wiki].
==fMRI Analysis (Golland)==
===Progress===

* Connectivity Analysis:

One of the major goals in analysis of fMRI data is the detection of functionally homogeneous networks in the brain. We developed a new method for characterizing functional connectivity patterns from fMRI. In contrast to the seed-based analysis typically employed to identify networks of co-activation, we propose to use clustering to simultaneously estimate the networks and their representative time courses, which effectively replace user-specified seeds.
During this year, we validated this method for characterizing functional connectivity patterns from fMRI. To investigate the sensitivity of the analysis to the generative model of the signal, we implemented and compared two distinct algorithms, the mixture-model clustering and the spectral clustering, in application to this problem. We validated our approach in a rest state fMRI scans of 45 healthy subjects. Our results demonstrate that the detected networks are stable across subjects and across methods.
At the same time, we worked with the Harvard DBP to identify relevant clinical data sets in which our approach promises to identify effect of a disorder. We have started a collaboration to apply the method to a group of schizophrenia patients and normal controls.

* Distortion Correction for EPI-Based Functiona Imaging

We developed and demonstrated a method for correcting the distortions present in echo planar images (EPI) and registering the EPI image to structural MRI scans. Our approach does not require acquiring fieldmaps, modifying EPI acquisition parameters, or having detailed knowledge of the shim system. The technique consists of two steps. First, a classifier is used to segment structural MR into an air/tissue susceptibility model. The resulting tissue map serves as input to a first order perturbation field model to compute a subject-specific fieldmap. The classifier is trained based on MR-CT image pairs, using MR intensities as features and exploiting air segmentation in the CT images to construct labels. Second, a simultaneous shim estimation and registration algorithm is employed to solve for the lower order field perturbations (shim parameters) needed to accurately unwarp and register the EPI data.

===Key Investigators===

* MIT: Polina Golland, Danial Lashkari,Archana Venkataraman, Clare Poynton
* Harvard/BWH: Sylvain Bouix, Martha Shenton, Marek Kubicki, Carl Frederick Westin, Sandy Wells

===Additional Information===
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:fMRIAnalysis here on the NA-MIC wiki].

==NA-MIC Kit Theme (Schroeder)==
===Progress===
The NAMIC-Kit consists of a framework of advanced computational components, as well as the support infrastructure for testing, documenting, and deploying leading edge medical imaging algorithms and software tools. The framework has been carefully constructed to provide low-level access to libraries and modules for advanced users, plus high-level application access that non-computer professionals can use to address a variety of problems in biomedical computing. In this fifth year of the NA-MIC projects <summary of progress>

===Software Releases===
The NAMIC-Kit can be represented as a pyramid of capabilities, with the base consisting of toolkits and libraries, and the apex standing in for the Slicer3 user application. In between, Slicer modules are stand-alone executables that can be integrated directly into the Slicer3 application, including GUI integration, while work-flows are groups of modules that are integrated together to manifest sophisticated segmentation, registration and biomedical computing algorithms. In a coordinated NAMIC effort, major releases of these many components were realized over the past year. This includes, but is not limited to:
*
*

===Slicer3 and the Software Framework===
One of the major achievements of the past year has been the release of [http://www.slicer.org/slicerWiki/index.php/Documentation-3.4 version 3.4 of 3D Slicer] in May of 2009. A number of important improvements have been made by the Engineering Core and significant new functionality has been added throug other NA-MIC cores and collaborators since the release of version 3.2 in August of 2008. A few notable examples include:

* [http://www.slicer.org/slicerWiki/index.php/Modules:Saving-Documentation-3.4 An Integrated Data Save Dialog]
* [http://www.slicer.org/slicerWiki/index.php/Modules:Fiducials-Documentation-3.4 Significant Rework of the Fiducials Interface]
* [http://www.slicer.org/slicerWiki/index.php/Modules:Slices-Documentation-3.4 A Slices Module to Support Advanced Visualization Modes]
* [http://www.slicer.org/slicerWiki/index.php/Modules:Editor-Documentation Significant Improvements to the Interactive Label Map Editor]
* [http://www.slicer.org/slicerWiki/index.php/Modules:ChangeTracker-Documentation-3.4 Integration of a Brain Tumor Change Tracking Module in collaboration with the Brain Science Foundation]
* [http://www.slicer.org/slicerWiki/index.php/Modules:IA_FEMesh-Documentation-3.4 Integration of a Finite Element Meshing Module as a deliverable of the NA-MIC Collaboration Grant at the University of Iowa]
* [http://www.slicer.org/slicerWiki/index.php/Modules:FetchMI-Documentation-3.4 A Medical Informatics Interface to XNAT in collaboration with BIRN]
* [http://www.slicer.org/slicerWiki/index.php/Slicer3:Python The Ability to Interactively Script Slicer in Python as well as Tcl]

In addition, there have been major extensions to the diffusion imaging tools, registration tools, filters, image guided therapy, and other core changes that enhance the utility and applicability of the software.

===Software Process===
One of the challenges facing developers has been the requirement to implement, test and deploy software systems across multiple computing platforms. NAMIC continues to push the state of the art with further development of the CMake, CTest, and CPack tools for cross-platform development, testing, and packaging, respectively...

===Key Investigators===
THis list needs to be updated:

* Kitware - Will Schroeder (Core 2 PI), Sebastien Barre, Luis Ibanez, Bill Hoffman
* GE - Jim Miller, Xiaodong Tao
* Isomics - Steve Pieper, Alex Yarmarkovich, Curt Lisle, Terry Lorber

===Additional Information===
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC-Kit here on the NA-MIC wiki].

=Highlights(Schroeder)=
===Advanced Algorithms===

===NAMIC-Kit===

===Outreach and Technology Transfer===
Cores 4-5-6 continue to support, train and dissemniate to the NAMIC community, and the broader biomedical computing community.
* The Slicer community held several workshops and tutorials. In xxx a satellite event was held for the international Organization for Human Brain Mapping at the annual meeting in xxx. The xx workshop on xx hosted xx participants representing xx countries from around the world, xx states within the US and xxdifferent laboratories including xx NIH institutes. In addition, <note how many slicer tutorials were held and where etc>
* Project Week continues to be a successful NAMIC venue. These semi-annual events are held in Boston in June, and January in Salt Lake City. These events are well attended with approximately 100 participants, of which about a third are outside collaborators. At the last Project Week in Salt Lake City, approximately xx projects were realized.
* NAMIC continues to participate in conferences and other technical venues. For example, NAMIC hosted xxx

=Impact and Value to Biocomputing (Miller)=
NA-MIC impacts Biocomputing through a variety of mechanisms. First,
NA-MIC produces scientific results, methodologies, workflows,
algorithms, imaging platforms, and software engineering tools and
paradigms in an open enviroment that contributes directly to the body of
knowledge available to the field. Second, NA-MIC science and
technology enables the entire medical imaging community to build on
NA-MIC results, methods, and techniques, to concentrate on the new
science instead of developing supporting infrastructure, to leverage
NA-MIC scientists and engineers to adapt NA-MIC technology to new
problem domains, and to leverage NA-MIC infrastructure to distribute
their own technology to a larger community.

===Impact within the Center===

===Impact within NIH Funded Research===

===National and International Impact===

= Timeline (Ross)=

<The table needs to be updated>

This section of the report gives the milestones for years 1 through 5 that are associated with the timelines in the original proposal. We have organized the milestones by core. For each milestone we have indicated the proposed year of completion and a very brief description of the current status. In some cases the milestones include ongoing work, and we have try to indicate that in the status. We have also included tables that list any significant changes to the proposed timelines. On the wiki page, we have links to the notes from the various PIs that give more details on their progress and the status of the milestones.

'''These tables demonstrate that the project is, on the whole, proceeding according to the originally planned schedule.'''

== Core 1: Algorithms ==

=== Timelines and Milestones ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Proposed time of completion'''
| '''Status'''
|-
| '''MIT'''
| 1
| '''Shape-based segmentation'''
|
|
|-
| '''MIT'''
| 1.1
| Methods to learn shape representations
| Year 2
| Completed
|-
| '''MIT'''
| 1.2
| Shape in atlas-driven segmentation
| Year 4
| Completed
|-
| '''MIT'''
| 1.3
| Validate and refine approach
| Year 5
| In Progress
|-
| '''MIT'''
| 2
| '''Shape analysis'''
|
|
|-
| '''MIT'''
| 2.1
| Methods to compute statistics of shapes
| Year 4
| Completed
|-
| '''MIT'''
| 2.3
| Validation of shape methods on application data
| Year 5
| Completed, refinements ongoing
|-
| '''MIT'''
| 3
| '''Analysis of DTI data'''
|
|
|-
| '''MIT'''
| 3.1
| Fiber geometry
| Year 3
| Completed
|-
| '''MIT'''
| 3.2
| Fiber statistics
| Year 5
| Completed, new developments ongoing
|-
| '''MIT'''
| 3.3
| Validation on real data
| Year 5
| Completed, refinements ongoing
|-
| '''Utah'''
| 1
| '''Processing of DTI data'''
|
|
|-
| '''Utah'''
| 1.1
| Filtering of DTI
| Year 2
| Completed
|-
| '''Utah'''
| 1.2
| Quantitative analysis of DTI
| Year 3
| Completed, refinements ongoing
|-
| '''Utah'''
| 1.3
| Segmentation of cortex/WM
| Year 3
| Completed partially, modified below
|-
| '''Utah'''
| 1.4
| Segmentation analysis of white matter tracts
| Year 3
| Completed, applications ongoing
|-
| '''Utah'''
| 1.5
| Joint analysis of DTI and functional data
| Year 5
| Initiated
|-
| '''Utah'''
| 2
| Nonparametric Shape Analysis
| Year 5
| Completed
|-
| '''Utah'''
| 2.1
| Framework in place
| Year 3
| Complete
|-
| '''Utah'''
| 2.2
| Demonstration on shape of neuranatomy (from Core 3)
| Year 4
| Complete
|-
| '''Utah'''
| 2.3
| Development for multiobject complexes
| Year 4
| Complete
|-
| '''Utah'''
| 2.4
| Demonstration of NP shape representations on clinical hypotheses from Core 3
| Year 5
| Complete, publications in progress
|-
| '''Utah'''
| 2.6
| Integration into NAMIC-kit
| Year 5
| Incomplete (initiated)
|-
| '''Utah'''
| 2.7
| Shape regression
| Year 5
| Incomplete
|-

|-
| '''UNC'''
| 1
| '''Statistical shape analysis'''
|
|
|-
| '''UNC'''
| 1.1
| Comparative anal. of shape anal. schemes
| Year 2
| Completed
|-
| '''UNC'''
| 1.3
| Statistical shape analysis incl. patient variable
| Year 5
| Complete, refinements ongoing
|-
| '''UNC'''
| 2
| '''Structural analysis of DW-MRI'''
|
|
|-
| '''UNC'''
| 2.1
| DTI tractography tools
| Year 4
| Completed
|-
| '''UNC'''
| 2.2
| Geometric characterization of fiber tracts
| Year 5
| Completed
|-
| '''UNC'''
| 2.3
| Quant. anal. of diffusion along fiber tracts
| Year 5
| Completed.
|-
| '''GaTech'''
| 1.1
| ITK Implementation of PDEs
| Year 2
| Completed
|-
| '''GaTech'''
| 1.1
| Applications to Core 3 data
| Year 4
| Completed
|-
| '''GaTech'''
| 1.2
| New statistic models
| Year 4
| Completed
|-
| '''GaTech'''
| 1.2
| Shape anaylsis
| Year 4
| Completed, refinements ongoing
|-
| '''GaTech'''
| 2.0
| Integration in to Slicer
| Year 4-5
| Preliminary results and ongoing
|-
| '''MGH'''
| 1
| '''Registration'''
|
|
|-
| '''MGH'''
| 1.1
| Collect DTI/QBALL data
| Year 2
| Completed
|-
| '''MGH'''
| 1.2
| Develop registration method
| Year 2
| Completed
|-
| '''MGH'''
| 1.3
| Test/optimize registration method
| Year 3
| In Progress
|-
| '''MGH'''
| 1.4
| Apply registration on core 3 data
| Year 5
| In Queue
|-
| '''MGH'''
| 2
| '''Group DTI Statistics'''
|
|
|-
| '''MGH'''
| 2.1
| Develop group statistic method
| Year 2
| Partially Complete
|-
| '''MGH'''
| 2.2
| Apply on core 3 data
| Year 5
| In Queue
|-
| '''MGH'''
| 3
| '''Diffusion Segmentation '''
|
|
|-
| '''MGH'''
| 3.1
| Collect DTI/QBALL data
| Year 2
| Completed
|-
| '''MGH'''
| 3.2
| Develop/optimize segmentation algorithm
| Year 3
| Modified
|-
| '''MGH'''
| 3.3
| Integrate w/ tractography
| Year 4
| Modified
|-
| '''MGH'''
| 3.4
| Apply on core 3 data
| Year 5
| Modified
|-
| '''MGH'''
| 4
| '''Group Morphometry Statistics'''
|
|
|-
| '''MGH'''
| 4.1
| Develop/optimize statistics algorithms
| Year 3
| Modified
|-
| '''MGH'''
| 4.2
| Develop GUI for Linux
| Year 3
| Modified
|-
| '''MGH'''
| 4.3
| Slicer integration
| Year 3
| Modified
|-
| '''MGH'''
| 4.4
| Compile application on Windows
| Year 4
| Modified
|-
| '''MGH'''
| 5
| XNAT Desktop
| Years 4-5
|
|-
| '''MGH'''
| 5.1
| Establish requirements for desktop version of XNAT
| Years 4-5
| Complete
|-
| '''MGH'''
| 5.2
| Develop implementation plan for prototype
| Years 4-5
| Complete
|-
| '''MGH'''
| 5.3
| Implement prototype version
| Years 4-5
| Incomplete (in progress)
|-
| '''MGH'''
| 5.4
| Implement alpha version
| Year 5
| Incomplete
|-
| '''MGH'''
| 6
| XNAT Central
| Years 4-5
|
|-
| '''MGH'''
| 6.1
| Deploy XNAT Central, a public access XNAT host
| Years 4-5
| Complete
|-
| '''MGH'''
| 6.2
| Coordinate with NAMIC sites to upload project data
| Years 4-5
| Incomplete (ongoing)
|-
| '''MGH'''
| 6.3
| Continue developing XNAT Central based on feedback from NAMIC sites
| Years 4-5
| Incomplete (ongoing)
|-
| '''MGH'''
| 7
| NAMIC Kit integration
| Years 4-5
|
|-
| '''MGH'''
| 7.1
| Implement web services to exchange data with Slicer, Batchmake, and other client applications
| Years 4-5
| Incomplete (ongoing)
|-
| '''MGH'''
| 7.2
| Add XNAT Desktop to standard NAMIC kit distribution
| Year 5
| Incomplete
|-
|}

