NAMIC Wiki - User contributions [en]

AHM 2010

2010-01-07T15:32:23Z

Hazlett: /* Agenda */

__NOTOC__
== Introduction ==
{| border="00" cellpadding="8" cellspacing="0"
|-
| rowspan="2" align="left" | '''This is the home page for the 2010 NA-MIC all hands meeting (AHM).''' NA-MIC participants meet for a AHM once a year. The purpose of the AHM is to coordinate, discuss plans and report to NIH officers and the external advisory board (EAB). The external advisory board meets with the NA-MIC leadership immediately after the AHM. In parallel, NA-MIC is organizing a project week. These events, with the exception of the EAB meeting, are open to collaborators and potential collaborators.

For more information about the project weeks in general, click [[Engineering:Programming_Events|'''here''']].

For information about the January 2010 project week, see below or click [[2010_Winter_Project_Week|'''here''']].

For information about Utah as a travel destination click [http://www.utah.com '''here'''].
| style="background: #ebeced" align="center"| [[Image:SLC.jpg|center|350px|View of the City]]
|-
| style="background: #ebeced"|The 2010 AHM, EAB and Project Week will be held '''January 4-8 2010''', in '''Salt Lake City''', Utah.
|}

== Agenda==

'''wireless connection: Select hotel wireless network - this gets you a non-secure line - Start your web browser, enter services.globalsuite.net in the address bar - Location is Capital Ball Room, group is Utah, passcode is Utah, both are case sensitive. If you have trouble, ask Deb or another attendee for help''' 

{| border="1" cellpadding="5"
|- style="background:#ebeced; color:black" align="left"
| style="width:4%" | '''Time'''
| style="width:12%" | '''Monday, January 4'''
| style="width:12%" | '''Tuesday, January 5'''
| style="width:12%" | '''Wednesday, January 6'''
| style="width:32%" | '''Thursday, January 7 '''
| style="width:12%" | '''Friday, January 8'''
|-
| style="background:#ffffdd; color:black"|
| style="background:#b4d597; color:#522200"| '''[[2010_Winter_Project_Week|Project Activities]] ''' in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Capitol B-C]
| style="background:#b4d597; color:#522200"| '''[[2010_Winter_Project_Week|Project Activities]] ''' in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Capitol B-C]
| style="background:#b4d597; color:#522200"| '''[[2010_Winter_Project_Week|Project Activities]] ''' in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Capitol B-C]
| style="background:#fff6a6; color:#522200"| '''AHM''' in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/Capitol A-B], [[2010_EAB|'''EAB''']] in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Olympus B]
'''[[2010_Winter_Project_Week|Project Activities]] ''' in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Olympus A]
| style="background:#b4d597; color:#522200"|'''[[2010_Winter_Project_Week|Project Activities]] ''' in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Capitol B-C]
|-
| style="background:#ffffdd; color:black"|'''7:30-8:00'''
| style="background:#ebeced; color:black"|
| style="background:#b4d597; color:black"| Breakfast
| style="background:#b4d597; color:black"| Breakfast
| style="background:#fff6a6; color:black"| Breakfast
| style="background:#b4d597; color:black"| Breakfast
|-
| style="background:#ffffdd; color:black"|'''8:00-10:00'''
| style="background:#ebeced; color:black"|'''9:30''' Core 1 and 2 PI closed session in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Capitol A]
| style="background:#b4d597; color:black"|Project Work '''8:00-8:30''' [[AHM2010:NA-MIC Kit Update| NA-MIC Kit Update]] (Jim Miller, Steve Pieper) 
'''8:30-10:00''' [[AHM2010:QtTutorial#Qt_tutorial|Qt Tutorial]] (Julien Finet)
| style="background:#b4d597; color:black"|Project Work '''8:00-9:00''' [[AHM2010:Tutorial Contest|Tutorial Contest]] [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Amethyst 1]
'''9:00-10:00''' [[AHM2010:AnnotationBreakout| Slicer Annotation Breakout (Kilian Pohl) ]] (Capital Ballroom A)
| style="background:#fff6a6; color:black"|'''8:00''' [[AHM 2010 Introduction|Introduction]], [http://www.spl.harvard.edu/~kikinis Ron Kikinis] 
'''8:05''' [[media:namic_highlights_jan10_tmp.ppt|NA-MIC Highlights]] ([http://www.cs.utah.edu/~whitaker/ Ross Whitaker]) 
'''8:20''' [[Media:2010_NAMICAHM_StynerValidation.ppt‎ |Validation]] ([http://www.cs.unc.edu/~styner/ Martin Styner]) 
'''Roadmap Projects''' 
'''8:35''': [[Media:Gabor-AHM-2010.ppt ‎|JHU/Queens]] ([http://research.cs.queensu.ca/~gabor/ Gabor Fichtinger]) 
'''8:55''': [[media:UNC DBP2 AHM Jan10.ppt|UNC]] ([http://www.med.unc.edu/psych/directories/faculty/hazlett/ Heather Cody]) 
'''9:15''': [[media:NA-MIC_Kubicki_2010.ppt|PNL]] ([http://pnl.bwh.harvard.edu/people/profiles/kubicki.html Marek Kubicki]) 
'''9.35''': [[AHM2010:Mind|Mind Institute]] ([http://www.mrn.org/principle-investigators/h.-jeremy-bockholt.html Jeremy Bockolt]) 
| style="background:#b4d597; color:black"|Project Work 
|-
| style="background:#ffffdd; color:black"|'''10:00-10:30'''
| style="background:#ebeced; color:black"| Core 1 and 2 PI closed session
| style="background:#b4d597; color:black"| Coffee
| style="background:#b4d597; color:black"| Coffee
| style="background:#fff6a6; color:black"| Coffee
| style="background:#b4d597; color:black"| '''10:00''' Project Review
|-
| style="background:#ffffdd; color:black"|'''10:30-12:00'''
| style="background:#ebeced; color:black"| Core 1 and 2 PI closed session
| style="background:#b4d597; color:black"|Project Work 
'''10:30-11:30'''[[2010_Winter_Project_Week_Slicer_Dashboard|Test We Must]] (Luis)
 
'''11:30-12:30'''[[AHM_2010_Tutorial_Polishing|Tutorial Polishing]] (Stuart Wallace, Sonia, Randy) 

| style="background:#b4d597; color:black"|Project Work 

'''10:30-11:30'''[[2010_Winter_Project_Week_DTI_Breakout_Session|DTI Breakout Session]] (Sonia Pujol, Lauren O'Donnell)
 

| style="background:#fff6a6; color:black"|
'''Collaborations''' 
'''10:30''': [[Media:NA-MIC-Mesh-Collaboration-2010.ppt|Iowa]] ([http://www.engineering.uiowa.edu/faculty-staff/profile-directory/bme/grosland_n.php Nicole Grosland]) 
'''10:40''': [[Media:NAMIC External-Wyatt2010.ppt|Wake Forest]] ([http://www.ece.vt.edu/faculty/wyatt.html Chris Wyatt]) 
'''10:50''': [[Media:NA-MIC_Cleary_RF_Ablation_Georgetown_Jan_2010.pdf|Georgetown]] ([http://www.isis.georgetown.edu/PORTALVBVS/DesktopDefault.aspx?tabindex=2&tabid=8 Kevin Cleary]) 
'''11:00''': [[Media:NAMIC-Shen-2010-01-07.ppt|UNC]]
([http://www.med.unc.edu/~dgshen Dinggang Shen]) 
'''11:10''': [[media:2010-NA-MIC-Prince.ppt|JHU]] ([http://iacl.ece.jhu.edu/~prince/ Jerry Prince]) 
'''11:20''': [[media:1120NAMIC-VC-2010-v02.ppt|MGH]] (Hiroyuki Yoshida) 
'''11:30''': [[media:Miller-NCBC.pdf|JHU]] (Michael Miller) 
'''11:40''': [[media:Lainhart_NAMIC_AHM2010.ppt|Utah]] (Janet Lainhart) 
'''11:50''': [[media:AHM2010-COPDGeneCollaboration.ppt|BWH]] (Raul San Jose) 
| style="background:#b4d597; color:black"|Project Work
|-
| style="background:#ffffdd; color:black"|'''12:00-1:00'''
| style="background:#b4d597; color:black"| Lunch
| style="background:#b4d597; color:black"| Lunch 
| style="background:#b4d597; color:black"| Lunch 
| style="background:#fff6a6; color:black"| Lunch
| style="background:#b4d597; color:black"| Adjourn
|-
| style="background:#ffffdd; color:black"|'''1:00-3:00'''
| style="background:#b4d597; color:black"|[[2010_Winter_Project_Week|Begin Project Activities]]: '''2:00''' Introduce Projects and Participants
| style="background:#b4d597; color:black"|'''1:00-2:00''' [[AHM2010:RegistrationBreakout|Registration Breakout (Dominik, Casey)]] in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/Amethyst Amethyst 1]
| style="background:#b4d597; color:black"| Project Work (Capital Ballroom A) '''12:30-1:30''' [[AHM2010:DiffusionDatatypesBreakout|Diffusion Datatypes Breakout (Lauren O'Donnell)]] '''2:00-3:00''' ''Grid Wizard Tutorial (Marco Ruiz) -- canceled''
| style="background:#fff6a6; color:black"|
'''Tools and Tutorials''' 
'''1:00-1:20''' [[media:2010 NA-MIC AHM Slicer.ppt|Slicer]] ([http://www.spl.harvard.edu/~pieper Steve Pieper]) 
'''1:20-1:40''' [[media:AHM2010-Plug-ins.ppt|Interfacing with Slicer]] ([http://wiki.na-mic.org/Wiki/index.php/User:Millerjv Jim Miller]) 
'''1:40-2:00''' [[AHM2010:Non-interactive-tools|Non-interactive tools]] ([[media:2010_AHM_Kitware-NonInteractiveTools.ppt|ppt]]) ([http://www.kitware.com/company/team/aylward.html Stephen Aylward]) 
'''2:00-2:30''' [[media:NA-MIC TrainingCore DTIValidation Update AHM2010 SoniaPujol.ppt| Training Core & DTI Tractography Validation Update]] ([http://lmi.bwh.harvard.edu/~spujol/ Sonia Pujol]) 
'''2:30-3:00''' [[AHM2010:Tutorial-Contest-Winners|Tutorial Contest]]: Presentations by the winners ([http://www.nmr.mgh.harvard.edu/martinos/people/showPerson.php?people_id=64 Randy Gollub]) 
| style="background:#ebeced; color:black"|
|-
| style="background:#ffffdd; color:black"|'''3:00-3:30'''
| style="background:#b4d597; color:black"| Coffee
| style="background:#b4d597; color:black"| Coffee
| style="background:#b4d597; color:black"| Coffee
| style="background:#fff6a6; color:black"| Coffee
| style="background:#ebeced; color:black"|
|-
| style="background:#ffffdd; color:black"|'''3:00-5:00'''
| style="background:#b4d597; color:black"|
| style="background:#b4d597; color:black"|'''3:30-4:30''' [[AHM2010:QtTutorial#Qt_technical_workshop|Qt Workshop]] (Julien Finet) Project Work [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Amethyst 1]
| style="background:#b4d597; color:black"|Project Work '''3:00-5:00''' [[AHM2010:SlicerHandson|Slicer Hands-on with Ron]] Breakout [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Amethyst 1]
| style="background:#fff6a6; color:black"|[[2010_EAB|EAB]] [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Olympus B] '''3:00-4:00''' Discussion with NA-MIC Leadership '''4:00-5:00''' Closed Session
| style="background:#ebeced; color:black"|
|-
| style="background:#ffffdd; color:black"|'''05:00-07:00'''
| style="background:#b4d597; color:black"|
| style="background:#b4d597; color:black"|
| style="background:#b4d597; color:black"|'''05:00-06:00''' Breakout(Capital Ballroom A)
| style="background:#fff6a6; color:black"|'''6:00''' Optional: [http://www.skisaltlake.com/murphys.htm Beer at Murphy's] (like last year)
| style="background:#ebeced; color:black"|
|}

Please note that there will be a Core 1&2 Site PI Retreat on the morning of Monday, January 4th. This is a closed session for Core 1&2 Site PIs, with no delegates. The topic is the competitive renewal.

== Dates Venue Registration ==
'''Dates:'''
* The All Hands Meeting and External Advisory Board Meeting will be held on '''Thursday, January 7th'''.
* Project Activities will be held rest of the week between '''Monday, January 4th and Friday, January 8th'''.

'''Venue:''' The venue for the meeting is [http://www.marriott.com/hotels/travel/slccc-salt-lake-city-marriott-city-center/ Marriott City Center, Salt Lake City, Utah] Marriott City Center, Salt Lake City, Utah. [http://marriott.com/property/meetingsandevents/floorplans/slccc (Floorplan)]. Please either call the hotel at +1-877-905-4491 (toll free) or book online at http://www.marriott.com/hotels/travel/slccc-salt-lake-city-marriott-city-center/?toDate=1/8/10&groupCode=NAMNAMA&fromDate=1/3/10&app=resvlink'''by December 14, 2009''' using the code NAMNAMA to get rooms at $139/night. Please note that we do need attendees to use this hotel in order to not incur additional charges for the use of conference rooms. Please also note that the room rate without the code is ~$200/night and we will not be able to help you get a discount if you don't book in time.

'''Registration:''' We are charging a registration fee to all participants ($200 for AHM only, and $450 for AHM+). The fee covers the costs of the facilities and food provided. In order to keep the fee low, we need to get a sufficient number of hotel nights by our participants. See above for more on this. Please click http://www.sci.utah.edu/namic2010.html for online registration. This registration must be completed by December 12, 2009. 

'''Connectivity'''
We have three wireless access points at the AHM. Two of them are located in the capital ballroom. One is named capital-ballroom, the other is named capital-ballroom2. If one access point doesn't let you connect it is probably overloaded. In that case, please try connecting to the other one.

== Attendees ==

'''The registered attendee list will be posted here by the organizers. DO NOT add your name to this list yourself.'''
 
#Ron Kikinis, BWH
#Katie Mastrogiacomo, BWH
#Sonia Pujol, BWH
#Nicole Aucoin, BWH
#Katie Hayes, BWH
#Lauren O'Donnell, BWH
#Sandy Wells, BWH
#Andrew Rausch, PNL
#Alexander Zaitsev, BWH
#Andriy Fedorov, BWH
#Raul San Jose Estepar, BWH (AHM only)
#Wendy Plesniak, BWH
#Peter Savadjiev, BWH
#Petter Risholm, BWH
#Dominik Meier, BWH
#Junichi Tokuda, BWH
#Scott Hoge, BWH
#Ben Schwartz, BWH
#Marek Kubicki, BWH
#Sylvain Bouix, BWH
#James Malcolm, BWH
#James Ross, BWH (AHM only)
#Daniel Haehn, BWH
#Sandy Napel, EAB (AHM only)
#Bill Lorensen, EAB
#Fred Prior, EAB (AHM only)
#Chris Johnson, EAB
#Godfrey Pearlson, EAB (AHM only)
#Morry Blumenfeld, EAB (AHM only)
#Kevin Cleary, Georgetown (AHM only)
#Heather Hazlett, UNC (AHM only)
#Chris Wyatt, Virginia Tech (AHM only)
#Gabor Fichtinger, Queens (AHM only)
#Zohara Cohen, NIBIB (AHM only)
#Yi Gao, Georgia Tech
#Sylvain Jaume, MIT CSAIL
#Vandana Mohan, Georgia Tech
#Min Chen, Johns Hopkins
#Deepika Mahalingam, UNC-Chapel Hill
#SUN HYUNG KIM, UNC at Chapel Hill
#Clement Vachet, UNC Chapel Hill
#Will Schroeder, Kitware
#Casey Goodlett, Kitware
#Yang Zhang, The University of Western Australia M050
#Corentin Hamel, UNC
#Martin Styner, UNC
#Julien Finet, Kitware Inc.
#LUIS IBANEZ, KITWARE Inc.
#Stephen Aylward, Kitware, Inc.
#Garrett Larson, UNC
#Steve Pieper, Isomics, Inc.
#Daniel Marcus, Washington University
#Curtis Lisle, KnowledgeVis, LLC
#Andras Lasso, Queens University
#GOPALKRISHNA VENI, University of Utah
#Hans Johnson, University of IowaPsychiatry
#Julien Jomier, Kitware Inc.
#Adam Wittek, The University of Western Australia M050
#alexandre gouaillard, CoSMo Software
#Kilian Pohl, IBM
#German Cavelier, NIMHNIHDNBBS (AHM only)
#Jean-Christophe Fillion-Robin, Kitware Inc. (AHM only)
#Daniel Blezek
#Arnaud Gelas, Harvard Medical School
#Behnood Gholami, Georgia Tech
#Hans Johnson, University of Iowa Psychiatry
#Peter Karasev, Georgia Tech
#Ivan Kolesov, GaTech
#James Miller
#Timothy Olsen, Washington University STL
#John Paulett, Washington University
#Nicolas Rannou,Harvard Medical School
#Yundi Shi, UNC
#Lydie Souhait, Harvard Medical School
#Duygu Tosun, UCSFCIND
#Alexander Yarmarkovich, Isomics
#Linda Krigbaum, National Library of Medicine (AHM only)
#Dinggang Shen, UNC-Chapel Hill
#Jeremy Bockholt, The Mind Research Network
#Elvis Chen, Robarts Research Institute
#Michal Depa, MIT
#James Fishbaugh, SCI Institute
#Guido Gerig, SCI Institute
#Nicole Grosland, The University of Iowa, CCAD
#Nathan Hageman, UCLA School of Medicine
#Xiaoxing Li, Virginia Tech
#Mahnaz Maddah, General Electric Co.
#Vincent Magnotta, The University of Iowa, CCAD
#Harish Doddi, Stanford University
#bjoern menze, CSAIL MIT
#Saikat Pal, Stanford University
#Marcel Prastawa, SCI Institute
#Andrzej Przybyszewski, UMASS
#Marco Ruiz, UCSD
#Mark Scully, The Mind Research Network
#Anuja Sharma, SCI Institute
#Gregory Sharp, MGH
#Xiaodong Tao, General Electric Co.
#Guorong Wu, UNC-Chapel Hill
#Minjeong Kim, UNC Chapel Hill
#Josh Cates, SCI Institute
#Stuart Wallace, MGH
#Michael Ackerman, National Library of Medicine
#Yong Zhang
#Michael Miller, Johns Hopkins University
#Jerry Prince, Johns Hopkins University
#Ross Whitaker, SCI Institute

 
'''The registered attendee list will be posted here by the organizers. DO NOT add your name to this list yourself.'''

File:UNC DBP2 AHM Jan10.ppt

2010-01-07T14:59:36Z

Hazlett: uploaded a new version of "File:UNC DBP2 AHM Jan10.ppt": UNC DBP2 presentation for AHM Jan 2010

Presentation from UNC DBP2 for AHM 2010

File:UNC DBP2 AHM Jan10.ppt

2010-01-07T06:36:05Z

Hazlett: Presentation from UNC DBP2 for AHM 2010

Presentation from UNC DBP2 for AHM 2010

File:UNC DBP2 AHM Jan09.ppt

2009-01-08T06:54:40Z

Hazlett: UNC DBP site presentation for AHM Jan 2009

UNC DBP site presentation for AHM Jan 2009

2008 Annual Scientific Report

2008-05-14T20:52:41Z

Hazlett: /* Clinical Component (Hazlett) */

Back to [[2008_Progress_Report]]

=Guidelines for preparation=

*[[2008_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 2007 report, which is provided here for reference: [[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 in its fourth year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The first driving biological projects (DBPs) three years for Center were inspired by schizophrenia research. In the fourth year new DBPs have been added. Three are centered around diseases of the brain: (a) brain lesion analysis in neuropschiatric systemic lupus erythematosus; (b) a study of cortical thickness for autism; and (c) stochastic tractography for VCFS. In an very new direction, we have added DBP on the prostate: brachytherapy needle positioning robot integration.

We briefly summarize the work of NAMIC during the four years of its existence. In the year one of the Center, alliances were forged amongst the cores and constituent groups in order to integrate the efforts of the cores and to define the kinds of tools needed for specific imaging applications. The second year emphasized the identification of the key research thrusts that cut across cores and were driven by the needs and requirements of the DBPs. This led to the formulation of the Center's four main 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 the collaborative efforts in order to give solutions to the various brain-oriented DBPs.