=== Timeline Modifications ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Modification'''
|-
| '''MIT'''
| 2.2
| Methods to compare shape statistics
| Removed, the effort refocused on registration necessary for population studies
|-
| '''MIT'''
| 2.4
| Software infrastructure to integrate shape analysis tools into the pipeline for population studies.
| New, morphed into collaboration with XNAT to provide more general population analysis tools. Partially completed.
|-
| '''MIT'''
| 4
| fMRI analysis including local and atlas-based priors for quantifying activation.
| New, partially completed. Refinements in progress. Clinical study with Core 1 is in progress.
|-
| '''Utah'''
| 2.2 (removed)
| Feature-based brain image registration.
| Shift emphasis to shape-based analysis/registration
|-
| '''Utah'''
| 2.1 (removed)
| Cortical filtering and feature detection
| Effort is subsumed by other Core 1 partners (e.g. see MGH/Freesurfer)
|-
| '''Utah'''
| 1.3 (removed)
| Segmentation of cortex/WM
| Effort is subsumed by other Core 1-2 partners (e.g. see EM-Segmenter)
|-
| '''Utah'''
| 3.0 (removed)
| Fast implmentations of PDEs
| Real-time filtering is demphasized in favor of shape/DTI analysis
|-
| '''Utah'''
| 1.5 (added)
| Joint analysis of DTI and functional data
| Opportunities/needs within various collaborations
|-
| '''Utah'''
| 2.1-2.3 (added, in place of cortical analysis)
| Shape analysis
| Nonparametric shape analysis added to address needs of core 3.
|-
| '''Utah'''
| 2.7
| Shape regression
| Extension/completion of framework. Opportunities/needs within various collaborations.
|-
| '''UNC'''
| 1.2
| Develop medially-based shape representation
| Remove
|-
| '''UNC'''
| 1.4
| Develop generic cortical correspondence framework (Years 3-5)
| New
|-
| '''UNC'''
| 2.4
| DTI Atlas Building (Years 2--4)
| New
|-
| '''GaTech'''
| 2.1
| FA analysis
| New
|-
| '''MGH'''
| 4.1 - 4.4
| Group Morphometry Statistics
| Added and then removed, based on personnel changes
|-
| '''MGH'''
| 5-7
| XNAT
| Added to support remote image database capabilities
|}

=== [[Core_1_Timeline_Notes|Core 1 Timeline Notes ]] ===

== Core 2: Engineering ==

=== Core 2 Timelines and Milestones ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Proposed time of completion'''
| '''Status'''
|-
| '''GE'''
| 1
| '''Define software architecture'''
|
|
|-
| '''GE'''
| 1
| Object design
| Yr 1
| Completed
|-
| '''GE'''
| 1
| Identify patterns
| Yr 3
| Patterns for processing scalar and vector images, models, fiducials complete. Patterns for diffusion weighted completed, fMRI ongoing.
|-
| '''GE'''
| 1
| Create frameworks
| Yr 3
| Frameworks for processing scalar and vector images, models, fiducials complete. Frameworks for diffusion weighted completed, fMRI ongoing.
|-
| '''GE'''
| 2
| '''Software engineering process'''
|
|
|-
| '''GE'''
| 2
| Extreme programming
| Yr 1-5
| On schedule, ongoing
|-
| '''GE'''
| 2
| Process automatiion
| Yr 3
| On schedule, ongoing
|-
| '''GE'''
| 2
| Refactoring
| Yr 3
| Complete
|-
| '''GE'''
| 3
| '''Automated quality system'''
|
|
|-
| '''GE'''
| 3
| DART deployment
| Yr 2
| Complete
|-
| '''GE'''
| 3
| Persistent testing system
| Yr 5
| Incomplete
|-
| '''GE'''
| 3
| Automatic defect detection
| Yr 5
| Incomplete
|-
| '''Kitware'''
| 1
| '''Cross-platform development'''
|
|
|-
| '''Kitware'''
| 1
| Deploy environment (CMake, CTest)
| Yr 1
| Complete
|-
| '''Kitware'''
| 1
| DART Integration and testing
| Yr 1
| Complete
|-
| '''Kitware'''
| 1
| Documentation tools
| Yr 2
| Complete
|-
| '''Kitware'''
| 2
| '''Integration tools'''
|
|
|-
| '''Kitware'''
| 2
| File Formats/IO facilities
| Yr 2
| Complete
|-
| '''Kitware'''
| 2
| CableSWIG deployment
| Yr 3
| Complete (integration ongoing)
|-
| '''Kitware'''
| 2
| Establish XML schema
| Yr 4
| Complete, refinements ongoing
|-
| '''Kitware'''
| 3
| '''Technology delivery'''
|
|
|-
| '''Kitware'''
| 3
| Deploy applications
| Yr 1
| Complete (ongoing)
|-
| '''Kitware'''
| 3
| Establish plug-in repository
| Yr 2
| Incomplete
|-
| '''Kitware'''
| 3
| Cpack
| Yr 4-5
| Incomplete
|-
| '''Isomics'''
| 1
| NAMIC builds of slicer
| Years 2--5
| Complete
|-
| '''Isomics'''
| 1
| Schizophrenia and DBP intefaces
| Year 3---5
| Completed (refinements ongoing)
|-
| '''Isomics'''
| 2
| ITK Integration tools
| Year 1---3
| Completed
|-
| '''Isomics'''
| 2
| Experiment Control Interfaces
| Year 2---5
| Migration from LONI to BatchMake Underway
|-
| '''Isomics'''
| 2
| fMRI/DTI algorithm support
| Year 2---5
| Completed DTI, fMRI Ongoing
|-
| '''Isomics'''
| 2
| New DBP algorithm support
| Year 2---5
| Ongoing
|-
| '''Isomics'''
| 3
| Compatible build process
| Year 1---3
| Completed
|-
| '''Isomics'''
| 3
| Dart Integration
| Year 1---2
| Completed (upgrades ongoing)
|-
| '''Isomics'''
| 3
| Test scripts for new code
| Year 2---5
| Ongoing
|-
| '''UCSD'''
| 1
| Grid computing---base
| Year 1
| Completed
|-
| '''UCSD'''
| 1
| Grid enabled algorithms
| Year 3
| First version (GWiz alpha) available - initial integration with Slicer3 and execution model.
|-
| '''UCSD'''
| 1
| Testing infrastructure
| Year 4
| Initiated
|-
| '''UCSD'''
| 2
| Data grid --- compatibility
| Year 2
| Completed
|-
| '''UCSD'''
| 2
| Data grid --- slicer access
| Year 2
| Completed for version 2.6. In progress for Slicer3
|-
| '''UCSD'''
| 3
| Data mediation --- deploy
| Year 1
| Incomplete (modfication below)
|-
| '''UCLA'''
| 1
| Debabeler functionality
| Year 1
| Continued Progress
|-
| '''UCLA'''
| 2
| SLIPIE Interpretation (Layer 1)
| Year 1--Year2
| In Progress
|-
| '''UCLA'''
| 3
| SLIPIE Interpretation (Layer 2)
| Year 1--Year2
| On Schedule
|-
| '''UCLA'''
| 3
| Developing ITK Modules
| Year2
| In Progress
|-
| '''UCLA'''
| 4
| Integrating SRB (GSI-enabled)
| Year2
| Completed
|-
| '''UCLA'''
| 5
| Integrating IDA
| Year2
| Completed
|-
| '''UCLA'''
| 5
| Integrating External Visualization Applications
| Year2
| Completed
|}

=== Core 2 Timeline Modifications ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Modification'''
|-
| '''Isomics'''
| 3
| Data mediation
| Delayed pending integration of databases into NAMIC infractructure
|}

=== [[Core_2_Timeline_Notes|Core 2 Timeline Notes ]] ===

== Core 3: Driving Biological Problems ==

The Core 3 projects submitted R01 style proposals, as specified in the RFA, and did not submit timelines.

== Core 4: Service ==

=== Core 4 Timelines and Milestones ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Proposed time of completion'''
| '''Status'''
|-
| '''Kitware'''
| 1
| '''Implement Development Farms'''
|
|
|-
| '''Kitware'''
| 1
| Deploy platforms
| Yrs 1
| Complete
|-
| '''Kitware'''
| 1
| Communications
| Yrs 1
| Complete, ongoing
|-
| '''Kitware'''
| 2
| '''Establish software process'''
|
|
|-
| '''Kitware'''
| 2
| Secure developer database
| Yr 1
| Complete, ongoing
|-
| '''Kitware'''
| 2
| Collect guidelines
| Yr 1
| Complete
|-
| '''Kitware'''
| 2
| Manage software submission process
| Yr 1
| Complete
|-
| '''Kitware'''
| 2
| Configure process tools
| Yr 1
| Complete
|-
| '''Kitware'''
| 2
| Survey community
| Yr 1
| Complete
|-
| '''Kitware'''
| 3
| '''Deploy NAMIC Tools'''
|
|
|-
| '''Kitware'''
| 3
| Toolkits
| Yr 1
| Complete
|-
| '''Kitware'''
| 3
| Integration tools
| Yr 1
| Complete
|-
| '''Kitware'''
| 3
| Applications
| Yr 1
| Complete
|-
| '''Kitware'''
| 3
| Integrate new computing resources
| Yr 1
| Complete
|-
| '''Kitware'''
| 4
| '''Provide support'''
|
|
|-
| '''Kitware'''
| 4
| Esablish support infrastructure
| Yrs 1--5
| On schedule, ongoing
|-
| '''Kitware'''
| 4
| NAMIC support
| Yr 1
| Complete
|-
| '''Kitware'''
| 5
| Manage NAMIC Software Releases
| Yrs 1--5
| On schedule, ongoing
|}

=== Core 4 Timeline Modifications ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Modification'''
|-
| Kitware
| 2-5
| Various
| Refined/modified the sub aims
|}

=== [[Core_4_Timeline_Notes|Core 4 Timeline Notes ]] ===

== Core 5: Training ==

=== Core 5 Timelines and Milestones ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Proposed time of completion'''
| '''Status'''
|-
| '''Harvard'''
| 1
| '''Formal Training Guidllines'''
|
|
|-
| '''Harvard'''
| 1
| Functional neuroanatomy
| Yr 1
| Complete
|-
| '''Harvard'''
| 1
| Clinical correlations
| Yr 1
| Complete
|-
| '''Harvard'''
| 2
| '''Mentoring'''
|
|
|-
| '''Harvard'''
| 2
| Programming workshops
| Yrs 1-5
| On schedule, ongoing
|-
| '''Harvard'''
| 2
| One-on-one mentoring, Cores 1, 2, 3
| Yrs 1-5
| On schedule, ongoing
|-
| '''Harvard'''
| 3
| '''Collaborative work environment'''
|
|
|-
| '''Harvard'''
| 3
| Wiki
| Yrs 1
| Complete
|-
| '''Harvard'''
| 3
| Mailing lists
| Yrs 1
| Complete
|-
| '''Harvard'''
| 3
| Regular telephone conferences
| Yrs 1-5
| On schedule, ongoing
|-
| '''Harvard'''
| 4
| '''Educational component for tools'''
|
|
|-
| '''Harvard'''
| 4
| Slicer training modules
| Yr 2-5
| Slicer 2.x tutorials complete, Two Slicer 3 tutorials complete, translation of 2.x tutorials to 3 is ongoing and on schedule
|-
| '''Harvard'''
| 5
| '''Demonstrations and hands-on training'''
|
|
|-
| '''Harvard'''
| 5
| Various workshops and conferences
| Yrs 1--5
| On schedule, ongoing
|}

=== Core 5 Timeline Modifications ===

None.