Year four has seen progress with the work of our new DBPs. As alluded to above these include work on neuropsychiatric disorders such as Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), as well as the prostate interventional work (Johns Hopkins and Queens Universities). We already have a number of publications as is indicated on our publications page, and software development is continuing as well.

In the next section (Section 3), we summarize this year’s progress on the four roadmap projects listed above: Section 3.1 stochastic tractography for Velocardiofacial Syndrome, Section 3.2 brachytherapy needle positioning for the prostate, Section 3.3 brain lesion analysis in neuropschiatric systemic lupus erythematosus, and Section 3.4 cortical thickness for autism. Next in Section 4, we describe recent 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). In Section 4.5, we outline some of the other key projects, in Section 4.6 some key highlights including the integration of the EM Segmentor into Slicer, and in Section 4.7 the impact of biocomputing at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 4.8, provides a timeline of Center activities.

=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 would be usefull in evaluating anatomical connectivity between segmented cortical regions of the brain. The ultimate goal of our program is to understand anatomical connectivity similarities and differences between genetically related schizophrenia and velocardio-fatial syndrome. Thus we plan to use the "stochastic tractography" tool for the analysis of abnormalities in integrity, or connectivity, provided by arcuate fasciculus, fiber bundle involved in language processing, in schizophrenia and VCFS.

===Algorithm Component (Golland)===
Over the last year, algorythm has been tested first on the already available to NAMIC dataset of schizophrenia subjects acquired on 1.5T. This step allowed us to optimize algorythm to our dataset, as well as to develop the pipeline for data analysis that would be then easilly transferable to other datasets, and structures. For this, as well as other applications, we use gray matter labels derived from either automatic or manual segmentation of structural MRI. Thus the first step was to obtain registration between diffusion and anatomical data. Right now, demon's registration alogythm that is part of slicer is being used, but we are also testing b-spline registration, as well as fluid? registration for this purpose. Next step, also accomplished this last year, was to apply the alogythm to new, higher resolution NAMIC dataset, and study smaller white matter connections including cingulum bundle, arcuate fasciculus, uncinate fasciculus and internal capsule. This step was accomplished and data presented at the Santa Fee meeting in October 2007. Algorythm was also additionally tested on the phantom, where differences in coordinate systems were debugged. Upon the completion of testing phase, we started analysis of arcuate fasciculus, language related fiber bundle, in new 3T, high resolution dataset. Additional step turned out to be required in preprocessing, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high, thus white matter sesgmentation was performed using free surfer, and co-registered with DTI. Our current work focuses on better registration alogythms, as well as the way to parametrize tracts, in order to obtain FA measurements along the tracts.

===Engineering Component (Davis)===
Stochastic Tractography slicer module has been finished, and presented at the AHM in SLC. Its now part of the slicer2.8 and slicer3. Module documentation have been also created.
Current engeneering effords are concentrated on maintaining the module, optimizing it for working with other data formats, and adding new functionality, such as better registration, distortion correction and ways of extracting and measuring FA along the tracts.

===Clinical Component (Kubicki)===
Over the last year, algorythm has been tested first on the already available to NAMIC dataset of schizophrenia subjects acquired on 1.5T. Anterior Limb of the internal capsule, large structure connecting thalamus with frontal lobe, have been extracvted, and analyzed in group of 20 schizphrenics, and 20 control subjects, and results showing group differences in Fractional Anisotropy presented at the ACNP symposium in December 2007. Next, stochastic tractography was tested, and optimized for new, high resolution DTI dataset acquired on 3T GE magnet. Upon the completion of testing phase, we started analysis of arcuate fasciculus, language related fiber bundle, in 20 controls and 20 chronic schizphrenics. Whole brain as well as white matter segmentations, using freesurfer, as well as automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Supperior Temporal Gyri), as well as another ROI that would guide the tract ("waypoint" ROI) have been generated for all subjects. Finally, paths of interest were generated, and averaged FA extracted for each tract. Preliminary data based on 7 patients and 12 controls were presented at the AHM in January 2008, study is currently under way.

===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: Brachytherapy Needle Positioning Robot Integration (Fichtinger)==
===Overview (Fichtinger)===
===Algorithm Component (Tannenbaum)===
We have worked on both the segmentation and the registration of the prostate from MRI and ultrasound data. We explain each of the steps now.

====Prostate Segmentation====

We first must extract the prostate. We have considered three possible methods: a combination of a combination of Cellular Automata(CA also known as Grow Cut) with Geometric Active Contour(GAC) methods; employing an ellipsoid to match the prostate in 3D image; shape based approach using spherical wavelets. More details are given below and images and further details may be found at [[Projects:ProstateSegmentation|GaTech Algorithm Prostate Segmentation]].

1. A cellular automata algorithm is used to give an initial segmentation. It begins with a rough manual initialization and then iteratively classifies all pixels into object and bacground until convergence. It effectively overcomes the problems of weak boundaries and inhomogeneity within the object or background. This in turn is fed into Geometric Active Contour for finer tuning. We are initially using the edge-based minimal surface pproach (the generalization of the standard Geodesic Active Contour model) which seems to give very reasonable results. Both steps of the algorithm algorithm are implemented in 3D. A ITK-Cellular Automata filter, dealing with N-D data, has already been completed and submitted to the NA-MIC SandBox.

2. Spherical wavelets have proven to be a very natural way of representing 3D shapes which are compact and simply connected (topological spheres). We developed a segmentation framework using this 3D wavelet representation and multiscale prior. The parameters of our model are the learned shape parameters based on the spherical wavelet coefficients}, as well as pose parameters that accommodate for shape variability due to a similarity transformation (rotation, scale, translation) which is not explicitly modeled with the shape parameters. The transformed surface based on the pose parameters. We used a region-based energy to drive the evolution of the parametric deformable surface for segmentation. Our segmentation algorithm deforms an initial surface according to the gradient flow that minimizes the energy functional in terms of the pose and shape parameters. Additionally, the optimization method can be applied in a coarse to fine manner. Spherical wavelets and conformal mappings are
already part of the NA-MIC SandBox.

3. The third method is very closely related to the second. It is based on the observation that the prostate may be roughly modelled as an ellipsoid. One can then employing this ellipsoid model coupled with a local/global segmentation energy approach which we have developed this year, as the basis of a segmentation procedure. Because of the local/global nature of the functional and the implicit introduction of scale this methodology may be very useful for MRI prostate data.

====Prostate Registration====

The registration and segmentation elements of our algorithm are difficult to separate. Thus for the 3D shape-driven segmentation part, the shapes must first be aligned through a conformal and area-correction alignment process. The prostate presents a number of difficulties for traditional approaches since there are no easily discernable landmarks. On the other hand, we observed that the surface of the prostate is almost half convex and half concave. The concave region may be captured and used to register the shapes, thus we register the whole shape by registering a certain region on it. Such concave region is characterized by its negative mean curvature. We treat the mean curvature as a scalar field defined on the surface, and we have extended the Chan-Vese method (in which one wants to separate the means with respect to the regions defined by the interior and exterior of the evolving active contour) to the case at hand on the prostate surface. The method is implemented in C++ and it successfully extracts the concave surface region. This method could also be used to exact regions on surface according to any feature charactered by a scalar field defined on the surface.

In order incorporate the extracted region as landmarks into the registration process, instead of matching two binary images directly, we transform the binary images into a form to highlight the boundary region. This is done by applying a Gauss function on the (narrow band) of the signed distance function of the binary image. The transformed image enjoys the advantages of both the parametric and implicit representations of shapes. Namely it has compact description, as the parametric representation does, and as in the implicit representation it avoids the correspondence problem. Moreover we incorporate the extracted concave regions into such images for registration which leads to a better result.

Finally, in the past year we have developed a particle filtering approach for the general problem of registering two point sets that differ by a rigid body transformation which may be very useful for this project. Typically, registration algorithms compute the transformation parameters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem, in which the corresponding distribution can naturally be estimated using a particle filter. We treat motion as a local variation in pose parameters obtained from running several iterations of the standard Iterative Closest Point (ICP) algorithm. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence often found in local optimizer functions used to tackle the registration task. In contrast with other techniques, this approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temporal 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.

===Engineering Component (Hayes)===
===Clinical Component (Fichtinger)===
===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)===
===Algorithm Component (Whitaker)===
===Engineering Component (Pieper)===
===Clinical Component (Bockholt)===
===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.
Tissue segmentation: We have successfully adapted the UNC segmentation tool called itkEMS to Slicer, which we have for segmentations of the young brain. We also created a young brain atlas for the current Slicer3 EM Segment module. Tests have been successful and a comparative study to itkEMS has shown that further parameter optimization is needed to reach the same quality.

====Cortical thickness measurement====
The UNC algorithm for the measurement of local cortical thickness given a labeling of white matter and gray matter has been developed into a Slicer3 external module. This module lends itself well for regional analysis of cortical thickness, but less so for local analysis due to its non-symmetric and sparse measurements. Ongoing development is focusing on a symmetric, Laplacian based cortical thickness suitable for local analysis.

====Cortical correspondence (regional)====

For regional correspondence, an existing lobar parcellation atlas is deformably registered using a b-spline registration tool. First tests have been very promising and the release of the corresponding Slicer 3 registration module is schedule to be finished within the next month and thus the regional analysis workflow will be available at that time.

====Cortical correspondence (local)====
Local cortical correspondence requires a two-step process of white/gray surface inflation followed by group-wise correspondence computation. White matter surface extraction and inflation is currently achieved with an external tool and developing a Slicer 3 based solution is a goal in the next year. The group-wise correspondence step has been fully solved, and a Slicer 3 module is already available. Evaluation on real data has shown that our method outperforms the currently widely employed Freesurfer framework.

====Statistical analysis/Hypothesis testing====
Regional analysis can be done with standard statistical tools such as MANOVA as there are a limited, relatively small number of regions. Local analysis on the other hand needs local non-parametric testing, multiple-comparison correction, and correlative analysis that is not routinely available. We are currently extending the current Slicer 3 module designed for statistical shape analysis to be used for this purpose incorporating a local applied General Linear Module and MANCOVA based testing framework.

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

Several of the algorithms for this Clinical Roadmap project were already in software tools utilizing ITK. These tools have been refactored to be NA-MIC compatible and repackaged as Slicer3 plugins. Slicer3 has been extended to support this Clinical Roadmap by adding transforms as a parameter type that can be passed to and returned by plugins. Slicer3 registration and resampling modules have been refactored to produce and accept transforms as parameters. Slicer3 has also been extended to support nonlinear transformation types (B-Spline and deformation fields) in its data model.

===Clinical Component (Hazlett)===
So far, the clinical component of this project has involved interfacing with the algorithms and engineering teams to provide the project specifications, feedback, and data (needed for testing). During this past year, development and programming work has proceeded satisfactorily, and we anticipate being able to test our project hypotheses about cortical thickness in autism by the end of our project period. Therefore, the primary accomplishment of this first year has been the development and testing of methods that are necessary for this cortical thickness work pipeline.

===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)==
===Progress===
===Key Investigators===
===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 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.

Structural Segmentation

* Directional Based Segmentation
We have proposed a directional segmentation framework for Direction-weighted Magnetic Resonance imagery by augmenting the Geodesic Active Contour framework with directional information. The classical scalar conformal factor is replaced by a factor that incorporates directionality. We mathematically showed that the optimization problem is well-defined when the factor is a Finsler metric. The calculus of variations or dynamic programming may be used to find the optimal curves. This past year we have applied this methodology in extracting the anchor tract (or centerline) of neural fiber bundles. Further we have applied this in conjunction with the Bayes’ rule into volumetric segmentation for extracting the entire fiber bundles. We have also proposed a novel shape prior in the volumetric segmentation to extract tubular fiber bundles.

* Stochastic Segmentation

We have continued work this year on developing new stochastic methods for implementing curvature-driven flows for medical tasks like segmentation. We can now generalize our results to an arbitrary Riemannian surface which includes the geodesic active contours as a special case. We are also implementing the directional flows based on the anisotropic conformal factor described above using this stochastic methodology. Our stochastic snakes’ models are based on the theory of interacting particle systems. This brings together the theories of curve evolution and hydrodynamic limits, and as such impacts our growing use of joint methods from probability and partial differential in image processing and computer vision. We now have working code written in C++ for the two dimensional case and have worked out the stochastic model of the general geodesic active contour model.

* Statistical PDE Methods for Segmentation

Our objective is to add various statistical measures into our PDE flows for medical imaging. This will allow the incorporation of global image information into the locally defined PDE framework. This year, we developed flows which can separate the distributions inside and outside the evolving contour, and we have also been including shape information in the flows. We have completed a statistically based flow for segmentation using fast marching, and the code has been integrated into Slicer.

* Atlas Renormalization for Improved Brain MR Image Segmentation

Atlas-based approaches can automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. However, the accuracy often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this project, we work to 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 shows that the new procedure improves 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.

*Multiscale Shape Segmentation Techniques

The goal of this project is to represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. Our technique defines a multiscale parametric model of surfaces belonging to the same population using a compact set of spherical wavelets targeted to that population. We derived a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior for segmentation. Additionally, the optimization method can be applied in a coarse-to-fine manner. We applied our algorithm to the caudate nucleus, a brain structure of interest in the study of schizophrenia. Our validation shows that our algorithm is computationally efficient and outperforms the Active Shape Model (ASM) algorithm, by capturing finer shape details.

Registration

* Optimal Mass Transport Registration
The aim of this project is to provide a computationally 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 gradient flow PDE approach using multi-resolution and multi-grid techniques to speed up the convergence. We also leverage the computational power of general purpose graphics processing units available on standard desktop computing machines to exploit the inherent parallelism in our algorithm. We have implemented 2D and 3D multi-resolution registration using Optimal Mass Transport and are currently working on the registration of 3D datasets.

* Diffusion Tensor Image Processing Tools

We aim to provide methods for computing geodesics and distances between diffusion tensors. One goal is to provide hypothesis testing for differences between groups. This will involve interpolation techniques for diffusion tensors as weighted averages in the metric framework. We will also provide filtering and eddy current correction. This year, we developed a Slicer module for DT-MRI Rician noise removal, developed prototypes of DTI geometry and statistical packages, and began work on a general method for hypothesis testing between diffusion tensor groups.

* Point Set Rigid Registration

We propose a particle filtering scheme for the registration of 2D and 3D point set undergoing a rigid body transformation where we incorporate stochastic dynamics to model the uncertainty of the registration process. Typically, registration algorithms compute the transformations parameters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem, in which the corresponding distribution can naturally be estimated using a particle filter. In this work, we treat motion as a local variation in the pose parameters obtained 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 uncertainty. In contrast with other techniques, our approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temporal 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.

* Cortical Correspondence using Particle System

In this project, we want to compute cortical correspondence on populations, using various features such as cortical structure, DTI connectivity, vascular structure, and functional data (fMRI). This presents a challenge because of the highly convoluted surface of the cortex, as well as because of the different properties of the data features we want to incorporate together. We would like to use a particle based entropy minimizing system for the correspondence computation, in a population-based manner. This is advantageous because it does not require a spherical parameterization of the surface, and does not require the surface to be of spherical topology. It would also eventually enable correspondence computation on the subcortical structures and on the cortical surface using the same framework. To circumvent the disadvantage that particles are assumed to lie on local tangent planes, we plan to first ‘inflate’ the cortex surface. Currently, we are at testing stage using structural data, namely, point locations and sulcal depth (as computed by FreeSurfer).

* Multimodal Atlas

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called iCluster for Image Clustering, is based on the following idea: given the templates, the co-registration problem becomes simple, reducing to a number of pairwise registration instances. On the other hand, given a collection of images that have been co-registered, an off-the shelf clustering or averaging algorithm can be used to compute the templates. The algorithm assumed a fixed and known number of template images. We formulate the problem as a maximum likelihood solution and employ a Generalized Maximum Likelihood algorithm to solve it. In the E-step, we compute membership probabilities. In the M-step, we update the template images as weighted averages of the images, where weights are the memberships and the template priors are updated, and then perform a collection of independent pairwise registration instances. The algorithm is currently implemented in the Insight ToolKit (ITK) and we next plan to integrate it into Slicer.

* Groupwise Registration

We aim at providing efficient groupwise registration algorithms for population analysis of anatomical structures. Here we extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment. Our results indicate that increasing the complexity of the deformation model improves registration accuracy significantly, especially at cortical regions.

Shape Analysis

* Shape Analysis Framework Using SPHARM-PDM

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described by sampled spherical harmonics SPHARM-PDM. The input of the proposed shape analysis is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. Group tests can be visualized by P-values and by mean difference magnitude and vector maps, as well as maps of the group covariance information. The implementation has reached a stable framework and has been disseminated to several collaborating labs within NAMIC (BWH, Georgia Tech, Utah). The current development focuses on integrating the current command line tools into the Slicer (v3) via the Slicer execution model. The whole shape analysis pipeline is encapsulated and accessible to the trained clinical collaborator. The current toolset distribution (via NeuroLib) now also contains open data for other researchers to evaluate their shape analysis enhancements.

* Multiscale Shape Analysis

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. As an application, we analyze two brain structures, the caudate nucleus and the hippocampus. We show that the results nicely complement the results obtained with shape analysis using a sampled point representation (SPHARM-PDM). We used the UNC pipeline to pre-process the images, and for each triangulated SPHARM-PDM surface, a spherical wavelet description is computed. We then use the UNC statistical toolbox to analyze differences between two groups of surfaces described by the features of choice that is the 3D spherical wavelet coefficients. This year, we conducted statistical shape analysis of the two brain structures and compared the results obtained to shape analysis using a SPHARM-PDM representation.

* Population Analysis of Anatomical Variability

In contrast to shape-based segmentation that utilizes a statistical model of the shape variability in one population (typically based on Principal Component Analysis), we are interested in identifying and characterizing differences between two sets of shape examples. We use the discriminative framework to characterize the differences in shape by training a classifier function and studying its sensitivity to small perturbations in the input data. An additional benefit is that the resulting classifier function can be used to label new examples into one of the two populations, e.g., for early detection in population screening or prediction in longitudinal studies. We have implemented stand alone code for training a classifier, jackknifing and permutation testing, and are currently porting the software into ITK. We have also started exploring alternative, surface-based descriptors which are promising in improving our ability to detect and characterize subtle differences in the shape of anatomical structures due to diseases such as schizophrenia.

* Shape Analysis with Overcomplete Wavelets

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development and show significantly consistent results as well as improved sensitivity compared with the previously used bi-orthogonal spherical wavelet. In particular, we are able to detect developmental asymmetry in the left and right hemispheres.

*Shape based Segmentation and Registration

When there is little or no contrast along boundaries of different regions, standard image segmentation algorithms perform poorly and segmentation is done manually using prior knowledge of shape and relative location of underlying structures. We have proposed 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. Our method filters out the noise as compared to thresholding using initial likelihoods, and it captures multiple structures as in the brain (where both major brain compartments and subcortical structures are obtained) because it naturally evolves families of curves. The algorithm is currently implemented in 3D Slicer Version 2.6 and a beta version is available in 3D Slicer Version 3.

*Spherical Wavelets

In this project, we apply a spherical wavelet transformation to extract shape features of cortical surfaces reconstructed from magnetic resonance images (MRI) of a set of subjects. The spherical wavelet transformation can characterize the underlying functions in a local fashion in both space and frequency, in contrast to spherical harmonics that have a global basis set. We perform principal component analysis (PCA) on these wavelet shape features to study patterns of shape variation within normal population from coarse to fine resolution. In addition, we study the development of cortical folding in newborns using the Gompertz model in the wavelet domain, allowing us to characterize the order of development of large-scale and finer folding patterns independently. We develop an efficient method to estimate the regularized Gompertz model based on the Broyden–Fletcher–Goldfarb–Shannon (BFGS) approximation. Promising results are presented using both PCA and the folding development model in the wavelet domain. The cortical folding development model provides quantitative anatomical information regarding macroscopic cortical folding development and may be of potential use as a biomarker for early diagnosis of neurological deficits in newborns.

===Key Investigators===
* 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
* 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===
===Key Investigators===
===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===
===Key Investigators===
* Kitware - Will Schroeder (Core 2 PI), Sebastien Barre, Luis Ibanez, Bill Hoffman
* GE - Jim Miller, Xiaodong Tao
* Isomics - Steve Pieper

===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].
==Other Projects==
Any Project(s) not covered by the 8 sections above

==Highlights(Schroeder)==
===EM Segmenter or TBD===
===DTI progress or TBD===
===Outreach (Gollub)===

NAMIC outreach is a joint effort of Cores 4, 5 and 6. The various mechanisms by which we ensure that the tools developed by NAMIC are rapidly and successfully deployed to the widest possible extent within the scientific community are closely integrated. This begins with the immediate posting of all software tools, interim updates and associated documentation via the NAMIC and Slicer wiki pages (links). The concerted effort to provide a harmonious visualization and analysis platform (Slicer 3) that enables the integration of the software algorithms of all Core 1 laboratories drives the sequence of development of training materials. With the January 2008 release of Slicer 3 in beta format, we prepared the first of the Slicer 3 based Powerpoint tutorials that guide new users through the process of loading, interacting with and saving data in Slicer 3. Given the intense and successful effort at engineering this platform to facilitate the process of integrating new command-line modules of image analysis software into the platform, our second tutorial targeted software developers . The "Hello World" tutorial guides a programmer, step-by-step through the process of integrating a command line tool into Slicer 3. Both these tutorials are available via the web (link). These tutorials have been thoroughly tested by using them in large Workshops (see next) to ensure that they are robust across platform (Linux, Mac, PC) and can be used successfully by users across a wide range of training backgrounds.
 
In June of 2007 as a satellite event to the international Organization for Human Brain Mapping annual meeting in Chicago, IL we ran an 8 hour workshop on analysis of Diffusion Imaging Data (link); it was our final Slicer workshop based on the Slicer 2.7 release. The Workshop rapidly filled after posting, the 50 participants represented 9 countries from around the world, 14 states within the US and 40 different laboratories including 2 NIH institutes. The single "no-show" was due to a European flight cancellation. The attendees, with backgrounds in basic or clinical neurosciences, physics, image processing or computer science, ranging from full professors to new graduate students were very comfortable learning together. The feedback from the workshop attendees was uniformly positive with 100% reporting that they would recommend the workshop to others and 50% planning to apply the tools and information they learned to their own work.
 
In January 2008 we debuted the "Hello World" tutorial at the NAMIC AHM in Salt Lake City to an audience of our project members and collaborators. This very constructive presentation was used to make significant improvements in the presentation and delivery of this material. In February 2008 we debuted the users tutorial at a workshop hosted by the Surgical Planning Laboratory at BWH. Again, this presentation was used to make significant improvements in the presentation and delivery of the material. In April of 2008 we ran an all day workshop, hosted by UNC (get details right) for users and developers that incorporated both tutorials. This was attended by approximately 20 individuals coming from a wide range of backgrounds. Time was taken to ensure that all participants gained significant understanding of the new software, sufficient to ensure their successful use of it following the workshop.
 
This year saw the publication of a peer-reviewed manuscript that describes the NAMIC approach to outreach including our multi-disciplinary approach, our integration of theory into practice as driven by a clinical goal, and the translation of concepts into skills through interactive instructor led training sessions (Pujol S, Kikinis R, Gollub R: Lowering the barriers inherent in translating advances in neuroimage analysis to clinical research applications, Academic Radiology 15: 114-118, 2008, add link to Publication DB).
* Text here about Project Events 5 & 6 from Tina if not already included elsewhere.
* Text here about the MICCAI Open Source Workshop if not already included elsewhere (Steve?)
* Slicer IGT event December 2007 (tina?)
* Wiki to web
* Impact as measured by number of downloads of tutorial materials (help someone)
* Should the DTI tractography validation project be written up somewhere, if so where? I will do it if it isn't already assigned.

==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===
Within the center, NA-MIC has formed a community around its software
engineering tools, imaging platforms, algorithms, and clinical
workflows. The NA-MIC calendar includes the All Hands Meeting and
Winter Project Week, the Spring Algorithm Meeting, the Summer Project
Week, Slicer3 Mini-Retreats, Core Site Visits, Training Workshops, and weekly telephone
conferences.

The NA-MIC software engineering tools (CMake, Dart, CTest, CPack) have
enabled the development and distribution of a cross-platform, nightly
tested, end-user application, Slicer3, that is a complex union of
novel application code, visualization tools (VTK), imaging libraries
(ITK, TEEM), user interface libraries (Tk, KWWidgets), and scripting
languages (TCL, Python). The NA-MIC software engineering tools have been
essential in the development and distribution of the Slicer3 imaging
platform to the NA-MIC community.

NA-MIC's end-user application, Slicer3, supports the research within
NA-MIC by providing a base application for visualization and data
management. Slicer3 also supports the research within NA-MIC by
providing plugin mechanisms which allow researchers to quickly and
easily integrate and distribute their technology with Slicer3. Slicer3
is available to all center participants and the external community
through its source code repository, official binary releases, and
unofficial nightly binary snapshots.

NA-MIC drives the development of platforms and algorithms through the
needs and research of its DBPs. Each DBP has selected specific
workflows and roadmaps as focal points for development with a goal of
providing the community with complete end-to-end solutions using
NA-MIC tools. The community will be able to reproduce these workflows
and roadmaps in their own research programs.

NA-MIC algorithms are designed and used to address specific needs of
the DBPs. Multiple solution paths are explored and compared within
NA-MIC, resulting in recommendations to the field. The NA-MIC
algorithm groups collaborate and orchestrate the solutions to the
DBP workflows and roadmaps.

===Impact within NIH Funded Research===
Within NIH funded research, NA-MIC is the NCBC collaborating center for three R01's: "Automated FE Mesh Development", "Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI", and "An Integrated System for Image-Guided Radiofrequency Ablation of Liver Tumors". Several other proposals have been submitted and are under
evaluation for the "Collaborations with NCBC PAR". NA-MIC also
collaborates on the Slicer3 platform with the NIH funded Neuroimage
Analysis Center and the National Center for Image-Guided Therapy. The
NIH funded "BRAINS Morphology and Image Analysis" project is also
leveraging NA-MIC and Slicer3 technology. NA-MIC collaborates with the
NIH funded Neuroimaging Informatics Tools and Resources Clearinghouse
on distribution of Slicer3 plugin modules.

===National and International Impact===
NA-MIC events and tools garner national and international interest.
Over 100 researchers participated in the NA-MIC All Hands Meeting and
Winter Project Week in January 2008. Many of these participants were
from outside of NA-MIC, attending the meetings to gain access to the
NA-MIC tools and researchers. These external researchers are
contributing ideas and technology back into NA-MIC. In fact, a
breakout session at the Winter Project Week on "Geometry and Topology
Processing of Meshes" was organized by four researchers from outside
of NA-MIC.

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

NA-MIC science is recognized by the medical imaging community. Over
100 NA-MIC related publications are listed on PubMed. Many of these
publications are in the most prestigious journals and conferences in the
field. Portions of the DBP workflows and roadmaps are already being
utilized by researchers in the broader community and in the
development of commercial products.

NA-MIC sponsored several events to promote NA-MIC tools and
methodologies. NA-MIC co-sponsored the "Third Annual Open Source
Workshop" at the Medical Image Computing and Computer-Assisted
Intervention (MICCAI) 2007 conference. The proceedings of the
workshop are published on the electronic Insight Journal, another
NIH-funded activity. NA-MIC sponsored three training workshops on
NA-MIC tools for the Biocomputing community in this fiscal year and
plans to hold sessions at upcoming MICCAI and RSNA conferences.

==NA-MIC Timeline (Whitaker)==

==Appendix A Publications (Kapur)==
These 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.

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

2008 Annual Scientific Report

2008-05-14T20:52:12Z

Hazlett: /* Clinical Component (Hazlett) */

Back to [[2008_Progress_Report]]

=Guidelines for preparation=

*[[2008_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 2007 report, which is provided here for reference: [[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 in its fourth year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The first driving biological projects (DBPs) three years for Center were inspired by schizophrenia research. In the fourth year new DBPs have been added. Three are centered around diseases of the brain: (a) brain lesion analysis in neuropschiatric systemic lupus erythematosus; (b) a study of cortical thickness for autism; and (c) stochastic tractography for VCFS. In an very new direction, we have added DBP on the prostate: brachytherapy needle positioning robot integration.

We briefly summarize the work of NAMIC during the four years of its existence. In the year one of the Center, alliances were forged amongst the cores and constituent groups in order to integrate the efforts of the cores and to define the kinds of tools needed for specific imaging applications. The second year emphasized the identification of the key research thrusts that cut across cores and were driven by the needs and requirements of the DBPs. This led to the formulation of the Center's four main 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 the collaborative efforts in order to give solutions to the various brain-oriented DBPs.

Year four has seen progress with the work of our new DBPs. As alluded to above these include work on neuropsychiatric disorders such as Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), as well as the prostate interventional work (Johns Hopkins and Queens Universities). We already have a number of publications as is indicated on our publications page, and software development is continuing as well.

In the next section (Section 3), we summarize this year’s progress on the four roadmap projects listed above: Section 3.1 stochastic tractography for Velocardiofacial Syndrome, Section 3.2 brachytherapy needle positioning for the prostate, Section 3.3 brain lesion analysis in neuropschiatric systemic lupus erythematosus, and Section 3.4 cortical thickness for autism. Next in Section 4, we describe recent 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). In Section 4.5, we outline some of the other key projects, in Section 4.6 some key highlights including the integration of the EM Segmentor into Slicer, and in Section 4.7 the impact of biocomputing at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 4.8, provides a timeline of Center activities.

=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 would be usefull in evaluating anatomical connectivity between segmented cortical regions of the brain. The ultimate goal of our program is to understand anatomical connectivity similarities and differences between genetically related schizophrenia and velocardio-fatial syndrome. Thus we plan to use the "stochastic tractography" tool for the analysis of abnormalities in integrity, or connectivity, provided by arcuate fasciculus, fiber bundle involved in language processing, in schizophrenia and VCFS.

===Algorithm Component (Golland)===
Over the last year, algorythm has been tested first on the already available to NAMIC dataset of schizophrenia subjects acquired on 1.5T. This step allowed us to optimize algorythm to our dataset, as well as to develop the pipeline for data analysis that would be then easilly transferable to other datasets, and structures. For this, as well as other applications, we use gray matter labels derived from either automatic or manual segmentation of structural MRI. Thus the first step was to obtain registration between diffusion and anatomical data. Right now, demon's registration alogythm that is part of slicer is being used, but we are also testing b-spline registration, as well as fluid? registration for this purpose. Next step, also accomplished this last year, was to apply the alogythm to new, higher resolution NAMIC dataset, and study smaller white matter connections including cingulum bundle, arcuate fasciculus, uncinate fasciculus and internal capsule. This step was accomplished and data presented at the Santa Fee meeting in October 2007. Algorythm was also additionally tested on the phantom, where differences in coordinate systems were debugged. Upon the completion of testing phase, we started analysis of arcuate fasciculus, language related fiber bundle, in new 3T, high resolution dataset. Additional step turned out to be required in preprocessing, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high, thus white matter sesgmentation was performed using free surfer, and co-registered with DTI. Our current work focuses on better registration alogythms, as well as the way to parametrize tracts, in order to obtain FA measurements along the tracts.

===Engineering Component (Davis)===
Stochastic Tractography slicer module has been finished, and presented at the AHM in SLC. Its now part of the slicer2.8 and slicer3. Module documentation have been also created.
Current engeneering effords are concentrated on maintaining the module, optimizing it for working with other data formats, and adding new functionality, such as better registration, distortion correction and ways of extracting and measuring FA along the tracts.

===Clinical Component (Kubicki)===
Over the last year, algorythm has been tested first on the already available to NAMIC dataset of schizophrenia subjects acquired on 1.5T. Anterior Limb of the internal capsule, large structure connecting thalamus with frontal lobe, have been extracvted, and analyzed in group of 20 schizphrenics, and 20 control subjects, and results showing group differences in Fractional Anisotropy presented at the ACNP symposium in December 2007. Next, stochastic tractography was tested, and optimized for new, high resolution DTI dataset acquired on 3T GE magnet. Upon the completion of testing phase, we started analysis of arcuate fasciculus, language related fiber bundle, in 20 controls and 20 chronic schizphrenics. Whole brain as well as white matter segmentations, using freesurfer, as well as automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Supperior Temporal Gyri), as well as another ROI that would guide the tract ("waypoint" ROI) have been generated for all subjects. Finally, paths of interest were generated, and averaged FA extracted for each tract. Preliminary data based on 7 patients and 12 controls were presented at the AHM in January 2008, study is currently under way.

===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: Brachytherapy Needle Positioning Robot Integration (Fichtinger)==
===Overview (Fichtinger)===
===Algorithm Component (Tannenbaum)===
We have worked on both the segmentation and the registration of the prostate from MRI and ultrasound data. We explain each of the steps now.

====Prostate Segmentation====

We first must extract the prostate. We have considered three possible methods: a combination of a combination of Cellular Automata(CA also known as Grow Cut) with Geometric Active Contour(GAC) methods; employing an ellipsoid to match the prostate in 3D image; shape based approach using spherical wavelets. More details are given below and images and further details may be found at [[Projects:ProstateSegmentation|GaTech Algorithm Prostate Segmentation]].

1. A cellular automata algorithm is used to give an initial segmentation. It begins with a rough manual initialization and then iteratively classifies all pixels into object and bacground until convergence. It effectively overcomes the problems of weak boundaries and inhomogeneity within the object or background. This in turn is fed into Geometric Active Contour for finer tuning. We are initially using the edge-based minimal surface pproach (the generalization of the standard Geodesic Active Contour model) which seems to give very reasonable results. Both steps of the algorithm algorithm are implemented in 3D. A ITK-Cellular Automata filter, dealing with N-D data, has already been completed and submitted to the NA-MIC SandBox.

2. Spherical wavelets have proven to be a very natural way of representing 3D shapes which are compact and simply connected (topological spheres). We developed a segmentation framework using this 3D wavelet representation and multiscale prior. The parameters of our model are the learned shape parameters based on the spherical wavelet coefficients}, as well as pose parameters that accommodate for shape variability due to a similarity transformation (rotation, scale, translation) which is not explicitly modeled with the shape parameters. The transformed surface based on the pose parameters. We used a region-based energy to drive the evolution of the parametric deformable surface for segmentation. Our segmentation algorithm deforms an initial surface according to the gradient flow that minimizes the energy functional in terms of the pose and shape parameters. Additionally, the optimization method can be applied in a coarse to fine manner. Spherical wavelets and conformal mappings are
already part of the NA-MIC SandBox.

3. The third method is very closely related to the second. It is based on the observation that the prostate may be roughly modelled as an ellipsoid. One can then employing this ellipsoid model coupled with a local/global segmentation energy approach which we have developed this year, as the basis of a segmentation procedure. Because of the local/global nature of the functional and the implicit introduction of scale this methodology may be very useful for MRI prostate data.

====Prostate Registration====

The registration and segmentation elements of our algorithm are difficult to separate. Thus for the 3D shape-driven segmentation part, the shapes must first be aligned through a conformal and area-correction alignment process. The prostate presents a number of difficulties for traditional approaches since there are no easily discernable landmarks. On the other hand, we observed that the surface of the prostate is almost half convex and half concave. The concave region may be captured and used to register the shapes, thus we register the whole shape by registering a certain region on it. Such concave region is characterized by its negative mean curvature. We treat the mean curvature as a scalar field defined on the surface, and we have extended the Chan-Vese method (in which one wants to separate the means with respect to the regions defined by the interior and exterior of the evolving active contour) to the case at hand on the prostate surface. The method is implemented in C++ and it successfully extracts the concave surface region. This method could also be used to exact regions on surface according to any feature charactered by a scalar field defined on the surface.

In order incorporate the extracted region as landmarks into the registration process, instead of matching two binary images directly, we transform the binary images into a form to highlight the boundary region. This is done by applying a Gauss function on the (narrow band) of the signed distance function of the binary image. The transformed image enjoys the advantages of both the parametric and implicit representations of shapes. Namely it has compact description, as the parametric representation does, and as in the implicit representation it avoids the correspondence problem. Moreover we incorporate the extracted concave regions into such images for registration which leads to a better result.

Finally, in the past year we have developed a particle filtering approach for the general problem of registering two point sets that differ by a rigid body transformation which may be very useful for this project. Typically, registration algorithms compute the transformation parameters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem, in which the corresponding distribution can naturally be estimated using a particle filter. We treat motion as a local variation in pose parameters obtained from running several iterations of the standard Iterative Closest Point (ICP) algorithm. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence often found in local optimizer functions used to tackle the registration task. In contrast with other techniques, this approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temporal 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.

===Engineering Component (Hayes)===
===Clinical Component (Fichtinger)===
===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)===
===Algorithm Component (Whitaker)===
===Engineering Component (Pieper)===
===Clinical Component (Bockholt)===
===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.
Tissue segmentation: We have successfully adapted the UNC segmentation tool called itkEMS to Slicer, which we have for segmentations of the young brain. We also created a young brain atlas for the current Slicer3 EM Segment module. Tests have been successful and a comparative study to itkEMS has shown that further parameter optimization is needed to reach the same quality.

====Cortical thickness measurement====
The UNC algorithm for the measurement of local cortical thickness given a labeling of white matter and gray matter has been developed into a Slicer3 external module. This module lends itself well for regional analysis of cortical thickness, but less so for local analysis due to its non-symmetric and sparse measurements. Ongoing development is focusing on a symmetric, Laplacian based cortical thickness suitable for local analysis.

====Cortical correspondence (regional)====

For regional correspondence, an existing lobar parcellation atlas is deformably registered using a b-spline registration tool. First tests have been very promising and the release of the corresponding Slicer 3 registration module is schedule to be finished within the next month and thus the regional analysis workflow will be available at that time.

====Cortical correspondence (local)====
Local cortical correspondence requires a two-step process of white/gray surface inflation followed by group-wise correspondence computation. White matter surface extraction and inflation is currently achieved with an external tool and developing a Slicer 3 based solution is a goal in the next year. The group-wise correspondence step has been fully solved, and a Slicer 3 module is already available. Evaluation on real data has shown that our method outperforms the currently widely employed Freesurfer framework.

====Statistical analysis/Hypothesis testing====
Regional analysis can be done with standard statistical tools such as MANOVA as there are a limited, relatively small number of regions. Local analysis on the other hand needs local non-parametric testing, multiple-comparison correction, and correlative analysis that is not routinely available. We are currently extending the current Slicer 3 module designed for statistical shape analysis to be used for this purpose incorporating a local applied General Linear Module and MANCOVA based testing framework.

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

Several of the algorithms for this Clinical Roadmap project were already in software tools utilizing ITK. These tools have been refactored to be NA-MIC compatible and repackaged as Slicer3 plugins. Slicer3 has been extended to support this Clinical Roadmap by adding transforms as a parameter type that can be passed to and returned by plugins. Slicer3 registration and resampling modules have been refactored to produce and accept transforms as parameters. Slicer3 has also been extended to support nonlinear transformation types (B-Spline and deformation fields) in its data model.

===Clinical Component (Hazlett)===
So far, the clinical component of this project has involved interfacing with the algorithms and engineering teams to provide the project specifications, feedback, and data (needed for testing). During this past year, development and programming work has proceeded satisfactorily, and we anticipate being able to test some of our project hypotheses about cortical thickness in autism by the end of our project period. Therefore, the primary accomplishment of this first year has been the development and testing of methods that are necessary for this cortical thickness work pipeline.

===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)==
===Progress===
===Key Investigators===
===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 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.

Structural Segmentation

* Directional Based Segmentation
We have proposed a directional segmentation framework for Direction-weighted Magnetic Resonance imagery by augmenting the Geodesic Active Contour framework with directional information. The classical scalar conformal factor is replaced by a factor that incorporates directionality. We mathematically showed that the optimization problem is well-defined when the factor is a Finsler metric. The calculus of variations or dynamic programming may be used to find the optimal curves. This past year we have applied this methodology in extracting the anchor tract (or centerline) of neural fiber bundles. Further we have applied this in conjunction with the Bayes’ rule into volumetric segmentation for extracting the entire fiber bundles. We have also proposed a novel shape prior in the volumetric segmentation to extract tubular fiber bundles.