=== [[Core_5_Timeline_Notes|Core 5 Timeline Notes ]] ===

== Core 6: Dissemination ==

=== Core 6 Timelines and Milestones ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Proposed time of completion'''
| '''Status'''
|-
| Isomics
| 1
| Create a collaboration metholdology for NA-MIC
|
|
|-
| Isomics
| 1.1
| develop a selection process
| Yr 1
| Complete
|-
| Isomics
| 1.2
| guidelines to govern the collaborations
| Yr 1-2
| Complete
|-
| Isomics
| 1.3
| Provide on-site training
| Yr 1-5
| Complete for current tools (ongoing for tool refinement)
|-
| Isomics
| 1.4
| develop a web site infrastructure
| Yr 1
| Complete
|-
| Isomics
| 2
| Facilitate communication between NA-MIC developers and wider research community
|
|
|-
| Isomics
| 2.1
| develop materials describing NAMIC technology
| Yr 1-5
| On Schedule
|-
| Isomics
| 2.2
| participate in scientific meetings
| Yr 2-5
| On Schedule
|-
| Isomics
| 2.3
| Document interactions with external researchers
| Yr 2-5
| On Schedule
|-
| Isomics
| 2.4
| Coordinate publication strategies
| Yr 3-5
| On Schedule
|-
| Isomics
| 3
| Develop a publicly accessible internet resource of data, software, documentation, and publication of new discoveries
|
|
|-
| Isomics
| 3.1
| On-line repository of NAMIC related publications and presentations
| Yr 1-5
| On Schedule
|-
| Isomics
| 3.2
| On-line repository of NAMIC tutorial and training material
| Yr 1-5
| On Schedule
|-
| Isomics
| 3.3
| Index and a searchable database
| Yr 1-2
| Done
|-
| Isomics
| 3.4
| Automated feedback systems that track software downloads
| Yr 3
| Done
|}

=== Core 6 Timeline Modifications ===

None.

=== [[Core_6_Timeline_Notes|Core 6 Timeline Notes ]] ===

=Appendix A Publications (Mastrogiacomo)=
A list should be mined from the publications database and attached here in MS word format.

=Appendix B EAB Report and Response (Kapur)=
===EAB Report===

===Response to EAB Report===

2009 Annual Scientific Report

2009-05-14T03:17:56Z

Sidd queens:

Back to [[2009_Progress_Report]]

=Guidelines for preparation=

*[[2009_Progress_Report#Scientific Report Timeline]] - Main point is that May 15 is the date by which all sections below need to be completed. No extensions are possible.
*DBPs - If there is work outside of the roadmap projects that you would like to report, you are welcome to create a separate section for it under "Other".
*The outline for this report is similar to the 2008 and 2007 reports, which are provided here for reference: [[2008_Annual_Scientific_Report]], [[2007_Annual_Scientific_Report]].
*In preparing summaries for each of the 8 topics in this report, please leverage the detailed pages for projects provided here: [[NA-MIC_Internal_Collaborations]].
*Publications will be mined from the SPL publications database. All core PIs need to ensure that all NA-MIC publications are in the publications database by May 15.

=Introduction (Tannenbaum)=

The National Alliance for Medical Imaging Computing (NA-MIC) is now completing its fifth year. The 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, as well as to oversee the training and dissemination of these tools to the medical research community. We are currently in year two of our second set of Driving Biological Projects (DBP), three of which involve diseases of the brain: (a) brain lesion analysis in neuropsychiatric systemic lupus erythematosus; (b) a study of cortical thickness for autism; and (c) stochastic tractography for velocardiofacial syndrome (VCFS). The fourth DBP takes the Center in a very new direction, (d) the prostate: brachytherapy needle positioning robot integration.

Over the past five years, NA-MIC has made substantial progress toward the attainment of its major objectives. In year one, the Center focused on forging alliances amongst its various cores and constituent groups to assure that the efforts of the cores were well integrated toward the attainment of common and specific goals. To that end a great deal of effort went into defining the kinds of tools that would be needed for specific imaging applications. The second year emphasized the identification of key research thrusts that cut across all cores and were driven by the needs and requirements of the DBPs. This led to the formulation of the Center's four main technical themes: Diffusion Tensor Analysis, Structural Analysis, Functional MRI Analysis, and the integration of newly developed tools into the NA-MIC Tool Kit. The third year of Center activity was devoted to the continuation of collaborative work to develop solutions for the various brain-oriented DBPs. The fourth year was focused on translating collaborative knowledge and work to a new set of DBPs. In the current fifth year, a number of projects have made sufficient progress to warrant introduction as modules in Slicer, thereby making the Core 1 algorithms available to the general medical imaging community. Some of these algorithms are quite general and can be used for purposes far broader than the original DBPs. For example, a new point cloud registration algorithm developed for the prostate brachytherapy needle positioning project also can be used for DWI registration. Likewise, work on DTI/DWI tractography has been applied to the segmentation of blood vessels and soft plaque detection in the coronary arteries.

Year five progress with respect to the current DBPs is relevant to the scope of this Annual Progress Report. As mentioned, we currently have three projects in the area of neuropsychiatric disorders: Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill). A fourth project from Johns Hopkins and Queens Universities involves the application of core technologies to imaging/robotics-guided treatments in prostate cancer. A number of papers have been published that specifically acknowledge the NA-MIC, and significant software development is continuing as well.

Section 3 outlines specific aims fulfilled this year by the four roadmap projects: Section 3.1 describes the Stochastic Tractography Approach for Velocardiofacial Syndrome; Section 3.2 details the application of our work to Brachytherapy Needle Positioning for the Prostate; Section 3.3 outlines the Brain Lesion Analysis in Neuropsychiatric Systemic Lupus Erythematosus project; and Section 3.4 documents the Cortical Thickness for Autism project. For all of these projects, a synergism of effort has produced working computer modules that are user friendly and accessible to both medical researchers and clinicians.

Section 4 describes year five work on the four infrastructure topics. These include: Diffusion Image Analysis (Section 4.1), Structural Analysis (Section 4.2), Functional MRI Analysis (Section 4.3), and the NA-MIC Toolkit (Section 4.4). Many of the algorithms produced by Cores 1-3 have been integrated into ITK and Slicer, including those concerning shape analysis (e.g., spherical wavelets), new segmentation algorithms (for DTI/DWI tractography and the segmentation of the prostate), and new approaches to registration (e.g., based on particle filtering).

Finally, the last three sections of this Annual Progress Report highlight some of the work that the the Scientific Leadership believes is particularly significant to the the overall goals of the Center. Section 5 summarizes the benefits of several advanced algorithms, gives a description of the growing NAMIC-Toolkit, and documents the scope of our efforts in technology transfer and outreach. It is essential to emphasize that although the algorithms emanating from this Center were developed to solve specific clinical problems raised by the DBPs, in application, most of these algorithms have far more general utility and far greater potential impact on the medical imaging technical base. To this end, Section 6 draws attention to the impact and value of our work on biocomputing imaging at three different levels: within the Center, within the NIH-funded research community, and externally to the national and international community. To further illustrate the impact of our work, Section 7 provides some updated timelines with specific milestones achieved by the various NA-MIC cores. Section 8 lists publications pertinent to the current reporting period that acknowledge NA-MIC support, and Section 9 provides the External Advisory Report and our considered response.

=Clinical Roadmap Projects=
==Roadmap Project: Stochastic Tractography for VCFS (Kubicki)==
===Overview (Kubicki)===
The goal of this project is to create an end-to-end application that is useful in evaluating anatomical connectivity between segmented cortical regions of the brain. The ultimate goal of our program is to understand similarities and differences in anatomical connectivity between genetically related schizophrenia and velocardio-facial syndrome. Thus we plan to use the "stochastic tractography" tool for the analysis of abnormalities in integrity or connectivity in the arcuate fasciculus fiber bundle that is involved in language processing in schizophrenia and VCFS.

===Algorithm Component (Golland)===
The core science involved in this project is the Stochastic Tractography algorithm. This algorithm was developed and implemented collaboratively by MIT and BWH. Stochastic Tractography is a Bayesian approach to estimating nerve fiber tracts from images created by diffusion tensor imaging (DTI).

In this approach, the diffusion tensor is used at each voxel in the volume to
construct a local probability distribution for the fiber direction
around the principal direction of diffusion. The tracts then are sampled
between two user-selected regions of interest (ROIs), by simulating a random walk between
the regions, based the local transition probabilities inferred from
the DTI image.

The resulting collection of fibers and the associated FA values
provide useful statistics on the properties of connections between the
two regions. To constrain the sampling process to the relevant white
matter region, atlas-based segmentation is used to label ventricles and
gray matter and to exclude them from the search space. This latter
step relies heavily on the registration and segmentation functionality
of Slicer.

Over the last year, we have been working on applying several pre- and postprocessing steps to the algorithm pipeline. These steps include "eddy current" and "geometric distortion correction," which were made available to us by the Utah group, as well as "DTI filtering" (BWH). White matter masks now also can be created based on T2 thresholding within the Slicer Stochastic Tractography module. These masks are more precise, as they do not rely on MRI-to-DTI co-registration.

We also have been working on the datasets that apply to situations where fMRI activations as well as gray matter segmentations need to be registered to DTI data to permit seeding within predefined gray matter regions. Significant progress has been made in modality registration, and additional improvement is expected when "geometric distortion correction" becomes part of the analysis pipeline.

Finally, we have been working on improved ways to visualize and quantify Stochastic Tractography output, not only by parametrizing fiber tracts, but also by creating connection probability distribution maps.

===Engineering Component (Davis)===
This year, the Stochastic Tractography Slicer module was rewritten in Python. The new module was released in December, 2008, and presented at the All Hands Meeting in Salt Lake City. The module is now a functional component of Slicer3. Documentation for operating the module also has been created to facilitate user training. Current Engineering efforts are focused on maintaining the module, optimizing the module for use with other data formats, and adding new functionality, such as better registration, distortion correction, and ways of extracting and measuring functional anisotropy (FA) along nerve fiber tracts.

The datasets used with the Stochastic Tractography module are computationally demanding. They involve higher spatial resolutions and many more diffusion directions as compared with White Matter Tractography, which was used previously. As well, the cortical ROIs tend to be much larger than white matter ROIs. Hence, there is a pressing need for performance improvement. This need can be appreciated by examining the differences between Stochastic Tractography, where literally hundreds of tracts are generated from a single seed, and Deterministic Tractography, where only a single tract is generated. Thus, some effort has been made to economize by use of multi-threading and parallel processing. A version of the Stochastic Tractography algorithm that uses large computer clusters also has been developed and can be downloaded and installed by individual users with minimal knowledge of parallel computing.

===Clinical Component (Kubicki)===
In this reporting period, we have designed, implemented, or completed several studies to test the Stochastic Tractography algorithm on the newly released 3T NA-MIC data. These datasets consist of high resolution DTI, structural RM data, and automatic anatomical segmentations. Since these data already have been co-registered, cortical ROIs can be used as seeding points for Stochastic Tractography.

The first of these clinical studies was designed to analyze the connections between the inferior frontal and superior temporal lobes, which represent important sites of the language network. The connections between these two regions were measured via Stochastic Tractography in a group of 20 chronic schizophrenia patients and 20 controls and then subjected to comparative analysis. We also examined gray matter volume in destination regions and attempted to estimate the relationship between gray and white matter abnormalities in schizophrenia. The results of this study were presented at the World Psychiatry Congress in Florence, Italy in April, 2009, and later that same month at the Harvard Psychiatry MYSELL conference.

Another current endeavor is the application of Stochastic Tractography to define the connections involved in emotional processing. [For this study, we are using cortical segmentations of the anterior cingulated gyrus, orbital-frontal gyrus, and amygdala and trace as well as quantify connections between these regions in healthy controls versus schizophrenia patients.] The results of this preliminary study were presented at MYSELL in April, 2009. Another presentation will be made at the Biological Psychiatry conference later this year.

We have also been involved in two collaborative efforts. The first involves the use of DTI data acquired at UCI. In this study, we have used the stochastic method to segment and measure the arcuate fasciculus in subjects with schizophrenia and language impairment, as evidenced in ERP data. In another collaboration, we are combining resting state fMRI data with DTI to measure connectivity between regions that form a functional network. Both of these projects are currently under way.

Finally, a paper that discusses the qualitative use of Stochastic Tractography has been accepted for publication in Human Brain Mapping and is currently in press. Here, when we combined fMRI with DTI whole brain data analysis, we identified certain regions that expressed abnormal functional connectivity in schizophrenia. These regions were then assigned to certain anatomical structures (white matter tracts) based on their location and relationship to Stochastic Tractography output.

===Additional Information===
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:Harvard:Brain_Segmentation_Roadmap here on the NA-MIC wiki].
==Roadmap Project: MR-guided Prostate Biopsy Needle Positioning Robot Integration (Fichtinger)==
===Overview (Fichtinger)===
Numerous studies have demonstrated the efficacy of image-guided needle-based therapy and biopsy in the management of prostate cancer. However, the accuracy of traditional prostate interventions that rely on transrectal ultrasound (TRUS) is limited by image fidelity, needle template guides, needle deflection, and tissue deformation. Magnetic Resonance
Imaging (MRI) is an ideal modality for guiding and monitoring such interventions because it provides excellent visualization of the prostate, its sub-structure, and surrounding tissues.

We have designed a comprehensive robotic assistant system that permits prostate biopsy and brachytherapy procedures to be performed entirely inside a 3T closed MRI scanner. The current system applies the transrectal approach to the prostate. With this approach, an endorectal coil and steerable needle guide, both tuned to 3T magnets and invariably to any particular scanner, are integrated into the MRI compatible manipulator.

Under the NA-MIC initiative, the interface between image computing, visualization, intervention planning, and kinematic planning is being managed by an open-source system built on the NA-MIC toolkit and its components, that is, Slicer3 and ITK. These tools are complemented by a collection of unsupervised prostate segmentation and registration methods that are important to the clinical performance of the interventional system as a whole.