* Stochastic Segmentation

We have continued work this year on developing new stochastic methods for implementing curvature-driven flows for medical tasks like segmentation. We can now generalize our results to an arbitrary Riemannian surface which includes the geodesic active contours as a special case. We are also implementing the directional flows based on the anisotropic conformal factor described above using this stochastic methodology. Our stochastic snakes’ models are based on the theory of interacting particle systems. This brings together the theories of curve evolution and hydrodynamic limits, and as such impacts our growing use of joint methods from probability and partial differential in image processing and computer vision. We now have working code written in C++ for the two dimensional case and have worked out the stochastic model of the general geodesic active contour model.

* Statistical PDE Methods for Segmentation

Our objective is to add various statistical measures into our PDE flows for medical imaging. This will allow the incorporation of global image information into the locally defined PDE framework. This year, we developed flows which can separate the distributions inside and outside the evolving contour, and we have also been including shape information in the flows. We have completed a statistically based flow for segmentation using fast marching, and the code has been integrated into Slicer.

* Atlas Renormalization for Improved Brain MR Image Segmentation

Atlas-based approaches can automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. However, the accuracy often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this project, we work to 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 shows that the new procedure improves 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.

*Multiscale Shape Segmentation Techniques

The goal of this project is to represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. Our technique defines a multiscale parametric model of surfaces belonging to the same population using a compact set of spherical wavelets targeted to that population. We derived a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior for segmentation. Additionally, the optimization method can be applied in a coarse-to-fine manner. We applied our algorithm to the caudate nucleus, a brain structure of interest in the study of schizophrenia. Our validation shows that our algorithm is computationally efficient and outperforms the Active Shape Model (ASM) algorithm, by capturing finer shape details.

Registration

* Optimal Mass Transport Registration
The aim of this project is to provide a computationally 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 gradient flow PDE approach using multi-resolution and multi-grid techniques to speed up the convergence. We also leverage the computational power of general purpose graphics processing units available on standard desktop computing machines to exploit the inherent parallelism in our algorithm. We have implemented 2D and 3D multi-resolution registration using Optimal Mass Transport and are currently working on the registration of 3D datasets.

* Diffusion Tensor Image Processing Tools

We aim to provide methods for computing geodesics and distances between diffusion tensors. One goal is to provide hypothesis testing for differences between groups. This will involve interpolation techniques for diffusion tensors as weighted averages in the metric framework. We will also provide filtering and eddy current correction. This year, we developed a Slicer module for DT-MRI Rician noise removal, developed prototypes of DTI geometry and statistical packages, and began work on a general method for hypothesis testing between diffusion tensor groups.

* Point Set Rigid Registration

We propose a particle filtering scheme for the registration of 2D and 3D point set undergoing a rigid body transformation where we incorporate stochastic dynamics to model the uncertainty of the registration process. Typically, registration algorithms compute the transformations parameters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem, in which the corresponding distribution can naturally be estimated using a particle filter. In this work, we treat motion as a local variation in the pose parameters obtained 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 uncertainty. In contrast with other techniques, our approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temporal 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.

* Cortical Correspondence using Particle System

In this project, we want to compute cortical correspondence on populations, using various features such as cortical structure, DTI connectivity, vascular structure, and functional data (fMRI). This presents a challenge because of the highly convoluted surface of the cortex, as well as because of the different properties of the data features we want to incorporate together. We would like to use a particle based entropy minimizing system for the correspondence computation, in a population-based manner. This is advantageous because it does not require a spherical parameterization of the surface, and does not require the surface to be of spherical topology. It would also eventually enable correspondence computation on the subcortical structures and on the cortical surface using the same framework. To circumvent the disadvantage that particles are assumed to lie on local tangent planes, we plan to first ‘inflate’ the cortex surface. Currently, we are at testing stage using structural data, namely, point locations and sulcal depth (as computed by FreeSurfer).

* Multimodal Atlas

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called iCluster for Image Clustering, is based on the following idea: given the templates, the co-registration problem becomes simple, reducing to a number of pairwise registration instances. On the other hand, given a collection of images that have been co-registered, an off-the shelf clustering or averaging algorithm can be used to compute the templates. The algorithm assumed a fixed and known number of template images. We formulate the problem as a maximum likelihood solution and employ a Generalized Maximum Likelihood algorithm to solve it. In the E-step, we compute membership probabilities. In the M-step, we update the template images as weighted averages of the images, where weights are the memberships and the template priors are updated, and then perform a collection of independent pairwise registration instances. The algorithm is currently implemented in the Insight ToolKit (ITK) and we next plan to integrate it into Slicer.

* Groupwise Registration

We aim at providing efficient groupwise registration algorithms for population analysis of anatomical structures. Here we extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment. Our results indicate that increasing the complexity of the deformation model improves registration accuracy significantly, especially at cortical regions.

Shape Analysis

* Shape Analysis Framework Using SPHARM-PDM

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described by sampled spherical harmonics SPHARM-PDM. The input of the proposed shape analysis is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. Group tests can be visualized by P-values and by mean difference magnitude and vector maps, as well as maps of the group covariance information. The implementation has reached a stable framework and has been disseminated to several collaborating labs within NAMIC (BWH, Georgia Tech, Utah). The current development focuses on integrating the current command line tools into the Slicer (v3) via the Slicer execution model. The whole shape analysis pipeline is encapsulated and accessible to the trained clinical collaborator. The current toolset distribution (via NeuroLib) now also contains open data for other researchers to evaluate their shape analysis enhancements.

* Multiscale Shape Analysis

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. As an application, we analyze two brain structures, the caudate nucleus and the hippocampus. We show that the results nicely complement the results obtained with shape analysis using a sampled point representation (SPHARM-PDM). We used the UNC pipeline to pre-process the images, and for each triangulated SPHARM-PDM surface, a spherical wavelet description is computed. We then use the UNC statistical toolbox to analyze differences between two groups of surfaces described by the features of choice that is the 3D spherical wavelet coefficients. This year, we conducted statistical shape analysis of the two brain structures and compared the results obtained to shape analysis using a SPHARM-PDM representation.

* Population Analysis of Anatomical Variability

In contrast to shape-based segmentation that utilizes a statistical model of the shape variability in one population (typically based on Principal Component Analysis), we are interested in identifying and characterizing differences between two sets of shape examples. We use the discriminative framework to characterize the differences in shape by training a classifier function and studying its sensitivity to small perturbations in the input data. An additional benefit is that the resulting classifier function can be used to label new examples into one of the two populations, e.g., for early detection in population screening or prediction in longitudinal studies. We have implemented stand alone code for training a classifier, jackknifing and permutation testing, and are currently porting the software into ITK. We have also started exploring alternative, surface-based descriptors which are promising in improving our ability to detect and characterize subtle differences in the shape of anatomical structures due to diseases such as schizophrenia.

* Shape Analysis with Overcomplete Wavelets

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development and show significantly consistent results as well as improved sensitivity compared with the previously used bi-orthogonal spherical wavelet. In particular, we are able to detect developmental asymmetry in the left and right hemispheres.

*Shape based Segmentation and Registration

When there is little or no contrast along boundaries of different regions, standard image segmentation algorithms perform poorly and segmentation is done manually using prior knowledge of shape and relative location of underlying structures. We have proposed 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. Our method filters out the noise as compared to thresholding using initial likelihoods, and it captures multiple structures as in the brain (where both major brain compartments and subcortical structures are obtained) because it naturally evolves families of curves. The algorithm is currently implemented in 3D Slicer Version 2.6 and a beta version is available in 3D Slicer Version 3.

*Spherical Wavelets

In this project, we apply a spherical wavelet transformation to extract shape features of cortical surfaces reconstructed from magnetic resonance images (MRI) of a set of subjects. The spherical wavelet transformation can characterize the underlying functions in a local fashion in both space and frequency, in contrast to spherical harmonics that have a global basis set. We perform principal component analysis (PCA) on these wavelet shape features to study patterns of shape variation within normal population from coarse to fine resolution. In addition, we study the development of cortical folding in newborns using the Gompertz model in the wavelet domain, allowing us to characterize the order of development of large-scale and finer folding patterns independently. We develop an efficient method to estimate the regularized Gompertz model based on the Broyden–Fletcher–Goldfarb–Shannon (BFGS) approximation. Promising results are presented using both PCA and the folding development model in the wavelet domain. The cortical folding development model provides quantitative anatomical information regarding macroscopic cortical folding development and may be of potential use as a biomarker for early diagnosis of neurological deficits in newborns.

===Key Investigators===
* 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
* 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===
===Key Investigators===
===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===
===Key Investigators===
* Kitware - Will Schroeder (Core 2 PI), Sebastien Barre, Luis Ibanez, Bill Hoffman
* GE - Jim Miller, Xiaodong Tao
* Isomics - Steve Pieper

===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].
==Other Projects==
Any Project(s) not covered by the 8 sections above

==Highlights(Schroeder)==
===EM Segmenter or TBD===
===DTI progress or TBD===
===Outreach (Gollub)===

NAMIC outreach is a joint effort of Cores 4, 5 and 6. The various mechanisms by which we ensure that the tools developed by NAMIC are rapidly and successfully deployed to the widest possible extent within the scientific community are closely integrated. This begins with the immediate posting of all software tools, interim updates and associated documentation via the NAMIC and Slicer wiki pages (links). The concerted effort to provide a harmonious visualization and analysis platform (Slicer 3) that enables the integration of the software algorithms of all Core 1 laboratories drives the sequence of development of training materials. With the January 2008 release of Slicer 3 in beta format, we prepared the first of the Slicer 3 based Powerpoint tutorials that guide new users through the process of loading, interacting with and saving data in Slicer 3. Given the intense and successful effort at engineering this platform to facilitate the process of integrating new command-line modules of image analysis software into the platform, our second tutorial targeted software developers . The "Hello World" tutorial guides a programmer, step-by-step through the process of integrating a command line tool into Slicer 3. Both these tutorials are available via the web (link). These tutorials have been thoroughly tested by using them in large Workshops (see next) to ensure that they are robust across platform (Linux, Mac, PC) and can be used successfully by users across a wide range of training backgrounds.
 
In June of 2007 as a satellite event to the international Organization for Human Brain Mapping annual meeting in Chicago, IL we ran an 8 hour workshop on analysis of Diffusion Imaging Data (link); it was our final Slicer workshop based on the Slicer 2.7 release. The Workshop rapidly filled after posting, the 50 participants represented 9 countries from around the world, 14 states within the US and 40 different laboratories including 2 NIH institutes. The single "no-show" was due to a European flight cancellation. The attendees, with backgrounds in basic or clinical neurosciences, physics, image processing or computer science, ranging from full professors to new graduate students were very comfortable learning together. The feedback from the workshop attendees was uniformly positive with 100% reporting that they would recommend the workshop to others and 50% planning to apply the tools and information they learned to their own work.
 
In January 2008 we debuted the "Hello World" tutorial at the NAMIC AHM in Salt Lake City to an audience of our project members and collaborators. This very constructive presentation was used to make significant improvements in the presentation and delivery of this material. In February 2008 we debuted the users tutorial at a workshop hosted by the Surgical Planning Laboratory at BWH. Again, this presentation was used to make significant improvements in the presentation and delivery of the material. In April of 2008 we ran an all day workshop, hosted by UNC (get details right) for users and developers that incorporated both tutorials. This was attended by approximately 20 individuals coming from a wide range of backgrounds. Time was taken to ensure that all participants gained significant understanding of the new software, sufficient to ensure their successful use of it following the workshop.
 
This year saw the publication of a peer-reviewed manuscript that describes the NAMIC approach to outreach including our multi-disciplinary approach, our integration of theory into practice as driven by a clinical goal, and the translation of concepts into skills through interactive instructor led training sessions (Pujol S, Kikinis R, Gollub R: Lowering the barriers inherent in translating advances in neuroimage analysis to clinical research applications, Academic Radiology 15: 114-118, 2008, add link to Publication DB).
* Text here about Project Events 5 & 6 from Tina if not already included elsewhere.
* Text here about the MICCAI Open Source Workshop if not already included elsewhere (Steve?)
* Slicer IGT event December 2007 (tina?)
* Wiki to web
* Impact as measured by number of downloads of tutorial materials (help someone)
* Should the DTI tractography validation project be written up somewhere, if so where? I will do it if it isn't already assigned.

==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===
Within the center, NA-MIC has formed a community around its software
engineering tools, imaging platforms, algorithms, and clinical
workflows. The NA-MIC calendar includes the All Hands Meeting and
Winter Project Week, the Spring Algorithm Meeting, the Summer Project
Week, Slicer3 Mini-Retreats, Core Site Visits, Training Workshops, and weekly telephone
conferences.

The NA-MIC software engineering tools (CMake, Dart, CTest, CPack) have
enabled the development and distribution of a cross-platform, nightly
tested, end-user application, Slicer3, that is a complex union of
novel application code, visualization tools (VTK), imaging libraries
(ITK, TEEM), user interface libraries (Tk, KWWidgets), and scripting
languages (TCL, Python). The NA-MIC software engineering tools have been
essential in the development and distribution of the Slicer3 imaging
platform to the NA-MIC community.

NA-MIC's end-user application, Slicer3, supports the research within
NA-MIC by providing a base application for visualization and data
management. Slicer3 also supports the research within NA-MIC by
providing plugin mechanisms which allow researchers to quickly and
easily integrate and distribute their technology with Slicer3. Slicer3
is available to all center participants and the external community
through its source code repository, official binary releases, and
unofficial nightly binary snapshots.

NA-MIC drives the development of platforms and algorithms through the
needs and research of its DBPs. Each DBP has selected specific
workflows and roadmaps as focal points for development with a goal of
providing the community with complete end-to-end solutions using
NA-MIC tools. The community will be able to reproduce these workflows
and roadmaps in their own research programs.

NA-MIC algorithms are designed and used to address specific needs of
the DBPs. Multiple solution paths are explored and compared within
NA-MIC, resulting in recommendations to the field. The NA-MIC
algorithm groups collaborate and orchestrate the solutions to the
DBP workflows and roadmaps.

===Impact within NIH Funded Research===
Within NIH funded research, NA-MIC is the NCBC collaborating center for three R01's: "Automated FE Mesh Development", "Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI", and "An Integrated System for Image-Guided Radiofrequency Ablation of Liver Tumors". Several other proposals have been submitted and are under
evaluation for the "Collaborations with NCBC PAR". NA-MIC also
collaborates on the Slicer3 platform with the NIH funded Neuroimage
Analysis Center and the National Center for Image-Guided Therapy. The
NIH funded "BRAINS Morphology and Image Analysis" project is also
leveraging NA-MIC and Slicer3 technology. NA-MIC collaborates with the
NIH funded Neuroimaging Informatics Tools and Resources Clearinghouse
on distribution of Slicer3 plugin modules.

===National and International Impact===
NA-MIC events and tools garner national and international interest.
Over 100 researchers participated in the NA-MIC All Hands Meeting and
Winter Project Week in January 2008. Many of these participants were
from outside of NA-MIC, attending the meetings to gain access to the
NA-MIC tools and researchers. These external researchers are
contributing ideas and technology back into NA-MIC. In fact, a
breakout session at the Winter Project Week on "Geometry and Topology
Processing of Meshes" was organized by four researchers from outside
of NA-MIC.

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

NA-MIC science is recognized by the medical imaging community. Over
100 NA-MIC related publications are listed on PubMed. Many of these
publications are in the most prestigious journals and conferences in the
field. Portions of the DBP workflows and roadmaps are already being
utilized by researchers in the broader community and in the
development of commercial products.

NA-MIC sponsored several events to promote NA-MIC tools and
methodologies. NA-MIC co-sponsored the "Third Annual Open Source
Workshop" at the Medical Image Computing and Computer-Assisted
Intervention (MICCAI) 2007 conference. The proceedings of the
workshop are published on the electronic Insight Journal, another
NIH-funded activity. NA-MIC sponsored three training workshops on
NA-MIC tools for the Biocomputing community in this fiscal year and
plans to hold sessions at upcoming MICCAI and RSNA conferences.

==NA-MIC Timeline (Whitaker)==

==Appendix A Publications (Kapur)==
These 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.

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

2008 Annual Scientific Report

2008-05-14T20:50:03Z

Hazlett: /* Clinical Component (Hazlett) */

Back to [[2008_Progress_Report]]

=Guidelines for preparation=

*[[2008_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 2007 report, which is provided here for reference: [[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 in its fourth year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The first driving biological projects (DBPs) three years for Center were inspired by schizophrenia research. In the fourth year new DBPs have been added. Three are centered around diseases of the brain: (a) brain lesion analysis in neuropschiatric systemic lupus erythematosus; (b) a study of cortical thickness for autism; and (c) stochastic tractography for VCFS. In an very new direction, we have added DBP on the prostate: brachytherapy needle positioning robot integration.

We briefly summarize the work of NAMIC during the four years of its existence. In the year one of the Center, alliances were forged amongst the cores and constituent groups in order to integrate the efforts of the cores and to define the kinds of tools needed for specific imaging applications. The second year emphasized the identification of the key research thrusts that cut across cores and were driven by the needs and requirements of the DBPs. This led to the formulation of the Center's four main 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 the collaborative efforts in order to give solutions to the various brain-oriented DBPs.

Year four has seen progress with the work of our new DBPs. As alluded to above these include work on neuropsychiatric disorders such as Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), as well as the prostate interventional work (Johns Hopkins and Queens Universities). We already have a number of publications as is indicated on our publications page, and software development is continuing as well.

In the next section (Section 3), we summarize this year’s progress on the four roadmap projects listed above: Section 3.1 stochastic tractography for Velocardiofacial Syndrome, Section 3.2 brachytherapy needle positioning for the prostate, Section 3.3 brain lesion analysis in neuropschiatric systemic lupus erythematosus, and Section 3.4 cortical thickness for autism. Next in Section 4, we describe recent 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). In Section 4.5, we outline some of the other key projects, in Section 4.6 some key highlights including the integration of the EM Segmentor into Slicer, and in Section 4.7 the impact of biocomputing at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 4.8, provides a timeline of Center activities.

=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 would be usefull in evaluating anatomical connectivity between segmented cortical regions of the brain. The ultimate goal of our program is to understand anatomical connectivity similarities and differences between genetically related schizophrenia and velocardio-fatial syndrome. Thus we plan to use the "stochastic tractography" tool for the analysis of abnormalities in integrity, or connectivity, provided by arcuate fasciculus, fiber bundle involved in language processing, in schizophrenia and VCFS.

===Algorithm Component (Golland)===
Over the last year, algorythm has been tested first on the already available to NAMIC dataset of schizophrenia subjects acquired on 1.5T. This step allowed us to optimize algorythm to our dataset, as well as to develop the pipeline for data analysis that would be then easilly transferable to other datasets, and structures. For this, as well as other applications, we use gray matter labels derived from either automatic or manual segmentation of structural MRI. Thus the first step was to obtain registration between diffusion and anatomical data. Right now, demon's registration alogythm that is part of slicer is being used, but we are also testing b-spline registration, as well as fluid? registration for this purpose. Next step, also accomplished this last year, was to apply the alogythm to new, higher resolution NAMIC dataset, and study smaller white matter connections including cingulum bundle, arcuate fasciculus, uncinate fasciculus and internal capsule. This step was accomplished and data presented at the Santa Fee meeting in October 2007. Algorythm was also additionally tested on the phantom, where differences in coordinate systems were debugged. Upon the completion of testing phase, we started analysis of arcuate fasciculus, language related fiber bundle, in new 3T, high resolution dataset. Additional step turned out to be required in preprocessing, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high, thus white matter sesgmentation was performed using free surfer, and co-registered with DTI. Our current work focuses on better registration alogythms, as well as the way to parametrize tracts, in order to obtain FA measurements along the tracts.

===Engineering Component (Davis)===
Stochastic Tractography slicer module has been finished, and presented at the AHM in SLC. Its now part of the slicer2.8 and slicer3. Module documentation have been also created.
Current engeneering effords are concentrated on maintaining the module, optimizing it for working with other data formats, and adding new functionality, such as better registration, distortion correction and ways of extracting and measuring FA along the tracts.