===Algorithm Component (Tannenbaum)===

Our group (GaTech) has worked on both the segmentation and registration of the prostate from MRI and ultrasound data. This process is described below.

'''Prostate Segmentation'''

The first step is to "extract" the prostate. We have provided two methods: a shape-based method and a semi-automatic method. More details are given below and images and further details may be found [http://www.na-mic.org/Wiki/index.php/Projects:ProstateSegmentation here]

# ''A shape-based algorithm''. This process begins by learning a group of shapes, obtained by manually segmenting a set of prostate 3D images. With the shapes represented as the hyperbolic tangent of the signed distance functions, principle component analysis is employed to learn the shapes. Further, given a new prostate image, we search the learned shape space in order to find one shape best segment the given image. The fitness of one shape to segment the image is evaluated by an energy functional measuring the discrepancy of the statistical characteristics inside and outside the current segmentation boundary. Such method is robust to the noise in the images. Moreover, the whole algorithm pipeline has been integrated into the Slicer3 through the command line module.
# ''Semi-automatic method''. This method is based on a random walk segmentation algorithm. With user provided initial seed regions inside and out side the object (prostate), the algorithm computes a probability distribution over the image domain by solving a boundary value partial differential equation where the value at seed regions are fixed at 1.0 or 0.0, depending or whether they are object or background seeds. The resulting distribution indicates the probability of each voxel belonging to the object. Simply threshold by 0.5 gives the segmentation of the object. Moreover, if the result is not suitable, the user can edit the seed regions, and the new result is computed based on this previous result. This algorithm has been integrated into the transrectal prostate MRI module of Slier3.

'''Prostate Registration'''

We developed a nonlinear (affine) prostate registration method by treating prostate images as point sets. Then the iterative closest point algorithm is improved to register the point sets generated by the two images to be registered. The proposed method shows robustness to long distance transition and partial image structure. Moreover, such representation is much sparser than sampling image on the uniform grid thus the registration is very fast comparing two 3D volumetric
image registration.

Furthermore, the registration is viewed as a posterior estimation problem, in which the distributions of the affine and translation parameters are to be estimated. This can naturally be estimated using a particle filter framework. Through this, the method can handle the otherwise difficult cases where the two prostates are one supine and
one prone.

More details are given [[Projects:pfPtSetImgReg|here...]]

===Engineering Component (Hayes)===

An end-to-end slicer loadable module that interfaces with MRI-compatible robotic device to perform MRI guided prostate biopsy has been developed. A complete end-user tutorial documentation has been uploaded on the project wiki-page together with sample tutorial dataset (phantom) to facilitate user training. This is one of the first modules that uses Slicer as an interventional tool contrary to traditional usage as a post-processing tool.

This has been an year of ideas to implementation. The design hatched was realized during this year.
Specifically speaking, following functionality was implemented. An intuitive workflow based GUI, that clearly identifies four phases of the intervention (Device calibration/registration, prostate segmentation, targeting, and verification) guides the user through the process.

The robotic device calibration/registration to scanner coordinates is achieved by means of segmenting fiducial markers in images. The registration parameters are used in targeting step for calculation of targeting parameters and needle tarjectory. The robotic device's optical encoders are interfaced, these sensors continuously sense the device rotation and needle angle, and are sent over USB interface, our module reads these on a 500 msec timer event.

The prostate segmentation algorithm developed by our algorithm core collaborators at Georgia Tech was integrated during NAMIC programming at Utah this year. The details are provided in previous sub-section.

The targeting step enables user to pick anatomic locations of interest by just clicking in any of 3 slice views. 3D Slicer's arbitrary reformat plane widget is an attractive feature which enables user/clinician to visualize and pick target in any desired plane. When a target is picked, the targeting parameters (device rotation and needle angle) for the device to hit the intended target are calculated and updated in the list of targets. Multiple targets can be picked and associated with a particular type of needle (e.g. biopsy or seed). Once a particular target is selected from the list of targets, the selected target is brought into view in all three slice views and highlighted in the 3D view, the information about the target and targeting parameters is displayed. Further, the robot's needle trajectory to the target is also visualized in 3D; this is a very crucial feedback for the clinician.

The biopsy is performed, and a validation volume is acquired while needle still in the prostate; this validation volume is then used to perform validation analysis, to find out how accurately did the device hit the target.

We've sought and received timely help from the engineering core. A couple of times, some functionality in Slicer 'core' was implemented for our specific requirements. The current engineering efforts are focussed on testing module at various levels, detecting and fixing bugs. We are in the process of designing a test protocol for functional and clinical evaluation of the software. Efforts are also on to add functionality e.g. additional dedicated MR room display window which will display the chosen 2D image view for particular target, the required robot targeting paramaters, and the sensed robot parameters, load previously saved experiment for post-op analysis, visualizing probe coverage.

===Clinical Component (Fichtinger)===
<Note Progress in the last year>

===Additional Information===
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:JHU:Roadmap here on the NA-MIC wiki].
==Roadmap Project: Brain Lesion Analysis in Neuropsychiatric Systemic Lupus Erythematosus (Bockholt)==
===Overview (Bockholt)===
The primary goal of the MIND DPB is to examine changes in white matter lesions in adults with Neuropsychiatric Systemic Lupus Erythematosus (SLE). We want to be able to characterize lesion location, size, and intensity, and would also like to examine longitudinal changes of lesions in an SLE cohort. To accomplish this goal, we will create an end-to-end application entirely within NA-MIC Kit allowing individual analysis of white matter lesions. Such a workflow will then be applied to a clinical sample in the process of being collected.

===Algorithm Component (Whitaker)===
The basic steps necessary for the white matter lesion analysis application entail first registration of T1, T2, and FLAIR images, second tissue classification into gray, white, csf, or lesion, thirdly clustering lesion for anatomical localization, and finally a summarization of lesion size and image intensity parameters within each unique lesion.

<Note Progress in the last year>

===Engineering Component (Pieper)===

At the [http://www.na-mic.org/Wiki/index.php/2009_Winter_Project_Week January 2009 NA-MIC project week] a first pass of a lesion segmentation tutorial was provided to the community. This was the first end-to-end workflow for this project and represented a significant step in the project. Based on feedback from the community and the target clinical users of these tools, we identified additional steps to improve the system.

The primary engineering effort has been directed to the following projects:

'''Interface Improvements'''

We have begun to look at the creation of a [[2009_Winter_Project_Week:HighLevelWizard| high level wizard]] as a front end to the processing task. This interface would allow users to go through the steps without directly navigating the slicer modules and can also provide state management that will simplify the visualization efforts.

'''Modularity and Deployment'''

The current tutorial has been difficult for some users to implement due to the requirement that the lesion detection module be compiled locally on the user's machine. Non-developers understandably find this to be a difficult requirement, so we are integrating the lesion segmentation code into the [http://www.slicer.org/slicerWiki/index.php/Slicer3:Loadable_Modules:Status Slicer3 loadable module project] so that pre-compiled versions of the module are available for users. To implement this, we are following the templates provided by the [http://www.nitrc.org/projects/slicer3examples/ slicer example modules] provided on the nitrc.org website. This infrastructure was created via a supplement to NA-MIC provided by the NITRC project. The [http://www.nitrc.org/projects/lupuslesion/ project page on nitrc.org] is being updated as new features are added to the modules.

'''Core Implementation Support'''

During this period we have also worked on [[2009_Winter_Project_Week:LesionSegmentationEfficiency|optimizing the implementation]] of the core ITK code. This effort has primarily been accomplished by the MIND group, with interactions as needed with the rest of the NA-MIC community.

In addition, ongoing discussions with the rest of the NA-MIC community are encouraging code sharing among projects through modularization of common processing tasks and development of 'best of breed' routines for lesion detection and quantification. These tools are then embodied as slicer modules for use in other applications such as brain tumor change tracking.

===Clinical Component (Bockholt)===
<Note Progress in the last year>
===Additional Information===
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:MIND:Roadmap here on the NA-MIC wiki].
==Roadmap Project: Cortical Thickness for Autism(Hazlett)==
===Overview (Hazlett)===

A primary goal of the UNC DPB is to examine changes in cortical thicknes in children with autism compared to typical controls. We want to examine group differences in both local and regional cortical thickness, and would also like to examine longitudinal changes in the cortex from ages 2-4 years. To accomplish this goal, this project will create an end-to-end application within Slicer3 allowing individual and group analysis of regional and local cortical thickness. Such a workflow will then be applied to our study data (already collected).

===Algorithm Component (Styner)===

The basic steps necessary for the cortical thickness application entail first tissue segmentation in order to separate white and gray matter regions, second cortical thickness measurement, thirdly cortical correspondence to compare measurements across subjects and finally a statistical analysis to locally compute group differences.

<Note Progress in the last year>

===Engineering Component (Miller, Vachet)===

<Note Progress in the last year>

===Clinical Component (Hazlett)===
<Note Progress in the last year>

===Additional Information===
Additional Information for this project is available [http://wiki.na-mic.org/Wiki/index.php/DBP2:UNC:Cortical_Thickness_Roadmap here on the NA-MIC wiki].

=Four Infrastructure Topics=
==Diffusion Image Analysis (Gerig)==
<Note Progress in the last year>
===Key Investigators===

<Need to update the list below>

* BWH: Marek Kubicki, Martha Shenton, Sylvain Bouix, Julien von Siebenthal, Thomas Whitford, Jennifer Fitzsimmons, Doug Terry, Jorge Alverado, Eric Melonakos, Carl-Fredrik Westin.
* MIT: Lauren O'Donnell, Polina Golland
* UCI: James Fallon, Judi Ford
* Utah I: Tom Fletcher, Ross Whitaker, Ran Tao, Yongsheng Pan
* Utah II: Casey Goodlett, Sylvain Gouttard, Guido Gerig
* GA Tech: John Melonakos, Vandana Mohan, Shawn Lankton, Allen Tannenbaum
* GE: Xiaodong Tao, Jim Miller, Mahnaz Mandah
* UCLA: Nathan Hageman
* Isomics: Steve Pieper
* Kitware: Luis Ibanez

===Additional Information===
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:DiffusionImageAnalysis here on the NA-MIC wiki].

==Structural Analysis(Tannenbaum)==
===Progress===
Under Structural Analysis, the main topics of research for NAMIC are structural segmentation, registration techniques and shape analysis. These topics are correlated and hence research in one often finds application in another. For example, shape analysis can yield useful priors for segmentation, or segmentation and registration can provide structural correspondences for use in shape analysis and so on.

An overview of selected progress highlights under these broad topics follows:

Segmentation

* Geodesic Tractography Segmentation: We proposed an image segmentation technique based on augmenting the conformal (or geodesic) active contour framework with directional information. This has been applied successfully to the segmentation of neural fiber bundles such as the Cingulum Bundle. This framework has now been integrated into Slicer and is being tested on a population of brain data sets.

* Tubular Surface Segmentation: We have proposed a new model for tubular surfaces that transforms the problem of detecting a surface in 3D space, to detecting a curve in 4D space. Besides allowing us to impose a "soft" tubular shape prior, this also leads to computational efficiency over conventional surface segmentation approaches. We have also developed the moving end points implementation of this framework wherein the required input is only a few points in the interior of the structure of interest. This yields the additional advantage that the framework simulatenously returns both the 3D segmentation and the 3D skeleton of the structure eliminating the need for apriori knowledge of end points, and an expensive skeletonization step. The framework is applicable to different tubular anatomical structures in the body. We have so far applied it successfully to the Cingulum Bundle, and blood vessels.

* Local-global Segmentation: We have proposed a novel segmentation approach that combines the advantages of local and global approaches to segmentation, by using statistics over regions that are local to each point on the evolving countour. This makes it well suited to applications with contrast differences within the structure of interest such as in blood vessel segmentation, as well as applications like the neural fiber bundles where the diffusion profiles of voxels within the structure are locally similar but vary along the length of the fiber bundle itself.

* Shape-based segmentation: 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 presented 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. We have applied the algorithm successfully to real MRI images, and we have also implemented it into 3D Slicer.

* Re-Orientation Approach for Segmentation of DW-MRI: This work proposes a methodology to segment tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. Segmentation is achieved through simple global statistical modeling of diffusion orientation which allows for a convex optimization formulation of the segmentation problem, combining orientation statistics and spatial regularization. The approach compares very favorably with segmentation by full-brain streamline tractography.

Registration

* Optimal Mass Transport based Registration: We have provided a computationaly efficient non-rigid/elastic image registration algorithm based on the Optimal Mass Transport theory. We use the Monge-Kantorovich formulation of the Optimal Mass Transport problem and implement the solution proposed by Haker et al. using multi-resolution and multigrid techniques to speed up the convergence. We also leverage the computation power of general purpose graphics processing units available on standard desktop computing machines to exploit the inherent parallelism in our algorithm. We extend the work by Haker et al. who compute the optimal warp from a first order partial differential equation, an improvement over earlier proposed higher order methods and those based on linear programming, and further implement the algorithm using a coarse-to-fine strategy resulting in phenomenol improvement in convergence. We have applied it successfully to the registration of 3D brain MRI datasets (preoperative and intra-operative), and are currently extending it to the non-rigid registration of baseline DWI to brain MRI data.

* Atlas Regularization for Image Segmentation: Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies.