===Clinical Component (Kubicki)===
Over the last year, algorythm has been tested first on the already available to NAMIC dataset of schizophrenia subjects acquired on 1.5T. Anterior Limb of the internal capsule, large structure connecting thalamus with frontal lobe, have been extracvted, and analyzed in group of 20 schizphrenics, and 20 control subjects, and results showing group differences in Fractional Anisotropy presented at the ACNP symposium in December 2007. Next, stochastic tractography was tested, and optimized for new, high resolution DTI dataset acquired on 3T GE magnet. Upon the completion of testing phase, we started analysis of arcuate fasciculus, language related fiber bundle, in 20 controls and 20 chronic schizphrenics. Whole brain as well as white matter segmentations, using freesurfer, as well as automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Supperior Temporal Gyri), as well as another ROI that would guide the tract ("waypoint" ROI) have been generated for all subjects. Finally, paths of interest were generated, and averaged FA extracted for each tract. Preliminary data based on 7 patients and 12 controls were presented at the AHM in January 2008, study is currently under way.

===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: Brachytherapy Needle Positioning Robot Integration (Fichtinger)==
===Overview (Fichtinger)===
===Algorithm Component (Tannenbaum)===
We have worked on both the segmentation and the registration of the prostate from MRI and ultrasound data. We explain each of the steps now.

====Prostate Segmentation====

We first must extract the prostate. We have considered three possible methods: a combination of a combination of Cellular Automata(CA also known as Grow Cut) with Geometric Active Contour(GAC) methods; employing an ellipsoid to match the prostate in 3D image; shape based approach using spherical wavelets. More details are given below and images and further details may be found at [[Projects:ProstateSegmentation|GaTech Algorithm Prostate Segmentation]].

1. A cellular automata algorithm is used to give an initial segmentation. It begins with a rough manual initialization and then iteratively classifies all pixels into object and bacground until convergence. It effectively overcomes the problems of weak boundaries and inhomogeneity within the object or background. This in turn is fed into Geometric Active Contour for finer tuning. We are initially using the edge-based minimal surface pproach (the generalization of the standard Geodesic Active Contour model) which seems to give very reasonable results. Both steps of the algorithm algorithm are implemented in 3D. A ITK-Cellular Automata filter, dealing with N-D data, has already been completed and submitted to the NA-MIC SandBox.

2. Spherical wavelets have proven to be a very natural way of representing 3D shapes which are compact and simply connected (topological spheres). We developed a segmentation framework using this 3D wavelet representation and multiscale prior. The parameters of our model are the learned shape parameters based on the spherical wavelet coefficients}, as well as pose parameters that accommodate for shape variability due to a similarity transformation (rotation, scale, translation) which is not explicitly modeled with the shape parameters. The transformed surface based on the pose parameters. We used a region-based energy to drive the evolution of the parametric deformable surface for segmentation. Our segmentation algorithm deforms an initial surface according to the gradient flow that minimizes the energy functional in terms of the pose and shape parameters. Additionally, the optimization method can be applied in a coarse to fine manner. Spherical wavelets and conformal mappings are
already part of the NA-MIC SandBox.

3. The third method is very closely related to the second. It is based on the observation that the prostate may be roughly modelled as an ellipsoid. One can then employing this ellipsoid model coupled with a local/global segmentation energy approach which we have developed this year, as the basis of a segmentation procedure. Because of the local/global nature of the functional and the implicit introduction of scale this methodology may be very useful for MRI prostate data.

====Prostate Registration====

The registration and segmentation elements of our algorithm are difficult to separate. Thus for the 3D shape-driven segmentation part, the shapes must first be aligned through a conformal and area-correction alignment process. The prostate presents a number of difficulties for traditional approaches since there are no easily discernable landmarks. On the other hand, we observed that the surface of the prostate is almost half convex and half concave. The concave region may be captured and used to register the shapes, thus we register the whole shape by registering a certain region on it. Such concave region is characterized by its negative mean curvature. We treat the mean curvature as a scalar field defined on the surface, and we have extended the Chan-Vese method (in which one wants to separate the means with respect to the regions defined by the interior and exterior of the evolving active contour) to the case at hand on the prostate surface. The method is implemented in C++ and it successfully extracts the concave surface region. This method could also be used to exact regions on surface according to any feature charactered by a scalar field defined on the surface.

In order incorporate the extracted region as landmarks into the registration process, instead of matching two binary images directly, we transform the binary images into a form to highlight the boundary region. This is done by applying a Gauss function on the (narrow band) of the signed distance function of the binary image. The transformed image enjoys the advantages of both the parametric and implicit representations of shapes. Namely it has compact description, as the parametric representation does, and as in the implicit representation it avoids the correspondence problem. Moreover we incorporate the extracted concave regions into such images for registration which leads to a better result.

Finally, in the past year we have developed a particle filtering approach for the general problem of registering two point sets that differ by a rigid body transformation which may be very useful for this project. Typically, registration algorithms compute the transformation parameters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem, in which the corresponding distribution can naturally be estimated using a particle filter. We treat motion as a local variation in pose parameters obtained from running several iterations of the standard Iterative Closest Point (ICP) algorithm. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence often found in local optimizer functions used to tackle the registration task. In contrast with other techniques, this approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temporal 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.

===Engineering Component (Hayes)===
===Clinical Component (Fichtinger)===
===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)===
===Algorithm Component (Whitaker)===
===Engineering Component (Pieper)===
===Clinical Component (Bockholt)===
===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.
Tissue segmentation: We have successfully adapted the UNC segmentation tool called itkEMS to Slicer, which we have for segmentations of the young brain. We also created a young brain atlas for the current Slicer3 EM Segment module. Tests have been successful and a comparative study to itkEMS has shown that further parameter optimization is needed to reach the same quality.

====Cortical thickness measurement====
The UNC algorithm for the measurement of local cortical thickness given a labeling of white matter and gray matter has been developed into a Slicer3 external module. This module lends itself well for regional analysis of cortical thickness, but less so for local analysis due to its non-symmetric and sparse measurements. Ongoing development is focusing on a symmetric, Laplacian based cortical thickness suitable for local analysis.

====Cortical correspondence (regional)====

For regional correspondence, an existing lobar parcellation atlas is deformably registered using a b-spline registration tool. First tests have been very promising and the release of the corresponding Slicer 3 registration module is schedule to be finished within the next month and thus the regional analysis workflow will be available at that time.

====Cortical correspondence (local)====
Local cortical correspondence requires a two-step process of white/gray surface inflation followed by group-wise correspondence computation. White matter surface extraction and inflation is currently achieved with an external tool and developing a Slicer 3 based solution is a goal in the next year. The group-wise correspondence step has been fully solved, and a Slicer 3 module is already available. Evaluation on real data has shown that our method outperforms the currently widely employed Freesurfer framework.

====Statistical analysis/Hypothesis testing====
Regional analysis can be done with standard statistical tools such as MANOVA as there are a limited, relatively small number of regions. Local analysis on the other hand needs local non-parametric testing, multiple-comparison correction, and correlative analysis that is not routinely available. We are currently extending the current Slicer 3 module designed for statistical shape analysis to be used for this purpose incorporating a local applied General Linear Module and MANCOVA based testing framework.

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

Several of the algorithms for this Clinical Roadmap project were already in software tools utilizing ITK. These tools have been refactored to be NA-MIC compatible and repackaged as Slicer3 plugins. Slicer3 has been extended to support this Clinical Roadmap by adding transforms as a parameter type that can be passed to and returned by plugins. Slicer3 registration and resampling modules have been refactored to produce and accept transforms as parameters. Slicer3 has also been extended to support nonlinear transformation types (B-Spline and deformation fields) in its data model.

===Clinical Component (Hazlett)===
During this past year, work has proceeded satisfactorily, and we anticipate being able to test some of our project hypotheses about cortical thickness in autism by the end of our project period. So far, the clinical component has involved interfacing with the algorithms and engineering teams to provide the project specifications, feedback, and data (needed for testing). Therefore, the primary accomplishment of this first year has been the development and testing of methods that are necessary for this cortical thickness work pipeline.

===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)==
===Progress===
===Key Investigators===
===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 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.

Structural Segmentation

* Directional Based Segmentation
We have proposed a directional segmentation framework for Direction-weighted Magnetic Resonance imagery by augmenting the Geodesic Active Contour framework with directional information. The classical scalar conformal factor is replaced by a factor that incorporates directionality. We mathematically showed that the optimization problem is well-defined when the factor is a Finsler metric. The calculus of variations or dynamic programming may be used to find the optimal curves. This past year we have applied this methodology in extracting the anchor tract (or centerline) of neural fiber bundles. Further we have applied this in conjunction with the Bayes’ rule into volumetric segmentation for extracting the entire fiber bundles. We have also proposed a novel shape prior in the volumetric segmentation to extract tubular fiber bundles.

* Stochastic Segmentation

We have continued work this year on developing new stochastic methods for implementing curvature-driven flows for medical tasks like segmentation. We can now generalize our results to an arbitrary Riemannian surface which includes the geodesic active contours as a special case. We are also implementing the directional flows based on the anisotropic conformal factor described above using this stochastic methodology. Our stochastic snakes’ models are based on the theory of interacting particle systems. This brings together the theories of curve evolution and hydrodynamic limits, and as such impacts our growing use of joint methods from probability and partial differential in image processing and computer vision. We now have working code written in C++ for the two dimensional case and have worked out the stochastic model of the general geodesic active contour model.

* Statistical PDE Methods for Segmentation

Our objective is to add various statistical measures into our PDE flows for medical imaging. This will allow the incorporation of global image information into the locally defined PDE framework. This year, we developed flows which can separate the distributions inside and outside the evolving contour, and we have also been including shape information in the flows. We have completed a statistically based flow for segmentation using fast marching, and the code has been integrated into Slicer.

* Atlas Renormalization for Improved Brain MR Image Segmentation

Atlas-based approaches can automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. However, the accuracy often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this project, we work to 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 shows that the new procedure improves 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.

*Multiscale Shape Segmentation Techniques

The goal of this project is to represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. Our technique defines a multiscale parametric model of surfaces belonging to the same population using a compact set of spherical wavelets targeted to that population. We derived a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior for segmentation. Additionally, the optimization method can be applied in a coarse-to-fine manner. We applied our algorithm to the caudate nucleus, a brain structure of interest in the study of schizophrenia. Our validation shows that our algorithm is computationally efficient and outperforms the Active Shape Model (ASM) algorithm, by capturing finer shape details.

Registration

* Optimal Mass Transport Registration
The aim of this project is to provide a computationally 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 gradient flow PDE approach using multi-resolution and multi-grid techniques to speed up the convergence. We also leverage the computational power of general purpose graphics processing units available on standard desktop computing machines to exploit the inherent parallelism in our algorithm. We have implemented 2D and 3D multi-resolution registration using Optimal Mass Transport and are currently working on the registration of 3D datasets.

* Diffusion Tensor Image Processing Tools

We aim to provide methods for computing geodesics and distances between diffusion tensors. One goal is to provide hypothesis testing for differences between groups. This will involve interpolation techniques for diffusion tensors as weighted averages in the metric framework. We will also provide filtering and eddy current correction. This year, we developed a Slicer module for DT-MRI Rician noise removal, developed prototypes of DTI geometry and statistical packages, and began work on a general method for hypothesis testing between diffusion tensor groups.

* Point Set Rigid Registration

We propose a particle filtering scheme for the registration of 2D and 3D point set undergoing a rigid body transformation where we incorporate stochastic dynamics to model the uncertainty of the registration process. Typically, registration algorithms compute the transformations parameters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem, in which the corresponding distribution can naturally be estimated using a particle filter. In this work, we treat motion as a local variation in the pose parameters obtained 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 uncertainty. In contrast with other techniques, our approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temporal 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.

* Cortical Correspondence using Particle System

In this project, we want to compute cortical correspondence on populations, using various features such as cortical structure, DTI connectivity, vascular structure, and functional data (fMRI). This presents a challenge because of the highly convoluted surface of the cortex, as well as because of the different properties of the data features we want to incorporate together. We would like to use a particle based entropy minimizing system for the correspondence computation, in a population-based manner. This is advantageous because it does not require a spherical parameterization of the surface, and does not require the surface to be of spherical topology. It would also eventually enable correspondence computation on the subcortical structures and on the cortical surface using the same framework. To circumvent the disadvantage that particles are assumed to lie on local tangent planes, we plan to first ‘inflate’ the cortex surface. Currently, we are at testing stage using structural data, namely, point locations and sulcal depth (as computed by FreeSurfer).

* Multimodal Atlas

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called iCluster for Image Clustering, is based on the following idea: given the templates, the co-registration problem becomes simple, reducing to a number of pairwise registration instances. On the other hand, given a collection of images that have been co-registered, an off-the shelf clustering or averaging algorithm can be used to compute the templates. The algorithm assumed a fixed and known number of template images. We formulate the problem as a maximum likelihood solution and employ a Generalized Maximum Likelihood algorithm to solve it. In the E-step, we compute membership probabilities. In the M-step, we update the template images as weighted averages of the images, where weights are the memberships and the template priors are updated, and then perform a collection of independent pairwise registration instances. The algorithm is currently implemented in the Insight ToolKit (ITK) and we next plan to integrate it into Slicer.

* Groupwise Registration

We aim at providing efficient groupwise registration algorithms for population analysis of anatomical structures. Here we extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment. Our results indicate that increasing the complexity of the deformation model improves registration accuracy significantly, especially at cortical regions.

Shape Analysis

* Shape Analysis Framework Using SPHARM-PDM

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described by sampled spherical harmonics SPHARM-PDM. The input of the proposed shape analysis is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. Group tests can be visualized by P-values and by mean difference magnitude and vector maps, as well as maps of the group covariance information. The implementation has reached a stable framework and has been disseminated to several collaborating labs within NAMIC (BWH, Georgia Tech, Utah). The current development focuses on integrating the current command line tools into the Slicer (v3) via the Slicer execution model. The whole shape analysis pipeline is encapsulated and accessible to the trained clinical collaborator. The current toolset distribution (via NeuroLib) now also contains open data for other researchers to evaluate their shape analysis enhancements.

* Multiscale Shape Analysis

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. As an application, we analyze two brain structures, the caudate nucleus and the hippocampus. We show that the results nicely complement the results obtained with shape analysis using a sampled point representation (SPHARM-PDM). We used the UNC pipeline to pre-process the images, and for each triangulated SPHARM-PDM surface, a spherical wavelet description is computed. We then use the UNC statistical toolbox to analyze differences between two groups of surfaces described by the features of choice that is the 3D spherical wavelet coefficients. This year, we conducted statistical shape analysis of the two brain structures and compared the results obtained to shape analysis using a SPHARM-PDM representation.

* Population Analysis of Anatomical Variability

In contrast to shape-based segmentation that utilizes a statistical model of the shape variability in one population (typically based on Principal Component Analysis), we are interested in identifying and characterizing differences between two sets of shape examples. We use the discriminative framework to characterize the differences in shape by training a classifier function and studying its sensitivity to small perturbations in the input data. An additional benefit is that the resulting classifier function can be used to label new examples into one of the two populations, e.g., for early detection in population screening or prediction in longitudinal studies. We have implemented stand alone code for training a classifier, jackknifing and permutation testing, and are currently porting the software into ITK. We have also started exploring alternative, surface-based descriptors which are promising in improving our ability to detect and characterize subtle differences in the shape of anatomical structures due to diseases such as schizophrenia.

* Shape Analysis with Overcomplete Wavelets

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development and show significantly consistent results as well as improved sensitivity compared with the previously used bi-orthogonal spherical wavelet. In particular, we are able to detect developmental asymmetry in the left and right hemispheres.

*Shape based Segmentation and Registration

When there is little or no contrast along boundaries of different regions, standard image segmentation algorithms perform poorly and segmentation is done manually using prior knowledge of shape and relative location of underlying structures. We have proposed 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. Our method filters out the noise as compared to thresholding using initial likelihoods, and it captures multiple structures as in the brain (where both major brain compartments and subcortical structures are obtained) because it naturally evolves families of curves. The algorithm is currently implemented in 3D Slicer Version 2.6 and a beta version is available in 3D Slicer Version 3.

*Spherical Wavelets

In this project, we apply a spherical wavelet transformation to extract shape features of cortical surfaces reconstructed from magnetic resonance images (MRI) of a set of subjects. The spherical wavelet transformation can characterize the underlying functions in a local fashion in both space and frequency, in contrast to spherical harmonics that have a global basis set. We perform principal component analysis (PCA) on these wavelet shape features to study patterns of shape variation within normal population from coarse to fine resolution. In addition, we study the development of cortical folding in newborns using the Gompertz model in the wavelet domain, allowing us to characterize the order of development of large-scale and finer folding patterns independently. We develop an efficient method to estimate the regularized Gompertz model based on the Broyden–Fletcher–Goldfarb–Shannon (BFGS) approximation. Promising results are presented using both PCA and the folding development model in the wavelet domain. The cortical folding development model provides quantitative anatomical information regarding macroscopic cortical folding development and may be of potential use as a biomarker for early diagnosis of neurological deficits in newborns.

===Key Investigators===
* 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
* 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===
===Key Investigators===
===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===
===Key Investigators===
* Kitware - Will Schroeder (Core 2 PI), Sebastien Barre, Luis Ibanez, Bill Hoffman
* GE - Jim Miller, Xiaodong Tao
* Isomics - Steve Pieper

===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].
==Other Projects==
Any Project(s) not covered by the 8 sections above

==Highlights(Schroeder)==
===EM Segmenter or TBD===
===DTI progress or TBD===
===Outreach (Gollub)===

NAMIC outreach is a joint effort of Cores 4, 5 and 6. The various mechanisms by which we ensure that the tools developed by NAMIC are rapidly and successfully deployed to the widest possible extent within the scientific community are closely integrated. This begins with the immediate posting of all software tools, interim updates and associated documentation via the NAMIC and Slicer wiki pages (links). The concerted effort to provide a harmonious visualization and analysis platform (Slicer 3) that enables the integration of the software algorithms of all Core 1 laboratories drives the sequence of development of training materials. With the January 2008 release of Slicer 3 in beta format, we prepared the first of the Slicer 3 based Powerpoint tutorials that guide new users through the process of loading, interacting with and saving data in Slicer 3. Given the intense and successful effort at engineering this platform to facilitate the process of integrating new command-line modules of image analysis software into the platform, our second tutorial targeted software developers . The "Hello World" tutorial guides a programmer, step-by-step through the process of integrating a command line tool into Slicer 3. Both these tutorials are available via the web (link). These tutorials have been thoroughly tested by using them in large Workshops (see next) to ensure that they are robust across platform (Linux, Mac, PC) and can be used successfully by users across a wide range of training backgrounds.
 
In June of 2007 as a satellite event to the international Organization for Human Brain Mapping annual meeting in Chicago, IL we ran an 8 hour workshop on analysis of Diffusion Imaging Data (link); it was our final Slicer workshop based on the Slicer 2.7 release. The Workshop rapidly filled after posting, the 50 participants represented 9 countries from around the world, 14 states within the US and 40 different laboratories including 2 NIH institutes. The single "no-show" was due to a European flight cancellation. The attendees, with backgrounds in basic or clinical neurosciences, physics, image processing or computer science, ranging from full professors to new graduate students were very comfortable learning together. The feedback from the workshop attendees was uniformly positive with 100% reporting that they would recommend the workshop to others and 50% planning to apply the tools and information they learned to their own work.
 
In January 2008 we debuted the "Hello World" tutorial at the NAMIC AHM in Salt Lake City to an audience of our project members and collaborators. This very constructive presentation was used to make significant improvements in the presentation and delivery of this material. In February 2008 we debuted the users tutorial at a workshop hosted by the Surgical Planning Laboratory at BWH. Again, this presentation was used to make significant improvements in the presentation and delivery of the material. In April of 2008 we ran an all day workshop, hosted by UNC (get details right) for users and developers that incorporated both tutorials. This was attended by approximately 20 individuals coming from a wide range of backgrounds. Time was taken to ensure that all participants gained significant understanding of the new software, sufficient to ensure their successful use of it following the workshop.
 
This year saw the publication of a peer-reviewed manuscript that describes the NAMIC approach to outreach including our multi-disciplinary approach, our integration of theory into practice as driven by a clinical goal, and the translation of concepts into skills through interactive instructor led training sessions (Pujol S, Kikinis R, Gollub R: Lowering the barriers inherent in translating advances in neuroimage analysis to clinical research applications, Academic Radiology 15: 114-118, 2008, add link to Publication DB).
* Text here about Project Events 5 & 6 from Tina if not already included elsewhere.
* Text here about the MICCAI Open Source Workshop if not already included elsewhere (Steve?)
* Slicer IGT event December 2007 (tina?)
* Wiki to web
* Impact as measured by number of downloads of tutorial materials (help someone)
* Should the DTI tractography validation project be written up somewhere, if so where? I will do it if it isn't already assigned.