* Point-set Rigid Registration: We have proposed a particle filtering scheme for the registration of 2D and 3D point set undergoing a rigid body transformation. Moreover, we incorporate stochastic dynamics to model the uncertainity of the registration process. We treat motion as a local variation in the pose parameters obatined from running a few iterations of the standard Iterative Closest Point (ICP) algorithm. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence as well as provide a dynamical model of uncertainity. In contrast with other techniques, our approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temoral coherency of the state (no loss of information). Also, unlike most alternative approaches for point set registration, we make no geometric assumptions on the two data sets.We applied the algorithm to different alignments of point clouds and it successfully found the correct optimal transformation that aligns two given point clouds despite the differing geometry around the local neighborhood of a point within their respective sets.

* Regularization for Optimal Mass Transport: To extend the flexibility of the existing OMT algorithm, we added a regularization term to the functional being minimized. This term controls the tradeoff between how well two images match after registration versus how warped the transformation map can become. A weighted sum of squared differences is used to penalize having to move mass over long distances; this addition also helps to keep the transformation physically accurate by reducing the likelihood that the transformation grid will fold over itself and keeping the grid smooth.

* Registration of DW-MRI to structural MRI: Optimal Mass Transport was applied to the problem of correcting EPI distortion in DW-MRI. A mask for white matter in DW-MRI was registered to the white matter mask extracted from the structural MRI for the same patient. Prior to registration, it is important to normalize intensities in the two masks; this was done by dividing the images into regions and uniformly normalizing over each region to assure the sum of the intensities is equal. Then, once a transformation between the white matter masks was calculated, this transformation was applied to the original DW-MRI image.

Shape Analysis

* Shape Analysis Framework using SPHARM-PDM: We have provided an analysis framework of objects with spherical topology, described by sampled spherical harmonics SPHARM-PDM. The input is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are first processed to fill any interior holes. The processed binary segmentations are converted to surface meshes, and a spherical parametrization is computed for the surface meshes using a area-preserving, distortion minimizing spherical mapping. The SPHARM description is computed from the mesh and its spherical parametrization. Using the first order ellipsoid from the spherical harmonic coefficients, the spherical parametrizations are aligned to establish correspondence across all surfaces. The SPHARM description is then sampled into a triangulated surfaces (SPHARM-PDM) via icosahedron subdivision of the spherical parametrization. These SPHARM-PDM surfaces are all spatially aligned using rigid Procrustes alignment. Group differences between groups of surfaces are computed using the standard robust Hotelling T 2 two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. We provide additional visualization of the group tests via mean difference magnitude and vector maps, as well as maps of the group covariance information. We have a stable implementation, and current development focuses on integrating the current command line tools into Slicer (v3) via the Slicer execution model.

* Population studies using Tubular Surface Model: We have proposed a tubular shape model for the Cingulum Bundle which models a tubular surface as a center-line coupled with a radius function at every point along the center-line. This model shows potential for population studies on the Cingulum Bundle which is believed to be involved in Schizophrenia, since it provides a natural way of sampling the structure to build a feature representation of it. We are currently segmenting the Cingulum Bundle from a population of brain data sets, towards performing this population analysis using the Pott's Model.

* Automatic Outlining of Sulci on a Brain Surface: We present a method to automatically extract certain key features on a surface. We apply this technique to outline sulci on the cortical surface of a brain, where the data is taken to be a 3D triangulated mesh formed from the segmentation of MR image slices. The problem is posed as energy minimization using penalizing the arc-length of segmenting curve using conformal factor involving the mean curvature of the underlying surface. The computation is made practical for dense meshes via the use of a sparse-field method to track the level set interfaces and regularized least-squares estimation of geometric quantities.

===Key Investigators===

Needs to be updated:

* MIT: Polina Golland, Kilian Pohl, Sandy Wells, Eric Grimson, Mert R. Sabuncu
* UNC: Martin Styner, Ipek Oguz, Xavier Barbero
* Utah: Ross Whitaker, Guido Gerig, Suyash Awate, Tolga Tasdizen, Tom Fletcher, Joshua Cates, Miriah Meyer
* GaTech: Allen Tannenbaum, John Melonakos, Vandana Mohan, Tauseef ur Rehman, Shawn Lankton, Samuel Dambreville, Yi Gao, Romeil Sandhu, Xavier Le Faucheur, James Malcolm, Ivan Kolosev
* Isomics: Steve Pieper
* GE: Bill Lorensen, Jim Miller
* Kitware: Luis Ibanez, Karthik Krishnan
* UCLA: Arthur Toga, Michael J. Pan, Jagadeeswaran Rajendiran
* BWH: Sylvain Bouix, Motoaki Nakamura, Min-Seong Koo, Martha Shenton, Marc Niethammer, Jim Levitt, Yogesh Rathi, Marek Kubicki, Steven Haker

===Additional Information===
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:StructuralImageAnalysis here on the NA-MIC wiki].
==fMRI Analysis (Golland)==
===Progress===

* Connectivity Analysis:

One of the major goals in analysis of fMRI data is the detection of functionally homogeneous networks in the brain. We developed a new method for characterizing functional connectivity patterns from fMRI. In contrast to the seed-based analysis typically employed to identify networks of co-activation, we propose to use clustering to simultaneously estimate the networks and their representative time courses, which effectively replace user-specified seeds.
During this year, we validated this method for characterizing functional connectivity patterns from fMRI. To investigate the sensitivity of the analysis to the generative model of the signal, we implemented and compared two distinct algorithms, the mixture-model clustering and the spectral clustering, in application to this problem. We validated our approach in a rest state fMRI scans of 45 healthy subjects. Our results demonstrate that the detected networks are stable across subjects and across methods.
At the same time, we worked with the Harvard DBP to identify relevant clinical data sets in which our approach promises to identify effect of a disorder. We have started a collaboration to apply the method to a group of schizophrenia patients and normal controls.

* Distortion Correction for EPI-Based Functiona Imaging

We developed and demonstrated a method for correcting the distortions present in echo planar images (EPI) and registering the EPI image to structural MRI scans. Our approach does not require acquiring fieldmaps, modifying EPI acquisition parameters, or having detailed knowledge of the shim system. The technique consists of two steps. First, a classifier is used to segment structural MR into an air/tissue susceptibility model. The resulting tissue map serves as input to a first order perturbation field model to compute a subject-specific fieldmap. The classifier is trained based on MR-CT image pairs, using MR intensities as features and exploiting air segmentation in the CT images to construct labels. Second, a simultaneous shim estimation and registration algorithm is employed to solve for the lower order field perturbations (shim parameters) needed to accurately unwarp and register the EPI data.

===Key Investigators===

* MIT: Polina Golland, Danial Lashkari,Archana Venkataraman, Clare Poynton
* Harvard/BWH: Sylvain Bouix, Martha Shenton, Marek Kubicki, Carl Frederick Westin, Sandy Wells

===Additional Information===
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC_Internal_Collaborations:fMRIAnalysis here on the NA-MIC wiki].

==NA-MIC Kit Theme (Schroeder)==
===Progress===
The NAMIC-Kit consists of a framework of advanced computational components, as well as the support infrastructure for testing, documenting, and deploying leading edge medical imaging algorithms and software tools. The framework has been carefully constructed to provide low-level access to libraries and modules for advanced users, plus high-level application access that non-computer professionals can use to address a variety of problems in biomedical computing. In this fifth year of the NA-MIC projects <summary of progress>

===Software Releases===
The NAMIC-Kit can be represented as a pyramid of capabilities, with the base consisting of toolkits and libraries, and the apex standing in for the Slicer3 user application. In between, Slicer modules are stand-alone executables that can be integrated directly into the Slicer3 application, including GUI integration, while work-flows are groups of modules that are integrated together to manifest sophisticated segmentation, registration and biomedical computing algorithms. In a coordinated NAMIC effort, major releases of these many components were realized over the past year. This includes, but is not limited to:
*
*

===Slicer3 and the Software Framework===
One of the major achievements of the past year has been the release of [http://www.slicer.org/slicerWiki/index.php/Documentation-3.4 version 3.4 of 3D Slicer] in May of 2009. A number of important improvements have been made by the Engineering Core and significant new functionality has been added throug other NA-MIC cores and collaborators since the release of version 3.2 in August of 2008. A few notable examples include:

* [http://www.slicer.org/slicerWiki/index.php/Modules:Saving-Documentation-3.4 An Integrated Data Save Dialog]
* [http://www.slicer.org/slicerWiki/index.php/Modules:Fiducials-Documentation-3.4 Significant Rework of the Fiducials Interface]
* [http://www.slicer.org/slicerWiki/index.php/Modules:Slices-Documentation-3.4 A Slices Module to Support Advanced Visualization Modes]
* [http://www.slicer.org/slicerWiki/index.php/Modules:Editor-Documentation Significant Improvements to the Interactive Label Map Editor]
* [http://www.slicer.org/slicerWiki/index.php/Modules:ChangeTracker-Documentation-3.4 Integration of a Brain Tumor Change Tracking Module in collaboration with the Brain Science Foundation]
* [http://www.slicer.org/slicerWiki/index.php/Modules:IA_FEMesh-Documentation-3.4 Integration of a Finite Element Meshing Module as a deliverable of the NA-MIC Collaboration Grant at the University of Iowa]
* [http://www.slicer.org/slicerWiki/index.php/Modules:FetchMI-Documentation-3.4 A Medical Informatics Interface to XNAT in collaboration with BIRN]
* [http://www.slicer.org/slicerWiki/index.php/Slicer3:Python The Ability to Interactively Script Slicer in Python as well as Tcl]

In addition, there have been major extensions to the diffusion imaging tools, registration tools, filters, image guided therapy, and other core changes that enhance the utility and applicability of the software.

===Software Process===
One of the challenges facing developers has been the requirement to implement, test and deploy software systems across multiple computing platforms. NAMIC continues to push the state of the art with further development of the CMake, CTest, and CPack tools for cross-platform development, testing, and packaging, respectively...

===Key Investigators===
THis list needs to be updated:

* Kitware - Will Schroeder (Core 2 PI), Sebastien Barre, Luis Ibanez, Bill Hoffman
* GE - Jim Miller, Xiaodong Tao
* Isomics - Steve Pieper, Alex Yarmarkovich, Curt Lisle, Terry Lorber

===Additional Information===
Additional Information for this topic is available [http://wiki.na-mic.org/Wiki/index.php/NA-MIC-Kit here on the NA-MIC wiki].

=Highlights(Schroeder)=
===Advanced Algorithms===

===NAMIC-Kit===

===Outreach and Technology Transfer===
Cores 4-5-6 continue to support, train and dissemniate to the NAMIC community, and the broader biomedical computing community.
* The Slicer community held several workshops and tutorials. In xxx a satellite event was held for the international Organization for Human Brain Mapping at the annual meeting in xxx. The xx workshop on xx hosted xx participants representing xx countries from around the world, xx states within the US and xxdifferent laboratories including xx NIH institutes. In addition, <note how many slicer tutorials were held and where etc>
* Project Week continues to be a successful NAMIC venue. These semi-annual events are held in Boston in June, and January in Salt Lake City. These events are well attended with approximately 100 participants, of which about a third are outside collaborators. At the last Project Week in Salt Lake City, approximately xx projects were realized.
* NAMIC continues to participate in conferences and other technical venues. For example, NAMIC hosted xxx

=Impact and Value to Biocomputing (Miller)=
NA-MIC impacts Biocomputing through a variety of mechanisms. First,
NA-MIC produces scientific results, methodologies, workflows,
algorithms, imaging platforms, and software engineering tools and
paradigms in an open enviroment that contributes directly to the body of
knowledge available to the field. Second, NA-MIC science and
technology enables the entire medical imaging community to build on
NA-MIC results, methods, and techniques, to concentrate on the new
science instead of developing supporting infrastructure, to leverage
NA-MIC scientists and engineers to adapt NA-MIC technology to new
problem domains, and to leverage NA-MIC infrastructure to distribute
their own technology to a larger community.

===Impact within the Center===

===Impact within NIH Funded Research===

===National and International Impact===

= Timeline (Ross)=

<The table needs to be updated>

This section of the report gives the milestones for years 1 through 5 that are associated with the timelines in the original proposal. We have organized the milestones by core. For each milestone we have indicated the proposed year of completion and a very brief description of the current status. In some cases the milestones include ongoing work, and we have try to indicate that in the status. We have also included tables that list any significant changes to the proposed timelines. On the wiki page, we have links to the notes from the various PIs that give more details on their progress and the status of the milestones.

'''These tables demonstrate that the project is, on the whole, proceeding according to the originally planned schedule.'''