==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===
Within the center, NA-MIC has formed a community around its software
engineering tools, imaging platforms, algorithms, and clinical
workflows. The NA-MIC calendar includes the All Hands Meeting and
Winter Project Week, the Spring Algorithm Meeting, the Summer Project
Week, Slicer3 Mini-Retreats, Core Site Visits, Training Workshops, and weekly telephone
conferences.

The NA-MIC software engineering tools (CMake, Dart, CTest, CPack) have
enabled the development and distribution of a cross-platform, nightly
tested, end-user application, Slicer3, that is a complex union of
novel application code, visualization tools (VTK), imaging libraries
(ITK, TEEM), user interface libraries (Tk, KWWidgets), and scripting
languages (TCL, Python). The NA-MIC software engineering tools have been
essential in the development and distribution of the Slicer3 imaging
platform to the NA-MIC community.

NA-MIC's end-user application, Slicer3, supports the research within
NA-MIC by providing a base application for visualization and data
management. Slicer3 also supports the research within NA-MIC by
providing plugin mechanisms which allow researchers to quickly and
easily integrate and distribute their technology with Slicer3. Slicer3
is available to all center participants and the external community
through its source code repository, official binary releases, and
unofficial nightly binary snapshots.

NA-MIC drives the development of platforms and algorithms through the
needs and research of its DBPs. Each DBP has selected specific
workflows and roadmaps as focal points for development with a goal of
providing the community with complete end-to-end solutions using
NA-MIC tools. The community will be able to reproduce these workflows
and roadmaps in their own research programs.

NA-MIC algorithms are designed and used to address specific needs of
the DBPs. Multiple solution paths are explored and compared within
NA-MIC, resulting in recommendations to the field. The NA-MIC
algorithm groups collaborate and orchestrate the solutions to the
DBP workflows and roadmaps.

===Impact within NIH Funded Research===
Within NIH funded research, NA-MIC is the NCBC collaborating center for three R01's: "Automated FE Mesh Development", "Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI", and "An Integrated System for Image-Guided Radiofrequency Ablation of Liver Tumors". Several other proposals have been submitted and are under
evaluation for the "Collaborations with NCBC PAR". NA-MIC also
collaborates on the Slicer3 platform with the NIH funded Neuroimage
Analysis Center and the National Center for Image-Guided Therapy. The
NIH funded "BRAINS Morphology and Image Analysis" project is also
leveraging NA-MIC and Slicer3 technology. NA-MIC collaborates with the
NIH funded Neuroimaging Informatics Tools and Resources Clearinghouse
on distribution of Slicer3 plugin modules.

===National and International Impact===
NA-MIC events and tools garner national and international interest.
Over 100 researchers participated in the NA-MIC All Hands Meeting and
Winter Project Week in January 2008. Many of these participants were
from outside of NA-MIC, attending the meetings to gain access to the
NA-MIC tools and researchers. These external researchers are
contributing ideas and technology back into NA-MIC. In fact, a
breakout session at the Winter Project Week on "Geometry and Topology
Processing of Meshes" was organized by four researchers from outside
of NA-MIC.

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

NA-MIC science is recognized by the medical imaging community. Over
100 NA-MIC related publications are listed on PubMed. Many of these
publications are in the most prestigious journals and conferences in the
field. Portions of the DBP workflows and roadmaps are already being
utilized by researchers in the broader community and in the
development of commercial products.

NA-MIC sponsored several events to promote NA-MIC tools and
methodologies. NA-MIC co-sponsored the "Third Annual Open Source
Workshop" at the Medical Image Computing and Computer-Assisted
Intervention (MICCAI) 2007 conference. The proceedings of the
workshop are published on the electronic Insight Journal, another
NIH-funded activity. NA-MIC sponsored three training workshops on
NA-MIC tools for the Biocomputing community in this fiscal year and
plans to hold sessions at upcoming MICCAI and RSNA conferences.

==NA-MIC Timeline (Whitaker)==

==Appendix A Publications (Kapur)==
These 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.

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

2008 Annual Scientific Report

2008-05-14T16:55:33Z

Hazlett: /* Clinical Component (Hazlett) */

Back to [[2008_Progress_Report]]

=Guidelines for preparation=

*[[2008_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 2007 report, which is provided here for reference: [[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 in its fourth year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The first driving biological projects (DBPs) three years for Center were inspired by schizophrenia research. In the fourth year new DBPs have been added. Three are centered around diseases of the brain: (a) brain lesion analysis in neuropschiatric systemic lupus erythematosus; (b) a study of cortical thickness for autism; and (c) stochastic tractography for VCFS. In an very new direction, we have added DBP on the prostate: brachytherapy needle positioning robot integration.

We briefly summarize the work of NAMIC during the four years of its existence. In the year one of the Center, alliances were forged amongst the cores and constituent groups in order to integrate the efforts of the cores and to define the kinds of tools needed for specific imaging applications. The second year emphasized the identification of the key research thrusts that cut across cores and were driven by the needs and requirements of the DBPs. This led to the formulation of the Center's four main 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 the collaborative efforts in order to give solutions to the various brain-oriented DBPs.

Year four has seen progress with the work of our new DBPs. As alluded to above these include work on neuropsychiatric disorders such as Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), as well as the prostate interventional work (Johns Hopkins and Queens Universities). We already have a number of publications as is indicated on our publications page, and software development is continuing as well.

In the next section (Section 3), we summarize this year’s progress on the four roadmap projects listed above: Section 3.1 stochastic tractography for Velocardiofacial Syndrome, Section 3.2 brachytherapy needle positioning for the prostate, Section 3.3 brain lesion analysis in neuropschiatric systemic lupus erythematosus, and Section 3.4 cortical thickness for autism. Next in Section 4, we describe recent 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). In Section 4.5, we outline some of the other key projects, in Section 4.6 some key highlights including the integration of the EM Segmentor into Slicer, and in Section 4.7 the impact of biocomputing at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 4.8, provides a timeline of Center activities.

=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 would be usefull in evaluating anatomical connectivity between segmented cortical regions of the brain. The ultimate goal of our program is to understand anatomical connectivity similarities and differences between genetically related schizophrenia and velocardio-fatial syndrome. Thus we plan to use the "stochastic tractography" tool for the analysis of abnormalities in integrity, or connectivity, provided by arcuate fasciculus, fiber bundle involved in language processing, in schizophrenia and VCFS.

===Algorithm Component (Golland)===
Over the last year, algorythm has been tested first on the already available to NAMIC dataset of schizophrenia subjects acquired on 1.5T. This step allowed us to optimize algorythm to our dataset, as well as to develop the pipeline for data analysis that would be then easilly transferable to other datasets, and structures. For this, as well as other applications, we use gray matter labels derived from either automatic or manual segmentation of structural MRI. Thus the first step was to obtain registration between diffusion and anatomical data. Right now, demon's registration alogythm that is part of slicer is being used, but we are also testing b-spline registration, as well as fluid? registration for this purpose. Next step, also accomplished this last year, was to apply the alogythm to new, higher resolution NAMIC dataset, and study smaller white matter connections including cingulum bundle, arcuate fasciculus, uncinate fasciculus and internal capsule. This step was accomplished and data presented at the Santa Fee meeting in October 2007. Algorythm was also additionally tested on the phantom, where differences in coordinate systems were debugged. Upon the completion of testing phase, we started analysis of arcuate fasciculus, language related fiber bundle, in new 3T, high resolution dataset. Additional step turned out to be required in preprocessing, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high, thus white matter sesgmentation was performed using free surfer, and co-registered with DTI. Our current work focuses on better registration alogythms, as well as the way to parametrize tracts, in order to obtain FA measurements along the tracts.

===Engineering Component (Davis)===
Stochastic Tractography slicer module has been finished, and presented at the AHM in SLC. Its now part of the slicer2.8 and slicer3. Module documentation have been also created.
Current engeneering effords are concentrated on maintaining the module, optimizing it for working with other data formats, and adding new functionality, such as better registration, distortion correction and ways of extracting and measuring FA along the tracts.

===Clinical Component (Kubicki)===
Over the last year, algorythm has been tested first on the already available to NAMIC dataset of schizophrenia subjects acquired on 1.5T. Anterior Limb of the internal capsule, large structure connecting thalamus with frontal lobe, have been extracvted, and analyzed in group of 20 schizphrenics, and 20 control subjects, and results showing group differences in Fractional Anisotropy presented at the ACNP symposium in December 2007. Next, stochastic tractography was tested, and optimized for new, high resolution DTI dataset acquired on 3T GE magnet. Upon the completion of testing phase, we started analysis of arcuate fasciculus, language related fiber bundle, in 20 controls and 20 chronic schizphrenics. Whole brain as well as white matter segmentations, using freesurfer, as well as automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Supperior Temporal Gyri), as well as another ROI that would guide the tract ("waypoint" ROI) have been generated for all subjects. Finally, paths of interest were generated, and averaged FA extracted for each tract. Preliminary data based on 7 patients and 12 controls were presented at the AHM in January 2008, study is currently under way.

===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: Brachytherapy Needle Positioning Robot Integration (Fichtinger)==
===Overview (Fichtinger)===
===Algorithm Component (Tannenbaum)===
We have worked on both the segmentation and the registration of the prostate from MRI and ultrasound data. We explain each of the steps now.

====Prostate Segmentation====

We first must extract the prostate. We have considered three possible methods: a combination of a combination of Cellular Automata(CA also known as Grow Cut) with Geometric Active Contour(GAC) methods; employing an ellipsoid to match the prostate in 3D image; shape based approach using spherical wavelets. More details are given below and images and further details may be found at [[Projects:ProstateSegmentation|GaTech Algorithm Prostate Segmentation]].

1. A cellular automata algorithm is used to give an initial segmentation. It begins with a rough manual initialization and then iteratively classifies all pixels into object and bacground until convergence. It effectively overcomes the problems of weak boundaries and inhomogeneity within the object or background. This in turn is fed into Geometric Active Contour for finer tuning. We are initially using the edge-based minimal surface pproach (the generalization of the standard Geodesic Active Contour model) which seems to give very reasonable results. Both steps of the algorithm algorithm are implemented in 3D. A ITK-Cellular Automata filter, dealing with N-D data, has already been completed and submitted to the NA-MIC SandBox.

2. Spherical wavelets have proven to be a very natural way of representing 3D shapes which are compact and simply connected (topological spheres). We developed a segmentation framework using this 3D wavelet representation and multiscale prior. The parameters of our model are the learned shape parameters based on the spherical wavelet coefficients}, as well as pose parameters that accommodate for shape variability due to a similarity transformation (rotation, scale, translation) which is not explicitly modeled with the shape parameters. The transformed surface based on the pose parameters. We used a region-based energy to drive the evolution of the parametric deformable surface for segmentation. Our segmentation algorithm deforms an initial surface according to the gradient flow that minimizes the energy functional in terms of the pose and shape parameters. Additionally, the optimization method can be applied in a coarse to fine manner. Spherical wavelets and conformal mappings are
already part of the NA-MIC SandBox.

3. The third method is very closely related to the second. It is based on the observation that the prostate may be roughly modelled as an ellipsoid. One can then employing this ellipsoid model coupled with a local/global segmentation energy approach which we have developed this year, as the basis of a segmentation procedure. Because of the local/global nature of the functional and the implicit introduction of scale this methodology may be very useful for MRI prostate data.

====Prostate Registration====

The registration and segmentation elements of our algorithm are difficult to separate. Thus for the 3D shape-driven segmentation part, the shapes must first be aligned through a conformal and area-correction alignment process. The prostate presents a number of difficulties for traditional approaches since there are no easily discernable landmarks. On the other hand, we observed that the surface of the prostate is almost half convex and half concave. The concave region may be captured and used to register the shapes, thus we register the whole shape by registering a certain region on it. Such concave region is characterized by its negative mean curvature. We treat the mean curvature as a scalar field defined on the surface, and we have extended the Chan-Vese method (in which one wants to separate the means with respect to the regions defined by the interior and exterior of the evolving active contour) to the case at hand on the prostate surface. The method is implemented in C++ and it successfully extracts the concave surface region. This method could also be used to exact regions on surface according to any feature charactered by a scalar field defined on the surface.

In order incorporate the extracted region as landmarks into the registration process, instead of matching two binary images directly, we transform the binary images into a form to highlight the boundary region. This is done by applying a Gauss function on the (narrow band) of the signed distance function of the binary image. The transformed image enjoys the advantages of both the parametric and implicit representations of shapes. Namely it has compact description, as the parametric representation does, and as in the implicit representation it avoids the correspondence problem. Moreover we incorporate the extracted concave regions into such images for registration which leads to a better result.

Finally, in the past year we have developed a particle filtering approach for the general problem of registering two point sets that differ by a rigid body transformation which may be very useful for this project. Typically, registration algorithms compute the transformation parameters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem, in which the corresponding distribution can naturally be estimated using a particle filter. We treat motion as a local variation in pose parameters obtained from running several iterations of the standard Iterative Closest Point (ICP) algorithm. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence often found in local optimizer functions used to tackle the registration task. In contrast with other techniques, this approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temporal 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.

===Engineering Component (Hayes)===
===Clinical Component (Fichtinger)===
===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)===
===Algorithm Component (Whitaker)===
===Engineering Component (Pieper)===
===Clinical Component (Bockholt)===
===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.
Tissue segmentation: We have successfully adapted the UNC segmentation tool called itkEMS to Slicer, which we have for segmentations of the young brain. We also created a young brain atlas for the current Slicer3 EM Segment module. Tests have been successful and a comparative study to itkEMS has shown that further parameter optimization is needed to reach the same quality.

====Cortical thickness measurement====
The UNC algorithm for the measurement of local cortical thickness given a labeling of white matter and gray matter has been developed into a Slicer3 external module. This module lends itself well for regional analysis of cortical thickness, but less so for local analysis due to its non-symmetric and sparse measurements. Ongoing development is focusing on a symmetric, Laplacian based cortical thickness suitable for local analysis.

====Cortical correspondence (regional)====

For regional correspondence, an existing lobar parcellation atlas is deformably registered using a b-spline registration tool. First tests have been very promising and the release of the corresponding Slicer 3 registration module is schedule to be finished within the next month and thus the regional analysis workflow will be available at that time.

====Cortical correspondence (local)====
Local cortical correspondence requires a two-step process of white/gray surface inflation followed by group-wise correspondence computation. White matter surface extraction and inflation is currently achieved with an external tool and developing a Slicer 3 based solution is a goal in the next year. The group-wise correspondence step has been fully solved, and a Slicer 3 module is already available. Evaluation on real data has shown that our method outperforms the currently widely employed Freesurfer framework.

====Statistical analysis/Hypothesis testing====
Regional analysis can be done with standard statistical tools such as MANOVA as there are a limited, relatively small number of regions. Local analysis on the other hand needs local non-parametric testing, multiple-comparison correction, and correlative analysis that is not routinely available. We are currently extending the current Slicer 3 module designed for statistical shape analysis to be used for this purpose incorporating a local applied General Linear Module and MANCOVA based testing framework.

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

Several of the algorithms for this Clinical Roadmap project were already in software tools utilizing ITK. These tools have been refactored to be NA-MIC compatible and repackaged as Slicer3 plugins. Slicer3 has been extended to support this Clinical Roadmap by adding transforms as a parameter type that can be passed to and returned by plugins. Slicer3 registration and resampling modules have been refactored to produce and accept transforms as parameters. Slicer3 has also been extended to support nonlinear transformation types (B-Spline and deformation fields) in its data model.

===Clinical Component (Hazlett)===
During this past year, work has proceeded satisfactorily, and we anticipate reaching the goal of our project (i.e. cortical thickness measurements) by the end of our project period. Because a number of components (see above) are required before we can obtain the cortical thickness measurements on our data, this first year has focused on developing and testing methods that are components of the the work pipeline. Below is a brief summary of what has been accomplished in each component, followed by some of our goals for year two:

(1) Tissue Segmentation: Tissue segmentation (white matter/gray matter) using multi-modal non-skull stripped data has been computed using the Slicer3 EM Segment module, and also using itkEMS as a Slicer3 module. A comparison study of itkEMS and EM Segment has been completed (note that tests indicate EM Segment is less reliable than itkEMS, and work to improve this will be done by other NA-MIC collaborators). These were key initial problems before we could attempt to do cortical thickness in Slicer3. Goals in this domain have been successfully reached.

(2) Cortical Thickness: The UNC cortical thickness tool (CortThick) has been adapted to work as a Slicer3 module, and a Laplacian cortical thickness measurement code (M. Neithammer) has successfully been implemented at UNC as a Slicer3 module. Goals for year 2 include conducting regional comparisons of the two cortical thickness modules (UNC CortThick and Neithammer’s Laplacian code).

(3) Cortical Correspondence: A spherical topology tool (M. Neithammer) has been tested on the cortical data. Goals for year 2 include completing a Slicer3 module for cortical correspondence based on the UNC Core 1 cortical correspondence Slicer module, using an inflated white/gray matter surface, and evaluating these measures on our pediatric dataset.

(4) Statistical analysis/hypothesis testing: We have not yet been able to do hypothesis testing, but goals for year 2 include testing the Slicer3 cortical thickness analysis module and conducting groupwise comparisons of regional and local cortical thickness. While standard statistical methods currently exist to examine the regional cortical thickness, there are no standard statistical tools available to deal with the large data generated for the local cortical thickness comparisons. There is development underway (as part of NA-MIC UNC Core 1) to create a framework to do such analysis in Slicer3, and that will take into account the multiple comparisons problem in a general linear model, allowing us to do groupwise analysis, and across development.

===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)==
===Progress===
===Key Investigators===
===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 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.

Structural Segmentation

* Directional Based Segmentation
We have proposed a directional segmentation framework for Direction-weighted Magnetic Resonance imagery by augmenting the Geodesic Active Contour framework with directional information. The classical scalar conformal factor is replaced by a factor that incorporates directionality. We mathematically showed that the optimization problem is well-defined when the factor is a Finsler metric. The calculus of variations or dynamic programming may be used to find the optimal curves. This past year we have applied this methodology in extracting the anchor tract (or centerline) of neural fiber bundles. Further we have applied this in conjunction with the Bayes’ rule into volumetric segmentation for extracting the entire fiber bundles. We have also proposed a novel shape prior in the volumetric segmentation to extract tubular fiber bundles.

* Stochastic Segmentation

We have continued work this year on developing new stochastic methods for implementing curvature-driven flows for medical tasks like segmentation. We can now generalize our results to an arbitrary Riemannian surface which includes the geodesic active contours as a special case. We are also implementing the directional flows based on the anisotropic conformal factor described above using this stochastic methodology. Our stochastic snakes’ models are based on the theory of interacting particle systems. This brings together the theories of curve evolution and hydrodynamic limits, and as such impacts our growing use of joint methods from probability and partial differential in image processing and computer vision. We now have working code written in C++ for the two dimensional case and have worked out the stochastic model of the general geodesic active contour model.

* Statistical PDE Methods for Segmentation

Our objective is to add various statistical measures into our PDE flows for medical imaging. This will allow the incorporation of global image information into the locally defined PDE framework. This year, we developed flows which can separate the distributions inside and outside the evolving contour, and we have also been including shape information in the flows. We have completed a statistically based flow for segmentation using fast marching, and the code has been integrated into Slicer.

* Atlas Renormalization for Improved Brain MR Image Segmentation

Atlas-based approaches can automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. However, the accuracy often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this project, we work to 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 shows that the new procedure improves 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.

*Multiscale Shape Segmentation Techniques

The goal of this project is to represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. Our technique defines a multiscale parametric model of surfaces belonging to the same population using a compact set of spherical wavelets targeted to that population. We derived a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior for segmentation. Additionally, the optimization method can be applied in a coarse-to-fine manner. We applied our algorithm to the caudate nucleus, a brain structure of interest in the study of schizophrenia. Our validation shows that our algorithm is computationally efficient and outperforms the Active Shape Model (ASM) algorithm, by capturing finer shape details.

Registration

* Optimal Mass Transport Registration
The aim of this project is to provide a computationally 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 gradient flow PDE approach using multi-resolution and multi-grid techniques to speed up the convergence. We also leverage the computational power of general purpose graphics processing units available on standard desktop computing machines to exploit the inherent parallelism in our algorithm. We have implemented 2D and 3D multi-resolution registration using Optimal Mass Transport and are currently working on the registration of 3D datasets.