== Core 1: Algorithms ==

=== Timelines and Milestones ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Proposed time of completion'''
| '''Status'''
|-
| '''MIT'''
| 1
| '''Shape-based segmentation'''
|
|
|-
| '''MIT'''
| 1.1
| Methods to learn shape representations
| Year 2
| Completed
|-
| '''MIT'''
| 1.2
| Shape in atlas-driven segmentation
| Year 4
| Completed
|-
| '''MIT'''
| 1.3
| Validate and refine approach
| Year 5
| In Progress
|-
| '''MIT'''
| 2
| '''Shape analysis'''
|
|
|-
| '''MIT'''
| 2.1
| Methods to compute statistics of shapes
| Year 4
| Completed
|-
| '''MIT'''
| 2.3
| Validation of shape methods on application data
| Year 5
| Completed, refinements ongoing
|-
| '''MIT'''
| 3
| '''Analysis of DTI data'''
|
|
|-
| '''MIT'''
| 3.1
| Fiber geometry
| Year 3
| Completed
|-
| '''MIT'''
| 3.2
| Fiber statistics
| Year 5
| Completed, new developments ongoing
|-
| '''MIT'''
| 3.3
| Validation on real data
| Year 5
| Completed, refinements ongoing
|-
| '''Utah'''
| 1
| '''Processing of DTI data'''
|
|
|-
| '''Utah'''
| 1.1
| Filtering of DTI
| Year 2
| Completed
|-
| '''Utah'''
| 1.2
| Quantitative analysis of DTI
| Year 3
| Completed, refinements ongoing
|-
| '''Utah'''
| 1.3
| Segmentation of cortex/WM
| Year 3
| Completed partially, modified below
|-
| '''Utah'''
| 1.4
| Segmentation analysis of white matter tracts
| Year 3
| Completed, applications ongoing
|-
| '''Utah'''
| 1.5
| Joint analysis of DTI and functional data
| Year 5
| Initiated
|-
| '''Utah'''
| 2
| Nonparametric Shape Analysis
| Year 5
| Completed
|-
| '''Utah'''
| 2.1
| Framework in place
| Year 3
| Complete
|-
| '''Utah'''
| 2.2
| Demonstration on shape of neuranatomy (from Core 3)
| Year 4
| Complete
|-
| '''Utah'''
| 2.3
| Development for multiobject complexes
| Year 4
| Complete
|-
| '''Utah'''
| 2.4
| Demonstration of NP shape representations on clinical hypotheses from Core 3
| Year 5
| Complete, publications in progress
|-
| '''Utah'''
| 2.6
| Integration into NAMIC-kit
| Year 5
| Incomplete (initiated)
|-
| '''Utah'''
| 2.7
| Shape regression
| Year 5
| Incomplete
|-

|-
| '''UNC'''
| 1
| '''Statistical shape analysis'''
|
|
|-
| '''UNC'''
| 1.1
| Comparative anal. of shape anal. schemes
| Year 2
| Completed
|-
| '''UNC'''
| 1.3
| Statistical shape analysis incl. patient variable
| Year 5
| Complete, refinements ongoing
|-
| '''UNC'''
| 2
| '''Structural analysis of DW-MRI'''
|
|
|-
| '''UNC'''
| 2.1
| DTI tractography tools
| Year 4
| Completed
|-
| '''UNC'''
| 2.2
| Geometric characterization of fiber tracts
| Year 5
| Completed
|-
| '''UNC'''
| 2.3
| Quant. anal. of diffusion along fiber tracts
| Year 5
| Completed.
|-
| '''GaTech'''
| 1.1
| ITK Implementation of PDEs
| Year 2
| Completed
|-
| '''GaTech'''
| 1.1
| Applications to Core 3 data
| Year 4
| Completed
|-
| '''GaTech'''
| 1.2
| New statistic models
| Year 4
| Completed
|-
| '''GaTech'''
| 1.2
| Shape anaylsis
| Year 4
| Completed, refinements ongoing
|-
| '''GaTech'''
| 2.0
| Integration in to Slicer
| Year 4-5
| Preliminary results and ongoing
|-
| '''MGH'''
| 1
| '''Registration'''
|
|
|-
| '''MGH'''
| 1.1
| Collect DTI/QBALL data
| Year 2
| Completed
|-
| '''MGH'''
| 1.2
| Develop registration method
| Year 2
| Completed
|-
| '''MGH'''
| 1.3
| Test/optimize registration method
| Year 3
| In Progress
|-
| '''MGH'''
| 1.4
| Apply registration on core 3 data
| Year 5
| In Queue
|-
| '''MGH'''
| 2
| '''Group DTI Statistics'''
|
|
|-
| '''MGH'''
| 2.1
| Develop group statistic method
| Year 2
| Partially Complete
|-
| '''MGH'''
| 2.2
| Apply on core 3 data
| Year 5
| In Queue
|-
| '''MGH'''
| 3
| '''Diffusion Segmentation '''
|
|
|-
| '''MGH'''
| 3.1
| Collect DTI/QBALL data
| Year 2
| Completed
|-
| '''MGH'''
| 3.2
| Develop/optimize segmentation algorithm
| Year 3
| Modified
|-
| '''MGH'''
| 3.3
| Integrate w/ tractography
| Year 4
| Modified
|-
| '''MGH'''
| 3.4
| Apply on core 3 data
| Year 5
| Modified
|-
| '''MGH'''
| 4
| '''Group Morphometry Statistics'''
|
|
|-
| '''MGH'''
| 4.1
| Develop/optimize statistics algorithms
| Year 3
| Modified
|-
| '''MGH'''
| 4.2
| Develop GUI for Linux
| Year 3
| Modified
|-
| '''MGH'''
| 4.3
| Slicer integration
| Year 3
| Modified
|-
| '''MGH'''
| 4.4
| Compile application on Windows
| Year 4
| Modified
|-
| '''MGH'''
| 5
| XNAT Desktop
| Years 4-5
|
|-
| '''MGH'''
| 5.1
| Establish requirements for desktop version of XNAT
| Years 4-5
| Complete
|-
| '''MGH'''
| 5.2
| Develop implementation plan for prototype
| Years 4-5
| Complete
|-
| '''MGH'''
| 5.3
| Implement prototype version
| Years 4-5
| Incomplete (in progress)
|-
| '''MGH'''
| 5.4
| Implement alpha version
| Year 5
| Incomplete
|-
| '''MGH'''
| 6
| XNAT Central
| Years 4-5
|
|-
| '''MGH'''
| 6.1
| Deploy XNAT Central, a public access XNAT host
| Years 4-5
| Complete
|-
| '''MGH'''
| 6.2
| Coordinate with NAMIC sites to upload project data
| Years 4-5
| Incomplete (ongoing)
|-
| '''MGH'''
| 6.3
| Continue developing XNAT Central based on feedback from NAMIC sites
| Years 4-5
| Incomplete (ongoing)
|-
| '''MGH'''
| 7
| NAMIC Kit integration
| Years 4-5
|
|-
| '''MGH'''
| 7.1
| Implement web services to exchange data with Slicer, Batchmake, and other client applications
| Years 4-5
| Incomplete (ongoing)
|-
| '''MGH'''
| 7.2
| Add XNAT Desktop to standard NAMIC kit distribution
| Year 5
| Incomplete
|-
|}

=== Timeline Modifications ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Modification'''
|-
| '''MIT'''
| 2.2
| Methods to compare shape statistics
| Removed, the effort refocused on registration necessary for population studies
|-
| '''MIT'''
| 2.4
| Software infrastructure to integrate shape analysis tools into the pipeline for population studies.
| New, morphed into collaboration with XNAT to provide more general population analysis tools. Partially completed.
|-
| '''MIT'''
| 4
| fMRI analysis including local and atlas-based priors for quantifying activation.
| New, partially completed. Refinements in progress. Clinical study with Core 1 is in progress.
|-
| '''Utah'''
| 2.2 (removed)
| Feature-based brain image registration.
| Shift emphasis to shape-based analysis/registration
|-
| '''Utah'''
| 2.1 (removed)
| Cortical filtering and feature detection
| Effort is subsumed by other Core 1 partners (e.g. see MGH/Freesurfer)
|-
| '''Utah'''
| 1.3 (removed)
| Segmentation of cortex/WM
| Effort is subsumed by other Core 1-2 partners (e.g. see EM-Segmenter)
|-
| '''Utah'''
| 3.0 (removed)
| Fast implmentations of PDEs
| Real-time filtering is demphasized in favor of shape/DTI analysis
|-
| '''Utah'''
| 1.5 (added)
| Joint analysis of DTI and functional data
| Opportunities/needs within various collaborations
|-
| '''Utah'''
| 2.1-2.3 (added, in place of cortical analysis)
| Shape analysis
| Nonparametric shape analysis added to address needs of core 3.
|-
| '''Utah'''
| 2.7
| Shape regression
| Extension/completion of framework. Opportunities/needs within various collaborations.
|-
| '''UNC'''
| 1.2
| Develop medially-based shape representation
| Remove
|-
| '''UNC'''
| 1.4
| Develop generic cortical correspondence framework (Years 3-5)
| New
|-
| '''UNC'''
| 2.4
| DTI Atlas Building (Years 2--4)
| New
|-
| '''GaTech'''
| 2.1
| FA analysis
| New
|-
| '''MGH'''
| 4.1 - 4.4
| Group Morphometry Statistics
| Added and then removed, based on personnel changes
|-
| '''MGH'''
| 5-7
| XNAT
| Added to support remote image database capabilities
|}

=== [[Core_1_Timeline_Notes|Core 1 Timeline Notes ]] ===

== Core 2: Engineering ==

=== Core 2 Timelines and Milestones ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Proposed time of completion'''
| '''Status'''
|-
| '''GE'''
| 1
| '''Define software architecture'''
|
|
|-
| '''GE'''
| 1
| Object design
| Yr 1
| Completed
|-
| '''GE'''
| 1
| Identify patterns
| Yr 3
| Patterns for processing scalar and vector images, models, fiducials complete. Patterns for diffusion weighted completed, fMRI ongoing.
|-
| '''GE'''
| 1
| Create frameworks
| Yr 3
| Frameworks for processing scalar and vector images, models, fiducials complete. Frameworks for diffusion weighted completed, fMRI ongoing.
|-
| '''GE'''
| 2
| '''Software engineering process'''
|
|
|-
| '''GE'''
| 2
| Extreme programming
| Yr 1-5
| On schedule, ongoing
|-
| '''GE'''
| 2
| Process automatiion
| Yr 3
| On schedule, ongoing
|-
| '''GE'''
| 2
| Refactoring
| Yr 3
| Complete
|-
| '''GE'''
| 3
| '''Automated quality system'''
|
|
|-
| '''GE'''
| 3
| DART deployment
| Yr 2
| Complete
|-
| '''GE'''
| 3
| Persistent testing system
| Yr 5
| Incomplete
|-
| '''GE'''
| 3
| Automatic defect detection
| Yr 5
| Incomplete
|-
| '''Kitware'''
| 1
| '''Cross-platform development'''
|
|
|-
| '''Kitware'''
| 1
| Deploy environment (CMake, CTest)
| Yr 1
| Complete
|-
| '''Kitware'''
| 1
| DART Integration and testing
| Yr 1
| Complete
|-
| '''Kitware'''
| 1
| Documentation tools
| Yr 2
| Complete
|-
| '''Kitware'''
| 2
| '''Integration tools'''
|
|
|-
| '''Kitware'''
| 2
| File Formats/IO facilities
| Yr 2
| Complete
|-
| '''Kitware'''
| 2
| CableSWIG deployment
| Yr 3
| Complete (integration ongoing)
|-
| '''Kitware'''
| 2
| Establish XML schema
| Yr 4
| Complete, refinements ongoing
|-
| '''Kitware'''
| 3
| '''Technology delivery'''
|
|
|-
| '''Kitware'''
| 3
| Deploy applications
| Yr 1
| Complete (ongoing)
|-
| '''Kitware'''
| 3
| Establish plug-in repository
| Yr 2
| Incomplete
|-
| '''Kitware'''
| 3
| Cpack
| Yr 4-5
| Incomplete
|-
| '''Isomics'''
| 1
| NAMIC builds of slicer
| Years 2--5
| Complete
|-
| '''Isomics'''
| 1
| Schizophrenia and DBP intefaces
| Year 3---5
| Completed (refinements ongoing)
|-
| '''Isomics'''
| 2
| ITK Integration tools
| Year 1---3
| Completed
|-
| '''Isomics'''
| 2
| Experiment Control Interfaces
| Year 2---5
| Migration from LONI to BatchMake Underway
|-
| '''Isomics'''
| 2
| fMRI/DTI algorithm support
| Year 2---5
| Completed DTI, fMRI Ongoing
|-
| '''Isomics'''
| 2
| New DBP algorithm support
| Year 2---5
| Ongoing
|-
| '''Isomics'''
| 3
| Compatible build process
| Year 1---3
| Completed
|-
| '''Isomics'''
| 3
| Dart Integration
| Year 1---2
| Completed (upgrades ongoing)
|-
| '''Isomics'''
| 3
| Test scripts for new code
| Year 2---5
| Ongoing
|-
| '''UCSD'''
| 1
| Grid computing---base
| Year 1
| Completed
|-
| '''UCSD'''
| 1
| Grid enabled algorithms
| Year 3
| First version (GWiz alpha) available - initial integration with Slicer3 and execution model.
|-
| '''UCSD'''
| 1
| Testing infrastructure
| Year 4
| Initiated
|-
| '''UCSD'''
| 2
| Data grid --- compatibility
| Year 2
| Completed
|-
| '''UCSD'''
| 2
| Data grid --- slicer access
| Year 2
| Completed for version 2.6. In progress for Slicer3
|-
| '''UCSD'''
| 3
| Data mediation --- deploy
| Year 1
| Incomplete (modfication below)
|-
| '''UCLA'''
| 1
| Debabeler functionality
| Year 1
| Continued Progress
|-
| '''UCLA'''
| 2
| SLIPIE Interpretation (Layer 1)
| Year 1--Year2
| In Progress
|-
| '''UCLA'''
| 3
| SLIPIE Interpretation (Layer 2)
| Year 1--Year2
| On Schedule
|-
| '''UCLA'''
| 3
| Developing ITK Modules
| Year2
| In Progress
|-
| '''UCLA'''
| 4
| Integrating SRB (GSI-enabled)
| Year2
| Completed
|-
| '''UCLA'''
| 5
| Integrating IDA
| Year2
| Completed
|-
| '''UCLA'''
| 5
| Integrating External Visualization Applications
| Year2
| Completed
|}

=== Core 2 Timeline Modifications ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Modification'''
|-
| '''Isomics'''
| 3
| Data mediation
| Delayed pending integration of databases into NAMIC infractructure
|}

=== [[Core_2_Timeline_Notes|Core 2 Timeline Notes ]] ===

== Core 3: Driving Biological Problems ==

The Core 3 projects submitted R01 style proposals, as specified in the RFA, and did not submit timelines.