* Diffusion Tensor Image Processing Tools

We aim to provide methods for computing geodesics and distances between diffusion tensors. One goal is to provide hypothesis testing for differences between groups. This will involve interpolation techniques for diffusion tensors as weighted averages in the metric framework. We will also provide filtering and eddy current correction. This year, we developed a Slicer module for DT-MRI Rician noise removal, developed prototypes of DTI geometry and statistical packages, and began work on a general method for hypothesis testing between diffusion tensor groups.

* Point Set Rigid Registration

We propose a particle filtering scheme for the registration of 2D and 3D point set undergoing a rigid body transformation where we incorporate stochastic dynamics to model the uncertainty of the registration process. Typically, registration algorithms compute the transformations parameters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem, in which the corresponding distribution can naturally be estimated using a particle filter. In this work, we treat motion as a local variation in the pose parameters obtained 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 uncertainty. In contrast with other techniques, our approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temporal 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.

* Cortical Correspondence using Particle System

In this project, we want to compute cortical correspondence on populations, using various features such as cortical structure, DTI connectivity, vascular structure, and functional data (fMRI). This presents a challenge because of the highly convoluted surface of the cortex, as well as because of the different properties of the data features we want to incorporate together. We would like to use a particle based entropy minimizing system for the correspondence computation, in a population-based manner. This is advantageous because it does not require a spherical parameterization of the surface, and does not require the surface to be of spherical topology. It would also eventually enable correspondence computation on the subcortical structures and on the cortical surface using the same framework. To circumvent the disadvantage that particles are assumed to lie on local tangent planes, we plan to first ‘inflate’ the cortex surface. Currently, we are at testing stage using structural data, namely, point locations and sulcal depth (as computed by FreeSurfer).

* Multimodal Atlas

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called iCluster for Image Clustering, is based on the following idea: given the templates, the co-registration problem becomes simple, reducing to a number of pairwise registration instances. On the other hand, given a collection of images that have been co-registered, an off-the shelf clustering or averaging algorithm can be used to compute the templates. The algorithm assumed a fixed and known number of template images. We formulate the problem as a maximum likelihood solution and employ a Generalized Maximum Likelihood algorithm to solve it. In the E-step, we compute membership probabilities. In the M-step, we update the template images as weighted averages of the images, where weights are the memberships and the template priors are updated, and then perform a collection of independent pairwise registration instances. The algorithm is currently implemented in the Insight ToolKit (ITK) and we next plan to integrate it into Slicer.

* Groupwise Registration

We aim at providing efficient groupwise registration algorithms for population analysis of anatomical structures. Here we extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment. Our results indicate that increasing the complexity of the deformation model improves registration accuracy significantly, especially at cortical regions.

Shape Analysis

* Shape Analysis Framework Using SPHARM-PDM

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described by sampled spherical harmonics SPHARM-PDM. The input of the proposed shape analysis is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. Group tests can be visualized by P-values and by mean difference magnitude and vector maps, as well as maps of the group covariance information. The implementation has reached a stable framework and has been disseminated to several collaborating labs within NAMIC (BWH, Georgia Tech, Utah). The current development focuses on integrating the current command line tools into the Slicer (v3) via the Slicer execution model. The whole shape analysis pipeline is encapsulated and accessible to the trained clinical collaborator. The current toolset distribution (via NeuroLib) now also contains open data for other researchers to evaluate their shape analysis enhancements.

* Multiscale Shape Analysis

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. As an application, we analyze two brain structures, the caudate nucleus and the hippocampus. We show that the results nicely complement the results obtained with shape analysis using a sampled point representation (SPHARM-PDM). We used the UNC pipeline to pre-process the images, and for each triangulated SPHARM-PDM surface, a spherical wavelet description is computed. We then use the UNC statistical toolbox to analyze differences between two groups of surfaces described by the features of choice that is the 3D spherical wavelet coefficients. This year, we conducted statistical shape analysis of the two brain structures and compared the results obtained to shape analysis using a SPHARM-PDM representation.

* Population Analysis of Anatomical Variability

In contrast to shape-based segmentation that utilizes a statistical model of the shape variability in one population (typically based on Principal Component Analysis), we are interested in identifying and characterizing differences between two sets of shape examples. We use the discriminative framework to characterize the differences in shape by training a classifier function and studying its sensitivity to small perturbations in the input data. An additional benefit is that the resulting classifier function can be used to label new examples into one of the two populations, e.g., for early detection in population screening or prediction in longitudinal studies. We have implemented stand alone code for training a classifier, jackknifing and permutation testing, and are currently porting the software into ITK. We have also started exploring alternative, surface-based descriptors which are promising in improving our ability to detect and characterize subtle differences in the shape of anatomical structures due to diseases such as schizophrenia.

* Shape Analysis with Overcomplete Wavelets

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development and show significantly consistent results as well as improved sensitivity compared with the previously used bi-orthogonal spherical wavelet. In particular, we are able to detect developmental asymmetry in the left and right hemispheres.

*Shape based Segmentation and Registration

When there is little or no contrast along boundaries of different regions, standard image segmentation algorithms perform poorly and segmentation is done manually using prior knowledge of shape and relative location of underlying structures. We have proposed 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. Our method filters out the noise as compared to thresholding using initial likelihoods, and it captures multiple structures as in the brain (where both major brain compartments and subcortical structures are obtained) because it naturally evolves families of curves. The algorithm is currently implemented in 3D Slicer Version 2.6 and a beta version is available in 3D Slicer Version 3.

*Spherical Wavelets

In this project, we apply a spherical wavelet transformation to extract shape features of cortical surfaces reconstructed from magnetic resonance images (MRI) of a set of subjects. The spherical wavelet transformation can characterize the underlying functions in a local fashion in both space and frequency, in contrast to spherical harmonics that have a global basis set. We perform principal component analysis (PCA) on these wavelet shape features to study patterns of shape variation within normal population from coarse to fine resolution. In addition, we study the development of cortical folding in newborns using the Gompertz model in the wavelet domain, allowing us to characterize the order of development of large-scale and finer folding patterns independently. We develop an efficient method to estimate the regularized Gompertz model based on the Broyden–Fletcher–Goldfarb–Shannon (BFGS) approximation. Promising results are presented using both PCA and the folding development model in the wavelet domain. The cortical folding development model provides quantitative anatomical information regarding macroscopic cortical folding development and may be of potential use as a biomarker for early diagnosis of neurological deficits in newborns.

===Key Investigators===
* 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
* 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===
===Key Investigators===
===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===
===Key Investigators===
* Kitware - Will Schroeder (Core 2 PI), Sebastien Barre, Luis Ibanez, Bill Hoffman
* GE - Jim Miller, Xiaodong Tao
* Isomics - Steve Pieper

===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].
==Other Projects==
Any Project(s) not covered by the 8 sections above

==Highlights(Schroeder)==
===EM Segmenter or TBD===
===DTI progress or TBD===
===Outreach (Gollub)===

NAMIC outreach is a joint effort of Cores 4, 5 and 6. The various mechanisms by which we ensure that the tools developed by NAMIC are rapidly and successfully deployed to the widest possible extent within the scientific community are closely integrated. This begins with the immediate posting of all software tools, interim updates and associated documentation via the NAMIC and Slicer wiki pages (links). The concerted effort to provide a harmonious visualization and analysis platform (Slicer 3) that enables the integration of the software algorithms of all Core 1 laboratories drives the sequence of development of training materials. With the January 2008 release of Slicer 3 in beta format, we prepared the first of the Slicer 3 based Powerpoint tutorials that guide new users through the process of loading, interacting with and saving data in Slicer 3. Given the intense and successful effort at engineering this platform to facilitate the process of integrating new command-line modules of image analysis software into the platform, our second tutorial targeted software developers . The "Hello World" tutorial guides a programmer, step-by-step through the process of integrating a command line tool into Slicer 3. Both these tutorials are available via the web (link). These tutorials have been thoroughly tested by using them in large Workshops (see next) to ensure that they are robust across platform (Linux, Mac, PC) and can be used successfully by users across a wide range of training backgrounds.
 
In June of 2007 as a satellite event to the international Organization for Human Brain Mapping annual meeting in Chicago, IL we ran an 8 hour workshop on analysis of Diffusion Imaging Data (link); it was our final Slicer workshop based on the Slicer 2.7 release. The Workshop rapidly filled after posting, the 50 participants represented 9 countries from around the world, 14 states within the US and 40 different laboratories including 2 NIH institutes. The single "no-show" was due to a European flight cancellation. The attendees, with backgrounds in basic or clinical neurosciences, physics, image processing or computer science, ranging from full professors to new graduate students were very comfortable learning together. The feedback from the workshop attendees was uniformly positive with 100% reporting that they would recommend the workshop to others and 50% planning to apply the tools and information they learned to their own work.
 
In January 2008 we debuted the "Hello World" tutorial at the NAMIC AHM in Salt Lake City to an audience of our project members and collaborators. This very constructive presentation was used to make significant improvements in the presentation and delivery of this material. In February 2008 we debuted the users tutorial at a workshop hosted by the Surgical Planning Laboratory at BWH. Again, this presentation was used to make significant improvements in the presentation and delivery of the material. In April of 2008 we ran an all day workshop, hosted by UNC (get details right) for users and developers that incorporated both tutorials. This was attended by approximately 20 individuals coming from a wide range of backgrounds. Time was taken to ensure that all participants gained significant understanding of the new software, sufficient to ensure their successful use of it following the workshop.
 
This year saw the publication of a peer-reviewed manuscript that describes the NAMIC approach to outreach including our multi-disciplinary approach, our integration of theory into practice as driven by a clinical goal, and the translation of concepts into skills through interactive instructor led training sessions (Pujol S, Kikinis R, Gollub R: Lowering the barriers inherent in translating advances in neuroimage analysis to clinical research applications, Academic Radiology 15: 114-118, 2008, add link to Publication DB).
* Text here about Project Events 5 & 6 from Tina if not already included elsewhere.
* Text here about the MICCAI Open Source Workshop if not already included elsewhere (Steve?)
* Slicer IGT event December 2007 (tina?)
* Wiki to web
* Impact as measured by number of downloads of tutorial materials (help someone)
* Should the DTI tractography validation project be written up somewhere, if so where? I will do it if it isn't already assigned.

==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===
Within the center, NA-MIC has formed a community around its software
engineering tools, imaging platforms, algorithms, and clinical
workflows. The NA-MIC calendar includes the All Hands Meeting and
Winter Project Week, the Spring Algorithm Meeting, the Summer Project
Week, Slicer3 Mini-Retreats, Core Site Visits, Training Workshops, and weekly telephone
conferences.

The NA-MIC software engineering tools (CMake, Dart, CTest, CPack) have
enabled the development and distribution of a cross-platform, nightly
tested, end-user application, Slicer3, that is a complex union of
novel application code, visualization tools (VTK), imaging libraries
(ITK, TEEM), user interface libraries (Tk, KWWidgets), and scripting
languages (TCL, Python). The NA-MIC software engineering tools have been
essential in the development and distribution of the Slicer3 imaging
platform to the NA-MIC community.

NA-MIC's end-user application, Slicer3, supports the research within
NA-MIC by providing a base application for visualization and data
management. Slicer3 also supports the research within NA-MIC by
providing plugin mechanisms which allow researchers to quickly and
easily integrate and distribute their technology with Slicer3. Slicer3
is available to all center participants and the external community
through its source code repository, official binary releases, and
unofficial nightly binary snapshots.

NA-MIC drives the development of platforms and algorithms through the
needs and research of its DBPs. Each DBP has selected specific
workflows and roadmaps as focal points for development with a goal of
providing the community with complete end-to-end solutions using
NA-MIC tools. The community will be able to reproduce these workflows
and roadmaps in their own research programs.

NA-MIC algorithms are designed and used to address specific needs of
the DBPs. Multiple solution paths are explored and compared within
NA-MIC, resulting in recommendations to the field. The NA-MIC
algorithm groups collaborate and orchestrate the solutions to the
DBP workflows and roadmaps.

===Impact within NIH Funded Research===
Within NIH funded research, NA-MIC is the NCBC collaborating center for three R01's: "Automated FE Mesh Development", "Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI", and "An Integrated System for Image-Guided Radiofrequency Ablation of Liver Tumors". Several other proposals have been submitted and are under
evaluation for the "Collaborations with NCBC PAR". NA-MIC also
collaborates on the Slicer3 platform with the NIH funded Neuroimage
Analysis Center and the National Center for Image-Guided Therapy. The
NIH funded "BRAINS Morphology and Image Analysis" project is also
leveraging NA-MIC and Slicer3 technology. NA-MIC collaborates with the
NIH funded Neuroimaging Informatics Tools and Resources Clearinghouse
on distribution of Slicer3 plugin modules.

===National and International Impact===
NA-MIC events and tools garner national and international interest.
Over 100 researchers participated in the NA-MIC All Hands Meeting and
Winter Project Week in January 2008. Many of these participants were
from outside of NA-MIC, attending the meetings to gain access to the
NA-MIC tools and researchers. These external researchers are
contributing ideas and technology back into NA-MIC. In fact, a
breakout session at the Winter Project Week on "Geometry and Topology
Processing of Meshes" was organized by four researchers from outside
of NA-MIC.

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

NA-MIC science is recognized by the medical imaging community. Over
100 NA-MIC related publications are listed on PubMed. Many of these
publications are in the most prestigious journals and conferences in the
field. Portions of the DBP workflows and roadmaps are already being
utilized by researchers in the broader community and in the
development of commercial products.

NA-MIC sponsored several events to promote NA-MIC tools and
methodologies. NA-MIC co-sponsored the "Third Annual Open Source
Workshop" at the Medical Image Computing and Computer-Assisted
Intervention (MICCAI) 2007 conference. The proceedings of the
workshop are published on the electronic Insight Journal, another
NIH-funded activity. NA-MIC sponsored three training workshops on
NA-MIC tools for the Biocomputing community in this fiscal year and
plans to hold sessions at upcoming MICCAI and RSNA conferences.

==NA-MIC Timeline (Whitaker)==

==Appendix A Publications (Kapur)==
These 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.

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

2008 Annual Scientific Report

2008-05-14T16:47:38Z

Hazlett: /* Overview (Hazlett) */

Back to [[2008_Progress_Report]]

=Guidelines for preparation=

*[[2008_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 2007 report, which is provided here for reference: [[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 in its fourth year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The first driving biological projects (DBPs) three years for Center were inspired by schizophrenia research. In the fourth year new DBPs have been added. Three are centered around diseases of the brain: (a) brain lesion analysis in neuropschiatric systemic lupus erythematosus; (b) a study of cortical thickness for autism; and (c) stochastic tractography for VCFS. In an very new direction, we have added DBP on the prostate: brachytherapy needle positioning robot integration.

We briefly summarize the work of NAMIC during the four years of its existence. In the year one of the Center, alliances were forged amongst the cores and constituent groups in order to integrate the efforts of the cores and to define the kinds of tools needed for specific imaging applications. The second year emphasized the identification of the key research thrusts that cut across cores and were driven by the needs and requirements of the DBPs. This led to the formulation of the Center's four main 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 the collaborative efforts in order to give solutions to the various brain-oriented DBPs.

Year four has seen progress with the work of our new DBPs. As alluded to above these include work on neuropsychiatric disorders such as Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), as well as the prostate interventional work (Johns Hopkins and Queens Universities). We already have a number of publications as is indicated on our publications page, and software development is continuing as well.

In the next section (Section 3), we summarize this year’s progress on the four roadmap projects listed above: Section 3.1 stochastic tractography for Velocardiofacial Syndrome, Section 3.2 brachytherapy needle positioning for the prostate, Section 3.3 brain lesion analysis in neuropschiatric systemic lupus erythematosus, and Section 3.4 cortical thickness for autism. Next in Section 4, we describe recent 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). In Section 4.5, we outline some of the other key projects, in Section 4.6 some key highlights including the integration of the EM Segmentor into Slicer, and in Section 4.7 the impact of biocomputing at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 4.8, provides a timeline of Center activities.

=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 would be usefull in evaluating anatomical connectivity between segmented cortical regions of the brain. The ultimate goal of our program is to understand anatomical connectivity similarities and differences between genetically related schizophrenia and velocardio-fatial syndrome. Thus we plan to use the "stochastic tractography" tool for the analysis of abnormalities in integrity, or connectivity, provided by arcuate fasciculus, fiber bundle involved in language processing, in schizophrenia and VCFS.

===Algorithm Component (Golland)===
Over the last year, algorythm has been tested first on the already available to NAMIC dataset of schizophrenia subjects acquired on 1.5T. This step allowed us to optimize algorythm to our dataset, as well as to develop the pipeline for data analysis that would be then easilly transferable to other datasets, and structures. For this, as well as other applications, we use gray matter labels derived from either automatic or manual segmentation of structural MRI. Thus the first step was to obtain registration between diffusion and anatomical data. Right now, demon's registration alogythm that is part of slicer is being used, but we are also testing b-spline registration, as well as fluid? registration for this purpose. Next step, also accomplished this last year, was to apply the alogythm to new, higher resolution NAMIC dataset, and study smaller white matter connections including cingulum bundle, arcuate fasciculus, uncinate fasciculus and internal capsule. This step was accomplished and data presented at the Santa Fee meeting in October 2007. Algorythm was also additionally tested on the phantom, where differences in coordinate systems were debugged. Upon the completion of testing phase, we started analysis of arcuate fasciculus, language related fiber bundle, in new 3T, high resolution dataset. Additional step turned out to be required in preprocessing, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high, thus white matter sesgmentation was performed using free surfer, and co-registered with DTI. Our current work focuses on better registration alogythms, as well as the way to parametrize tracts, in order to obtain FA measurements along the tracts.

===Engineering Component (Davis)===
Stochastic Tractography slicer module has been finished, and presented at the AHM in SLC. Its now part of the slicer2.8 and slicer3. Module documentation have been also created.
Current engeneering effords are concentrated on maintaining the module, optimizing it for working with other data formats, and adding new functionality, such as better registration, distortion correction and ways of extracting and measuring FA along the tracts.

===Clinical Component (Kubicki)===
Over the last year, algorythm has been tested first on the already available to NAMIC dataset of schizophrenia subjects acquired on 1.5T. Anterior Limb of the internal capsule, large structure connecting thalamus with frontal lobe, have been extracvted, and analyzed in group of 20 schizphrenics, and 20 control subjects, and results showing group differences in Fractional Anisotropy presented at the ACNP symposium in December 2007. Next, stochastic tractography was tested, and optimized for new, high resolution DTI dataset acquired on 3T GE magnet. Upon the completion of testing phase, we started analysis of arcuate fasciculus, language related fiber bundle, in 20 controls and 20 chronic schizphrenics. Whole brain as well as white matter segmentations, using freesurfer, as well as automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Supperior Temporal Gyri), as well as another ROI that would guide the tract ("waypoint" ROI) have been generated for all subjects. Finally, paths of interest were generated, and averaged FA extracted for each tract. Preliminary data based on 7 patients and 12 controls were presented at the AHM in January 2008, study is currently under way.

===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: Brachytherapy Needle Positioning Robot Integration (Fichtinger)==
===Overview (Fichtinger)===
===Algorithm Component (Tannenbaum)===
We have worked on both the segmentation and the registration of the prostate from MRI and ultrasound data. We explain each of the steps now.

====Prostate Segmentation====

We first must extract the prostate. We have considered three possible methods: a combination of a combination of Cellular Automata(CA also known as Grow Cut) with Geometric Active Contour(GAC) methods; employing an ellipsoid to match the prostate in 3D image; shape based approach using spherical wavelets. More details are given below and images and further details may be found at [[Projects:ProstateSegmentation|GaTech Algorithm Prostate Segmentation]].

1. A cellular automata algorithm is used to give an initial segmentation. It begins with a rough manual initialization and then iteratively classifies all pixels into object and bacground until convergence. It effectively overcomes the problems of weak boundaries and inhomogeneity within the object or background. This in turn is fed into Geometric Active Contour for finer tuning. We are initially using the edge-based minimal surface pproach (the generalization of the standard Geodesic Active Contour model) which seems to give very reasonable results. Both steps of the algorithm algorithm are implemented in 3D. A ITK-Cellular Automata filter, dealing with N-D data, has already been completed and submitted to the NA-MIC SandBox.