== Core 4: Service ==

=== Core 4 Timelines and Milestones ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Proposed time of completion'''
| '''Status'''
|-
| '''Kitware'''
| 1
| '''Implement Development Farms'''
|
|
|-
| '''Kitware'''
| 1
| Deploy platforms
| Yrs 1
| Complete
|-
| '''Kitware'''
| 1
| Communications
| Yrs 1
| Complete, ongoing
|-
| '''Kitware'''
| 2
| '''Establish software process'''
|
|
|-
| '''Kitware'''
| 2
| Secure developer database
| Yr 1
| Complete, ongoing
|-
| '''Kitware'''
| 2
| Collect guidelines
| Yr 1
| Complete
|-
| '''Kitware'''
| 2
| Manage software submission process
| Yr 1
| Complete
|-
| '''Kitware'''
| 2
| Configure process tools
| Yr 1
| Complete
|-
| '''Kitware'''
| 2
| Survey community
| Yr 1
| Complete
|-
| '''Kitware'''
| 3
| '''Deploy NAMIC Tools'''
|
|
|-
| '''Kitware'''
| 3
| Toolkits
| Yr 1
| Complete
|-
| '''Kitware'''
| 3
| Integration tools
| Yr 1
| Complete
|-
| '''Kitware'''
| 3
| Applications
| Yr 1
| Complete
|-
| '''Kitware'''
| 3
| Integrate new computing resources
| Yr 1
| Complete
|-
| '''Kitware'''
| 4
| '''Provide support'''
|
|
|-
| '''Kitware'''
| 4
| Esablish support infrastructure
| Yrs 1--5
| On schedule, ongoing
|-
| '''Kitware'''
| 4
| NAMIC support
| Yr 1
| Complete
|-
| '''Kitware'''
| 5
| Manage NAMIC Software Releases
| Yrs 1--5
| On schedule, ongoing
|}

=== Core 4 Timeline Modifications ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Modification'''
|-
| Kitware
| 2-5
| Various
| Refined/modified the sub aims
|}

=== [[Core_4_Timeline_Notes|Core 4 Timeline Notes ]] ===

== Core 5: Training ==

=== Core 5 Timelines and Milestones ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Proposed time of completion'''
| '''Status'''
|-
| '''Harvard'''
| 1
| '''Formal Training Guidllines'''
|
|
|-
| '''Harvard'''
| 1
| Functional neuroanatomy
| Yr 1
| Complete
|-
| '''Harvard'''
| 1
| Clinical correlations
| Yr 1
| Complete
|-
| '''Harvard'''
| 2
| '''Mentoring'''
|
|
|-
| '''Harvard'''
| 2
| Programming workshops
| Yrs 1-5
| On schedule, ongoing
|-
| '''Harvard'''
| 2
| One-on-one mentoring, Cores 1, 2, 3
| Yrs 1-5
| On schedule, ongoing
|-
| '''Harvard'''
| 3
| '''Collaborative work environment'''
|
|
|-
| '''Harvard'''
| 3
| Wiki
| Yrs 1
| Complete
|-
| '''Harvard'''
| 3
| Mailing lists
| Yrs 1
| Complete
|-
| '''Harvard'''
| 3
| Regular telephone conferences
| Yrs 1-5
| On schedule, ongoing
|-
| '''Harvard'''
| 4
| '''Educational component for tools'''
|
|
|-
| '''Harvard'''
| 4
| Slicer training modules
| Yr 2-5
| Slicer 2.x tutorials complete, Two Slicer 3 tutorials complete, translation of 2.x tutorials to 3 is ongoing and on schedule
|-
| '''Harvard'''
| 5
| '''Demonstrations and hands-on training'''
|
|
|-
| '''Harvard'''
| 5
| Various workshops and conferences
| Yrs 1--5
| On schedule, ongoing
|}

=== Core 5 Timeline Modifications ===

None.

=== [[Core_5_Timeline_Notes|Core 5 Timeline Notes ]] ===

== Core 6: Dissemination ==

=== Core 6 Timelines and Milestones ===

{| border="1"
| '''Group'''
| '''Aim'''
| '''Milestone'''
| '''Proposed time of completion'''
| '''Status'''
|-
| Isomics
| 1
| Create a collaboration metholdology for NA-MIC
|
|
|-
| Isomics
| 1.1
| develop a selection process
| Yr 1
| Complete
|-
| Isomics
| 1.2
| guidelines to govern the collaborations
| Yr 1-2
| Complete
|-
| Isomics
| 1.3
| Provide on-site training
| Yr 1-5
| Complete for current tools (ongoing for tool refinement)
|-
| Isomics
| 1.4
| develop a web site infrastructure
| Yr 1
| Complete
|-
| Isomics
| 2
| Facilitate communication between NA-MIC developers and wider research community
|
|
|-
| Isomics
| 2.1
| develop materials describing NAMIC technology
| Yr 1-5
| On Schedule
|-
| Isomics
| 2.2
| participate in scientific meetings
| Yr 2-5
| On Schedule
|-
| Isomics
| 2.3
| Document interactions with external researchers
| Yr 2-5
| On Schedule
|-
| Isomics
| 2.4
| Coordinate publication strategies
| Yr 3-5
| On Schedule
|-
| Isomics
| 3
| Develop a publicly accessible internet resource of data, software, documentation, and publication of new discoveries
|
|
|-
| Isomics
| 3.1
| On-line repository of NAMIC related publications and presentations
| Yr 1-5
| On Schedule
|-
| Isomics
| 3.2
| On-line repository of NAMIC tutorial and training material
| Yr 1-5
| On Schedule
|-
| Isomics
| 3.3
| Index and a searchable database
| Yr 1-2
| Done
|-
| Isomics
| 3.4
| Automated feedback systems that track software downloads
| Yr 3
| Done
|}

=== Core 6 Timeline Modifications ===

None.

=== [[Core_6_Timeline_Notes|Core 6 Timeline Notes ]] ===

=Appendix A Publications (Mastrogiacomo)=
A list should be mined from the publications database and attached here in MS word format.

=Appendix B EAB Report and Response (Kapur)=
===EAB Report===

===Response to EAB Report===

DBP2:Queens:Roadmap

2009-05-12T23:08:16Z

Sidd queens:

Back to [[NA-MIC_Internal_Collaborations|NA-MIC Internal Collaborations]], [[DBP2:JHU|JHU DBP 2]]

=Johns Hopkins Roadmap Project (Transrectal MRI-guided robotic prostate biopsy)=

[[Image:TRProstateBiopsyRobot.jpg|thumb|320px|The transrectal prostate robot visualization inside SLICER.]]
== Objective ==

We would like to create an end-to-end application within the NA-MIC Kit to enable an existing transrectal prostate biopsy device to perform multi-parametric MRI guided prostate biopsy in closed-bore high-field MRI magnets.

This page describes the technology roadmap for robotic prostate biopsy in the NA-MIC Kit. The basic components necessary for this application are:

*'''Tissue segmentation''': Should be multi-modality, correcting for intensity inhomogeneity and work for both supine and prone patients, all imaged with an endorectal coil (ERC).
*'''Registration''': co-registration of MRI datasets taken at different times, in different body positions, and under different imaging parameters
*'''Prostate Measurement''': Measure volume of all segmented structures
*'''Biopsy Device Parameters''': Geometry, kinematics, and calibration/registration of the robot system must be available in some form. This capability is not currently part of the NA-MIC kit. The application will be modular, to enable use of different devices.
*'''Tutorial''': Documentation will be written for a tutorial and sample data sets will be provided to perform simulated biopsies.

== '''Roadmap''' ==

The primary goal for the roadmap is to develop an interventional module for Slicer3 for MRI-guided prostate biopsies. This module and the accompanying tutorial will serve as a template for interventional applications with Slicer3. The module will provide the necessary functionality for calibrating the robot to the MR scanner, planning biopsies, computing the necessary robot trajectory to perform each biopsy, and verification via post-biopsy images. We will obtain a biopsy plan from multi-parametric endorectal image volumes, executable with an existing prostate biopsy device. The system will be will be implemented under Slicer3 as an interactive application.

<center>
{|
|valign="top"|[[Image:Menard.jpg|thumb|350px|Prostate intervention (biopsy) in closed MR scanner.]]
|[[Image:Patient.jpg|Close-up of the transrectal procedure|thumb|420px]]
|}
</center>

<center>
{|
|valign="top"|[[Image:Robot.jpg|thumb|400px|Transrectal prostate intervention robot assembled.]]
|}
</center>

== '''Workflow''' ==
:*'''1. Calibrate and register robot:''' Acquire a calibration volume, calibrate/register robotic device to MR coordinate system
<center>
{|
|valign="center"|[[Image:Calib3DView3.JPG|thumb|350px|Calibrate and register robot markers]]
|[[Image:Calib3DViewOnly.JPG|Close-up of 3D view|thumb|300px]]
|}
</center>
:*'''2. Segment prostate:''' Acquire the target planning volume, segment prostate if necessary[[Image:TRPB_ProstateSegmentation.JPG|center|thumb|350px|Prostate segmentation]]
:*'''3. Pick biopsy targets, perform biopsy:''' Pick targets, and perform biopsy
<center>
{|
|valign="center"|[[Image:TRPBTarget3DView3.JPG|thumb|350px|Biopsy targets picked]]
|[[Image:TRPBTarget3DView1-AllTargets.JPG|Close-up of 3D view|thumb|350px]]
|}
</center>
<center>
{|
|valign="center"|[[Image:TRPBTarget3DView5.JPG|thumb|350px|Target selected, and needle trajectory visualized]]
|[[Image:TRPBTarget3DView7--TrajectoryCloseUp3D.JPG|Close-up of 3D view|thumb|350px]]
|}
</center>
<center>
{|
|valign="center"|[[Image:TRPBReformat1.JPG|thumb|350px|Use reformat widget to slice in arbitrary plane]]
|[[Image:TRPBArbitraryReformatPlaneIn3D.JPG|Slice reformated|thumb|350px]]
|}
</center>
:*'''4. Validate target:''' Acquire validation volume, while needle still in and then provide needle axis to perform validation

== '''Schedule''' ==

Data Collection: Done. Initial data available, hand segmented for ground truth.

Segmentation: We plan to use shape-based segmentation methods for the MRI prostate data. Several parts of the procedure have already been implemented with NA-MIC tools such as the conformal flattening procedure. Spherical wavelets for shape analysis are already available in ITK. Despeckling techniques will be used to enhance ultrasound imagery as a pre-processing step for segmentation of the prostate data.

System Implementation: Apart from the one research element (segmentation), the rest of the project is a massive software engineering effort, and will follow these major milestones and schedule:

:*'''10/15/2007:''' ''Application Workflow Development:'' Define the workflow for the application (David, Csaba, Gabor)

:*'''10/22/2007:''' '' Application Workflow Development:'' Create GUI templates for the workflow steps only till Calibration step (David)

:*'''12/15/2007:''' ''Device Modeling:'' Conversion of engineering data into VTK-viewable objects (David)

:*'''12/15/2007:''' ''Data Display:'' Provide display logic for targets and prostate outlines (David)

:*'''07/01/2008:''' ''Measurement Tools:'' Semi-automatic identification of fiducials via thresholding & centroids still not integrated inside SLICER (Csaba)

:*'''07/01/2008:''' ''Measurement Tools:'' Logic for robot registration with fiducials still not integrated inside SLICER (Csaba)

:*'''07/01/2008:''' ''Biopsy Planning/Targeting:'' Calculations for robot trajectory based on target position still not integrated inside SLICER (Csaba)

:*'''09/19/2008:''' ''Robot positioning:'' GUI targeting readouts for optical encoders (David, Siddharth)

:*'''11/10/2008:''' ''Robot positioning:'' Integrate optical encoders with our Slicer module (Siddharth)

:*'''11/20/2008:''' ''Application Workflow Development:'' Wizard GUI created for Targeting step, and Verification step (Siddharth)

:*'''11/20/2008:''' ''Software architecture compliance of module:'' Model View Controller pattern reflected in corresponding MRML, GUI, Logic (Siddharth)

:*'''11/26/2008:''' ''Measurement Tools:'' Integration of semi-automatic identification of fiducials in the SLICER module (Siddharth)

:*'''01/09/2009:''' ''Measurement Tools:'' Integration of prostate segmentation developed at Georgia Tech by Yi, Tennanbaum during the NAMIC project week in Utah (Siddharth, Yi): [[prostateSegmentationAHM2009|prostate segmentation AHM2009]]

:*'''02/02/2009:''' ''Measurement tools:'' Integration of Logic for robot marker registration based on segmentation in the SLICER module (Siddharth)

:*'''02/20/2009:''' ''Biopsy Planning/Targeting:'' Intergrated finding targeting parameters of robot for particular target (Siddharth)

:*'''03/10/2009:''' ''Biopsy Planning/Targeting:'' Ability to have multiple needles list, with each needle having multiple targets (Siddharth)

:*'''03/10/2009:''' ''Biopsy Planning/Targeting:'' Targets' list also implemented as fiducials lists for visualization of targets in 2d and 3D viewers (Siddharth)

:*'''03/20/2009:''' ''Software architecture compliance of module:'' MRML node updated to have all system state parameters ( volume nodes, fiducial lists (targets and markers), targeting parameters for each descriptor..) (Siddharth)

:*'''04/05/2009:''' ''Biopsy Planning/Targeting:'' On selecting target from the list, bring target to view in all viewers (Siddharth)

:*'''04/10/2009:''' ''Biopsy Planning/Targeting:'' Needle trajectory visualization (Siddharth)

:*'''04/15/2009:''' ''Application Workflow Development:'' Verification step GUI and functionality implemented (Siddharth)

:*'''04/23/2009:''' ''Application Workflow Development:'' Save experiment functionality implemented (Siddharth)

== '''Software''' ==

* The TRProstateBiopsy module is in the "Queens" directory of the NAMICSandBox - access [http://svn.na-mic.org/NAMICSandBox/trunk/Queens/TRProstateBiopsy/ online]

== '''Tutorial (end-to-end)''' ==

* Trans-rectal prosate biopsy module: [[Media:DBP2JohnsHopkinsTransRectalProstateBiopsy.pdf|TransRectalProstateBiopsyTutorial]] ''New''
* Trans-rectal prostate biopsy tutorial dataset: [[Media:TransRectalProstateBiopsyTutorialDataset.zip‎ |TransRectalProstateBiopsyTutorialDataset.zip‎ ]] ''New''

== '''Publications''' ==

* Krieger A, Susil RC, Menard C, Coleman JA, Fichtinger G, Atalar E, Whitcomb LL, Design of A Novel MRI Compatible Manipulator for Image Guided Prostate Intervention, IEEE Trans. Biomed. Eng. 2005; 52(2):306-313

* Susil RC, Ménard C, Krieger A, Coleman JA, Camphausen K, Choyke P, Ullman K, Smith S, Fichtinger G, Whitcomb LL, Coleman NC, Atalar E, Transrectal Prostate Biopsy and Fiducial Marker Placement in a Standard 1.5T MRI Scanner, J Urol. 2006 Jan;175(1):113-20

* GS Fischer, A Krieger, I Iordachita, LL Whitcomb, and G Fichtinger, "MRI Compatibility of Robot Actuation Techniques -- A Comparative Study", Eleventh International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Proceedings in Lecture Notes in Computer Science Vol. 5242, pp 509-517, Springer, 2008

* Siddharth Vikal, Steven Haker, Clare Tempany, Gabor Fichtinger, Prostate contouring in MRI guided biopsy, SPIE Medical Imaging 2009

* S. Gill, P Abolmaesumi, S Vikal, P Mousavi, G. Fichtinger, Intraoperative Prostate Tracking with Slice-to-Volume Registration in MRI, 20th International Conference of the Society for Medical Innovation and Technology (SMIT), Vienna, Austria, August 28-31, 2008, Electronic proceedings, ISBN 3-902087-25-0, pp. 154-158

* Mewes PW, Tokuda J, DiMaio SP, Fischer GS, Csoma C, Gobbi DG, Tempany CM, Fichtinger G, Hata N, An Integrated MRI and Robot Control Software for an MRI compatible Robot in Prostate Intervention, International Conference on Robotics and Automation - ICRA 2008, Pasadena, CA, pp 2950-2962, May 2008

== '''Team and Institutes''' ==

*PI: Gabor Fichtinger, Queen’s University (gabor at cs.queensu.ca)
*Co-I: Purang Abolmaesumi, Queen’s University (purang at cs.queensu.ca)
*Software Engineer Lead: Siddharth Vikal, Queen’s University (vikal at cs.queensu.ca
*Software Engineer: David Gobbi, Queen’s University (dgobbi at cs.queensu.ca)
*JHU Software Engineer Support: Csaba Csoma, Johns Hopkins University, csoma at jhu.edu
*NA-MIC Engineering Contact: Katie Hayes, MSc, Brigham and Women's Hospital, hayes at bwh.harvard.edu
*NA-MIC Algorithms Contact: Allen Tannenbaum, PhD, GeorgiaTech, tannenba at ece.gatech.edu
*Host Institutes: Queen's University & Johns Hopkins University

[[Category: Segmentation]] [[Category: Registration]] [[Category: Slicer]] [[Category: Prostate]]

File:TransRectalProstateBiopsyTutorialDataset.zip

2009-05-12T23:07:20Z

Sidd queens:

2009-04-29T18:40:25Z

Sidd queens:

Back to [[NA-MIC_Internal_Collaborations|NA-MIC Internal Collaborations]], [[DBP2:JHU|JHU DBP 2]]
__NOTOC__
[[Image:TRProstateBiopsyRobot.jpg|thumb|320px|The transrectal prostate robot visualization inside SLICER.]]

== Objective ==

We would like to create an end-to-end application within the NA-MIC Kit to enable an existing transrectal prostate biopsy device to perform multi-parametric MRI guided prostate biopsy in closed-bore high-field MRI magnets.

This page describes the technology roadmap for robotic prostate biopsy in the NA-MIC Kit. The basic components necessary for this application are:

*'''Tissue segmentation''': Should be multi-modality, correcting for intensity inhomogeneity and work for both supine and prone patients, all imaged with an endorectal coil (ERC).
*'''Registration''': co-registration of MRI datasets taken at different times, in different body positions, and under different imaging parameters
*'''Prostate Measurement''': Measure volume of all segmented structures
*'''Biopsy Device Parameters''': Geometry, kinematics, and calibration/registration of the robot system must be available in some form. This capability is not currently part of the NA-MIC kit. The application will be modular, to enable use of different devices.
*'''Tutorial''': Documentation will be written for a tutorial and sample data sets will be provided to perform simulated biopsies.

== '''Roadmap''' ==

The primary goal for the roadmap is to develop an interventional module for Slicer3 for MRI-guided prostate biopsies. This module and the accompanying tutorial will serve as a template for interventional applications with Slicer3. The module will provide the necessary functionality for calibrating the robot to the MR scanner, planning biopsies, computing the necessary robot trajectory to perform each biopsy, and verification via post-biopsy images. We will obtain a biopsy plan from multi-parametric endorectal image volumes, executable with an existing prostate biopsy device. The system will be will be implemented under Slicer3 as an interactive application.

<center>
{|
|valign="top"|[[Image:Menard.jpg|thumb|350px|Prostate intervention (biopsy) in closed MR scanner.]]
|[[Image:Patient.jpg|Close-up of the transrectal procedure|thumb|420px]]
|}
</center>

<center>
{|
|valign="top"|[[Image:Robot.jpg|thumb|400px|Transrectal prostate intervention robot assembled.]]
|}
</center>

== '''Description''' ==

== '''Schedule''' ==

Data Collection: Done. Initial data available, hand segmented for ground truth.

Segmentation: We plan to use shape-based segmentation methods for the MRI prostate data. Several parts of the procedure have already been implemented with NA-MIC tools such as the conformal flattening procedure. Spherical wavelets for shape analysis are already available in ITK. Despeckling techniques will be used to enhance ultrasound imagery as a pre-processing step for segmentation of the prostate data.

System Implementation: Apart from the one research element (segmentation), the rest of the project is a massive software engineering effort, and will follow these major milestones and schedule:

:*'''10/15/2007:''' ''Application Workflow Development:'' Define the workflow for the application (David, Csaba, Gabor)

:*10/22/2007:'' Application Workflow Development:'' Create GUI templates for the workflow steps only till Calibration step (David)

:*12/15/2007: ''Device Modeling:'' Conversion of engineering data into VTK-viewable objects (David)

:*12/15/2007: ''Data Display:'' Provide display logic for targets and prostate outlines (David)

:*07/01/2008: ''Measurement Tools:'' Semi-automatic identification of fiducials via thresholding & centroids still not integrated inside SLICER (Csaba)

:*07/01/2008: ''Measurement Tools:'' Logic for robot registration with fiducials still not integrated inside SLICER (Csaba)

:*07/01/2008: ''Biopsy Planning/Targeting:'' Calculations for robot trajectory based on target position still not integrated inside SLICER (Csaba)

:*09/19/2008: ''Robot positioning:'' GUI targeting readouts for optical encoders (David, Siddharth)

:*11/10/2008: ''Robot positioning:'' Integrate optical encoders with our Slicer module (Siddharth)

:*11/20/2008: ''Application Workflow Development:'' Wizard GUI created for Targeting step, and Verification step (Siddharth)

:*11/20/2008: ''Software architecture compliance of module:'' Model View Controller pattern reflected in corresponding MRML, GUI, Logic (Siddharth)

:*11/26/2008: ''Measurement Tools:'' Integration of semi-automatic identification of fiducials in the SLICER module (Siddharth)

:*01/09/2009: ''Measurement Tools:'' Integration of prostate segmentation developed at Georgia Tech by Yi, Tennanbaum during the NAMIC project week in Utah (Siddharth, Yi): [[prostateSegmentationAHM2009|prostate segmentation AHM2009]]

:*02/02/2009: ''Measurement tools:'' Integration of Logic for robot marker registration based on segmentation in the SLICER module (Siddharth)

:*02/20/2009: ''Biopsy Planning/Targeting:'' Intergrated finding targeting parameters of robot for particular target (Siddharth)

:*03/10/2009: ''Biopsy Planning/Targeting:'' Ability to have multiple needles list, with each needle having multiple targets (Siddharth)

:*03/10/2009: ''Biopsy Planning/Targeting:'' Targets' list also implemented as fiducials lists for visualization of targets in 2d and 3D viewers (Siddharth)

:*03/20/2009: ''Software architecture compliance of module:'' MRML node updated to have all system state parameters ( volume nodes, fiducial lists (targets and markers), targeting parameters for each descriptor..) (Siddharth)

:*04/05/2009: ''Biopsy Planning/Targeting:'' On selecting target from the list, bring target to view in all viewers (Siddharth)

:*04/10/2009: ''Biopsy Planning/Targeting:'' Needle trajectory visualization (Siddharth)

:*04/15/2009: ''Application Workflow Development:'' Verification step GUI and functionality implemented (Siddharth)

:*04/23/2009: ''Application Workflow Development:'' Save experiment functionality implemented (Siddharth)

== '''Software''' ==

* The TRProstateBiopsy module is in the "Queens" directory of the NAMICSandBox - access [http://svn.na-mic.org/NAMICSandBox/trunk/Queens/TRProstateBiopsy/ online]

== '''Tutorial (end-to-end)''' ==

* Trans-rectal prosate biopsy module: [[Media:DBP2JohnsHopkinsTransRectalProstateBiopsy.pdf|TransRectalProstateBiopsyTutorial]]
* Trans-rectal prostate biopsy tutorial dataset: [[Media:TRPBTutorialDataset.zip|TRPBTutorialDataset.zip]]

== '''Publications''' ==

* Krieger A, Susil RC, Menard C, Coleman JA, Fichtinger G, Atalar E, Whitcomb LL, Design of A Novel MRI Compatible Manipulator for Image Guided Prostate Intervention, IEEE Trans. Biomed. Eng. 2005; 52(2):306-313

* Susil RC, Ménard C, Krieger A, Coleman JA, Camphausen K, Choyke P, Ullman K, Smith S, Fichtinger G, Whitcomb LL, Coleman NC, Atalar E, Transrectal Prostate Biopsy and Fiducial Marker Placement in a Standard 1.5T MRI Scanner, J Urol. 2006 Jan;175(1):113-20

* GS Fischer, A Krieger, I Iordachita, LL Whitcomb, and G Fichtinger, "MRI Compatibility of Robot Actuation Techniques -- A Comparative Study", Eleventh International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Proceedings in Lecture Notes in Computer Science Vol. 5242, pp 509-517, Springer, 2008

* Siddharth Vikal, Steven Haker, Clare Tempany, Gabor Fichtinger, Prostate contouring in MRI guided biopsy, SPIE Medical Imaging 2009

* S. Gill, P Abolmaesumi, S Vikal, P Mousavi, G. Fichtinger, Intraoperative Prostate Tracking with Slice-to-Volume Registration in MRI, 20th International Conference of the Society for Medical Innovation and Technology (SMIT), Vienna, Austria, August 28-31, 2008, Electronic proceedings, ISBN 3-902087-25-0, pp. 154-158

* Mewes PW, Tokuda J, DiMaio SP, Fischer GS, Csoma C, Gobbi DG, Tempany CM, Fichtinger G, Hata N, An Integrated MRI and Robot Control Software for an MRI compatible Robot in Prostate Intervention, International Conference on Robotics and Automation - ICRA 2008, Pasadena, CA, pp 2950-2962, May 2008

== '''Team and Institutes''' ==

*PI: Gabor Fichtinger, Queen’s University (gabor at cs.queensu.ca)
*Co-I: Purang Abolmaesumi, Queen’s University (purang at cs.queensu.ca)
*Software Engineer Lead: Siddharth Vikal, Queen’s University (vikal at cs.queensu.ca
*Software Engineer: David Gobbi, Queen’s University (dgobbi at cs.queensu.ca)
*JHU Software Engineer Support: Csaba Csoma, Johns Hopkins University, csoma at jhu.edu
*NA-MIC Engineering Contact: Katie Hayes, MSc, Brigham and Women's Hospital, hayes at bwh.harvard.edu
*NA-MIC Algorithms Contact: Allen Tannenbaum, PhD, GeorgiaTech, tannenba at ece.gatech.edu
*Host Institutes: Queen's University & Johns Hopkins University

[[Category: Segmentation]] [[Category: Registration]] [[Category: Slicer]] [[Category: Prostate]]