2. Spherical wavelets have proven to be a very natural way of representing 3D shapes which are compact and simply connected (topological spheres). We developed a segmentation framework using this 3D wavelet representation and multiscale prior. The parameters of our model are the learned shape parameters based on the spherical wavelet coefficients}, as well as pose parameters that accommodate for shape variability due to a similarity transformation (rotation, scale, translation) which is not explicitly modeled with the shape parameters. The transformed surface based on the pose parameters. We used a region-based energy to drive the evolution of the parametric deformable surface for segmentation. Our segmentation algorithm deforms an initial surface according to the gradient flow that minimizes the energy functional in terms of the pose and shape parameters. Additionally, the optimization method can be applied in a coarse to fine manner. Spherical wavelets and conformal mappings are
already part of the NA-MIC SandBox.

3. The third method is very closely related to the second. It is based on the observation that the prostate may be roughly modelled as an ellipsoid. One can then employing this ellipsoid model coupled with a local/global segmentation energy approach which we have developed this year, as the basis of a segmentation procedure. Because of the local/global nature of the functional and the implicit introduction of scale this methodology may be very useful for MRI prostate data.

====Prostate Registration====

The registration and segmentation elements of our algorithm are difficult to separate. Thus for the 3D shape-driven segmentation part, the shapes must first be aligned through a conformal and area-correction alignment process. The prostate presents a number of difficulties for traditional approaches since there are no easily discernable landmarks. On the other hand, we observed that the surface of the prostate is almost half convex and half concave. The concave region may be captured and used to register the shapes, thus we register the whole shape by registering a certain region on it. Such concave region is characterized by its negative mean curvature. We treat the mean curvature as a scalar field defined on the surface, and we have extended the Chan-Vese method (in which one wants to separate the means with respect to the regions defined by the interior and exterior of the evolving active contour) to the case at hand on the prostate surface. The method is implemented in C++ and it successfully extracts the concave surface region. This method could also be used to exact regions on surface according to any feature charactered by a scalar field defined on the surface.

In order incorporate the extracted region as landmarks into the registration process, instead of matching two binary images directly, we transform the binary images into a form to highlight the boundary region. This is done by applying a Gauss function on the (narrow band) of the signed distance function of the binary image. The transformed image enjoys the advantages of both the parametric and implicit representations of shapes. Namely it has compact description, as the parametric representation does, and as in the implicit representation it avoids the correspondence problem. Moreover we incorporate the extracted concave regions into such images for registration which leads to a better result.

Finally, in the past year we have developed a particle filtering approach for the general problem of registering two point sets that differ by a rigid body transformation which may be very useful for this project. Typically, registration algorithms compute the transformation parameters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem, in which the corresponding distribution can naturally be estimated using a particle filter. We treat motion as a local variation in pose parameters obtained from running several iterations of the standard Iterative Closest Point (ICP) algorithm. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence often found in local optimizer functions used to tackle the registration task. In contrast with other techniques, this approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temporal 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.

===Engineering Component (Hayes)===
===Clinical Component (Fichtinger)===
===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)===
===Algorithm Component (Whitaker)===
===Engineering Component (Pieper)===
===Clinical Component (Bockholt)===
===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.
Tissue segmentation: We have successfully adapted the UNC segmentation tool called itkEMS to Slicer, which we have for segmentations of the young brain. We also created a young brain atlas for the current Slicer3 EM Segment module. Tests have been successful and a comparative study to itkEMS has shown that further parameter optimization is needed to reach the same quality.

====Cortical thickness measurement====
The UNC algorithm for the measurement of local cortical thickness given a labeling of white matter and gray matter has been developed into a Slicer3 external module. This module lends itself well for regional analysis of cortical thickness, but less so for local analysis due to its non-symmetric and sparse measurements. Ongoing development is focusing on a symmetric, Laplacian based cortical thickness suitable for local analysis.

====Cortical correspondence (regional)====

For regional correspondence, an existing lobar parcellation atlas is deformably registered using a b-spline registration tool. First tests have been very promising and the release of the corresponding Slicer 3 registration module is schedule to be finished within the next month and thus the regional analysis workflow will be available at that time.

====Cortical correspondence (local)====
Local cortical correspondence requires a two-step process of white/gray surface inflation followed by group-wise correspondence computation. White matter surface extraction and inflation is currently achieved with an external tool and developing a Slicer 3 based solution is a goal in the next year. The group-wise correspondence step has been fully solved, and a Slicer 3 module is already available. Evaluation on real data has shown that our method outperforms the currently widely employed Freesurfer framework.

====Statistical analysis/Hypothesis testing====
Regional analysis can be done with standard statistical tools such as MANOVA as there are a limited, relatively small number of regions. Local analysis on the other hand needs local non-parametric testing, multiple-comparison correction, and correlative analysis that is not routinely available. We are currently extending the current Slicer 3 module designed for statistical shape analysis to be used for this purpose incorporating a local applied General Linear Module and MANCOVA based testing framework.

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

Several of the algorithms for this Clinical Roadmap project were already in software tools utilizing ITK. These tools have been refactored to be NA-MIC compatible and repackaged as Slicer3 plugins. Slicer3 has been extended to support this Clinical Roadmap by adding transforms as a parameter type that can be passed to and returned by plugins. Slicer3 registration and resampling modules have been refactored to produce and accept transforms as parameters. Slicer3 has also been extended to support nonlinear transformation types (B-Spline and deformation fields) in its data model.

===Clinical Component (Hazlett)===
===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)==
===Progress===
===Key Investigators===
===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 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.

Structural Segmentation

* Directional Based Segmentation
We have proposed a directional segmentation framework for Direction-weighted Magnetic Resonance imagery by augmenting the Geodesic Active Contour framework with directional information. The classical scalar conformal factor is replaced by a factor that incorporates directionality. We mathematically showed that the optimization problem is well-defined when the factor is a Finsler metric. The calculus of variations or dynamic programming may be used to find the optimal curves. This past year we have applied this methodology in extracting the anchor tract (or centerline) of neural fiber bundles. Further we have applied this in conjunction with the Bayes’ rule into volumetric segmentation for extracting the entire fiber bundles. We have also proposed a novel shape prior in the volumetric segmentation to extract tubular fiber bundles.

* Stochastic Segmentation

We have continued work this year on developing new stochastic methods for implementing curvature-driven flows for medical tasks like segmentation. We can now generalize our results to an arbitrary Riemannian surface which includes the geodesic active contours as a special case. We are also implementing the directional flows based on the anisotropic conformal factor described above using this stochastic methodology. Our stochastic snakes’ models are based on the theory of interacting particle systems. This brings together the theories of curve evolution and hydrodynamic limits, and as such impacts our growing use of joint methods from probability and partial differential in image processing and computer vision. We now have working code written in C++ for the two dimensional case and have worked out the stochastic model of the general geodesic active contour model.

* Statistical PDE Methods for Segmentation

Our objective is to add various statistical measures into our PDE flows for medical imaging. This will allow the incorporation of global image information into the locally defined PDE framework. This year, we developed flows which can separate the distributions inside and outside the evolving contour, and we have also been including shape information in the flows. We have completed a statistically based flow for segmentation using fast marching, and the code has been integrated into Slicer.

* Atlas Renormalization for Improved Brain MR Image Segmentation

Atlas-based approaches can automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. However, the accuracy often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this project, we work to 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 shows that the new procedure improves 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.

*Multiscale Shape Segmentation Techniques

The goal of this project is to represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. Our technique defines a multiscale parametric model of surfaces belonging to the same population using a compact set of spherical wavelets targeted to that population. We derived a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior for segmentation. Additionally, the optimization method can be applied in a coarse-to-fine manner. We applied our algorithm to the caudate nucleus, a brain structure of interest in the study of schizophrenia. Our validation shows that our algorithm is computationally efficient and outperforms the Active Shape Model (ASM) algorithm, by capturing finer shape details.

Registration

* Optimal Mass Transport Registration
The aim of this project is to provide a computationally 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 gradient flow PDE approach using multi-resolution and multi-grid techniques to speed up the convergence. We also leverage the computational power of general purpose graphics processing units available on standard desktop computing machines to exploit the inherent parallelism in our algorithm. We have implemented 2D and 3D multi-resolution registration using Optimal Mass Transport and are currently working on the registration of 3D datasets.

* Diffusion Tensor Image Processing Tools

We aim to provide methods for computing geodesics and distances between diffusion tensors. One goal is to provide hypothesis testing for differences between groups. This will involve interpolation techniques for diffusion tensors as weighted averages in the metric framework. We will also provide filtering and eddy current correction. This year, we developed a Slicer module for DT-MRI Rician noise removal, developed prototypes of DTI geometry and statistical packages, and began work on a general method for hypothesis testing between diffusion tensor groups.

* Point Set Rigid Registration

We propose a particle filtering scheme for the registration of 2D and 3D point set undergoing a rigid body transformation where we incorporate stochastic dynamics to model the uncertainty of the registration process. Typically, registration algorithms compute the transformations parameters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem, in which the corresponding distribution can naturally be estimated using a particle filter. In this work, we treat motion as a local variation in the pose parameters obtained 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 uncertainty. In contrast with other techniques, our approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temporal 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.

* Cortical Correspondence using Particle System

In this project, we want to compute cortical correspondence on populations, using various features such as cortical structure, DTI connectivity, vascular structure, and functional data (fMRI). This presents a challenge because of the highly convoluted surface of the cortex, as well as because of the different properties of the data features we want to incorporate together. We would like to use a particle based entropy minimizing system for the correspondence computation, in a population-based manner. This is advantageous because it does not require a spherical parameterization of the surface, and does not require the surface to be of spherical topology. It would also eventually enable correspondence computation on the subcortical structures and on the cortical surface using the same framework. To circumvent the disadvantage that particles are assumed to lie on local tangent planes, we plan to first ‘inflate’ the cortex surface. Currently, we are at testing stage using structural data, namely, point locations and sulcal depth (as computed by FreeSurfer).

* Multimodal Atlas

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called iCluster for Image Clustering, is based on the following idea: given the templates, the co-registration problem becomes simple, reducing to a number of pairwise registration instances. On the other hand, given a collection of images that have been co-registered, an off-the shelf clustering or averaging algorithm can be used to compute the templates. The algorithm assumed a fixed and known number of template images. We formulate the problem as a maximum likelihood solution and employ a Generalized Maximum Likelihood algorithm to solve it. In the E-step, we compute membership probabilities. In the M-step, we update the template images as weighted averages of the images, where weights are the memberships and the template priors are updated, and then perform a collection of independent pairwise registration instances. The algorithm is currently implemented in the Insight ToolKit (ITK) and we next plan to integrate it into Slicer.

* Groupwise Registration

We aim at providing efficient groupwise registration algorithms for population analysis of anatomical structures. Here we extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment. Our results indicate that increasing the complexity of the deformation model improves registration accuracy significantly, especially at cortical regions.

Shape Analysis

* Shape Analysis Framework Using SPHARM-PDM

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described by sampled spherical harmonics SPHARM-PDM. The input of the proposed shape analysis is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. Group tests can be visualized by P-values and by mean difference magnitude and vector maps, as well as maps of the group covariance information. The implementation has reached a stable framework and has been disseminated to several collaborating labs within NAMIC (BWH, Georgia Tech, Utah). The current development focuses on integrating the current command line tools into the Slicer (v3) via the Slicer execution model. The whole shape analysis pipeline is encapsulated and accessible to the trained clinical collaborator. The current toolset distribution (via NeuroLib) now also contains open data for other researchers to evaluate their shape analysis enhancements.

* Multiscale Shape Analysis

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. As an application, we analyze two brain structures, the caudate nucleus and the hippocampus. We show that the results nicely complement the results obtained with shape analysis using a sampled point representation (SPHARM-PDM). We used the UNC pipeline to pre-process the images, and for each triangulated SPHARM-PDM surface, a spherical wavelet description is computed. We then use the UNC statistical toolbox to analyze differences between two groups of surfaces described by the features of choice that is the 3D spherical wavelet coefficients. This year, we conducted statistical shape analysis of the two brain structures and compared the results obtained to shape analysis using a SPHARM-PDM representation.

* Population Analysis of Anatomical Variability

In contrast to shape-based segmentation that utilizes a statistical model of the shape variability in one population (typically based on Principal Component Analysis), we are interested in identifying and characterizing differences between two sets of shape examples. We use the discriminative framework to characterize the differences in shape by training a classifier function and studying its sensitivity to small perturbations in the input data. An additional benefit is that the resulting classifier function can be used to label new examples into one of the two populations, e.g., for early detection in population screening or prediction in longitudinal studies. We have implemented stand alone code for training a classifier, jackknifing and permutation testing, and are currently porting the software into ITK. We have also started exploring alternative, surface-based descriptors which are promising in improving our ability to detect and characterize subtle differences in the shape of anatomical structures due to diseases such as schizophrenia.

* Shape Analysis with Overcomplete Wavelets

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development and show significantly consistent results as well as improved sensitivity compared with the previously used bi-orthogonal spherical wavelet. In particular, we are able to detect developmental asymmetry in the left and right hemispheres.

*Shape based Segmentation and Registration

When there is little or no contrast along boundaries of different regions, standard image segmentation algorithms perform poorly and segmentation is done manually using prior knowledge of shape and relative location of underlying structures. We have proposed 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. Our method filters out the noise as compared to thresholding using initial likelihoods, and it captures multiple structures as in the brain (where both major brain compartments and subcortical structures are obtained) because it naturally evolves families of curves. The algorithm is currently implemented in 3D Slicer Version 2.6 and a beta version is available in 3D Slicer Version 3.

*Spherical Wavelets

In this project, we apply a spherical wavelet transformation to extract shape features of cortical surfaces reconstructed from magnetic resonance images (MRI) of a set of subjects. The spherical wavelet transformation can characterize the underlying functions in a local fashion in both space and frequency, in contrast to spherical harmonics that have a global basis set. We perform principal component analysis (PCA) on these wavelet shape features to study patterns of shape variation within normal population from coarse to fine resolution. In addition, we study the development of cortical folding in newborns using the Gompertz model in the wavelet domain, allowing us to characterize the order of development of large-scale and finer folding patterns independently. We develop an efficient method to estimate the regularized Gompertz model based on the Broyden–Fletcher–Goldfarb–Shannon (BFGS) approximation. Promising results are presented using both PCA and the folding development model in the wavelet domain. The cortical folding development model provides quantitative anatomical information regarding macroscopic cortical folding development and may be of potential use as a biomarker for early diagnosis of neurological deficits in newborns.

===Key Investigators===
* 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
* 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===
===Key Investigators===
===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===
===Key Investigators===
* Kitware - Will Schroeder (Core 2 PI), Sebastien Barre, Luis Ibanez, Bill Hoffman
* GE - Jim Miller, Xiaodong Tao
* Isomics - Steve Pieper

===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].
==Other Projects==
Any Project(s) not covered by the 8 sections above

==Highlights(Schroeder)==
===EM Segmenter or TBD===
===DTI progress or TBD===
===Outreach (Gollub)===

NAMIC outreach is a joint effort of Cores 4, 5 and 6. The various mechanisms by which we ensure that the tools developed by NAMIC are rapidly and successfully deployed to the widest possible extent within the scientific community are closely integrated. This begins with the immediate posting of all software tools, interim updates and associated documentation via the NAMIC and Slicer wiki pages (links). The concerted effort to provide a harmonious visualization and analysis platform (Slicer 3) that enables the integration of the software algorithms of all Core 1 laboratories drives the sequence of development of training materials. With the January 2008 release of Slicer 3 in beta format, we prepared the first of the Slicer 3 based Powerpoint tutorials that guide new users through the process of loading, interacting with and saving data in Slicer 3. Given the intense and successful effort at engineering this platform to facilitate the process of integrating new command-line modules of image analysis software into the platform, our second tutorial targeted software developers . The "Hello World" tutorial guides a programmer, step-by-step through the process of integrating a command line tool into Slicer 3. Both these tutorials are available via the web (link). These tutorials have been thoroughly tested by using them in large Workshops (see next) to ensure that they are robust across platform (Linux, Mac, PC) and can be used successfully by users across a wide range of training backgrounds.
 
In June of 2007 as a satellite event to the international Organization for Human Brain Mapping annual meeting in Chicago, IL we ran an 8 hour workshop on analysis of Diffusion Imaging Data (link); it was our final Slicer workshop based on the Slicer 2.7 release. The Workshop rapidly filled after posting, the 50 participants represented 9 countries from around the world, 14 states within the US and 40 different laboratories including 2 NIH institutes. The single "no-show" was due to a European flight cancellation. The attendees, with backgrounds in basic or clinical neurosciences, physics, image processing or computer science, ranging from full professors to new graduate students were very comfortable learning together. The feedback from the workshop attendees was uniformly positive with 100% reporting that they would recommend the workshop to others and 50% planning to apply the tools and information they learned to their own work.
 
In January 2008 we debuted the "Hello World" tutorial at the NAMIC AHM in Salt Lake City to an audience of our project members and collaborators. This very constructive presentation was used to make significant improvements in the presentation and delivery of this material. In February 2008 we debuted the users tutorial at a workshop hosted by the Surgical Planning Laboratory at BWH. Again, this presentation was used to make significant improvements in the presentation and delivery of the material. In April of 2008 we ran an all day workshop, hosted by UNC (get details right) for users and developers that incorporated both tutorials. This was attended by approximately 20 individuals coming from a wide range of backgrounds. Time was taken to ensure that all participants gained significant understanding of the new software, sufficient to ensure their successful use of it following the workshop.
 
This year saw the publication of a peer-reviewed manuscript that describes the NAMIC approach to outreach including our multi-disciplinary approach, our integration of theory into practice as driven by a clinical goal, and the translation of concepts into skills through interactive instructor led training sessions (Pujol S, Kikinis R, Gollub R: Lowering the barriers inherent in translating advances in neuroimage analysis to clinical research applications, Academic Radiology 15: 114-118, 2008, add link to Publication DB).
* Text here about Project Events 5 & 6 from Tina if not already included elsewhere.
* Text here about the MICCAI Open Source Workshop if not already included elsewhere (Steve?)
* Slicer IGT event December 2007 (tina?)
* Wiki to web
* Impact as measured by number of downloads of tutorial materials (help someone)
* Should the DTI tractography validation project be written up somewhere, if so where? I will do it if it isn't already assigned.

==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===
Within the center, NA-MIC has formed a community around its software
engineering tools, imaging platforms, algorithms, and clinical
workflows. The NA-MIC calendar includes the All Hands Meeting and
Winter Project Week, the Spring Algorithm Meeting, the Summer Project
Week, Slicer3 Mini-Retreats, Core Site Visits, Training Workshops, and weekly telephone
conferences.

The NA-MIC software engineering tools (CMake, Dart, CTest, CPack) have
enabled the development and distribution of a cross-platform, nightly
tested, end-user application, Slicer3, that is a complex union of
novel application code, visualization tools (VTK), imaging libraries
(ITK, TEEM), user interface libraries (Tk, KWWidgets), and scripting
languages (TCL, Python). The NA-MIC software engineering tools have been
essential in the development and distribution of the Slicer3 imaging
platform to the NA-MIC community.

NA-MIC's end-user application, Slicer3, supports the research within
NA-MIC by providing a base application for visualization and data
management. Slicer3 also supports the research within NA-MIC by
providing plugin mechanisms which allow researchers to quickly and
easily integrate and distribute their technology with Slicer3. Slicer3
is available to all center participants and the external community
through its source code repository, official binary releases, and
unofficial nightly binary snapshots.

NA-MIC drives the development of platforms and algorithms through the
needs and research of its DBPs. Each DBP has selected specific
workflows and roadmaps as focal points for development with a goal of
providing the community with complete end-to-end solutions using
NA-MIC tools. The community will be able to reproduce these workflows
and roadmaps in their own research programs.

NA-MIC algorithms are designed and used to address specific needs of
the DBPs. Multiple solution paths are explored and compared within
NA-MIC, resulting in recommendations to the field. The NA-MIC
algorithm groups collaborate and orchestrate the solutions to the
DBP workflows and roadmaps.

===Impact within NIH Funded Research===
Within NIH funded research, NA-MIC is the NCBC collaborating center for three R01's: "Automated FE Mesh Development", "Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI", and "An Integrated System for Image-Guided Radiofrequency Ablation of Liver Tumors". Several other proposals have been submitted and are under
evaluation for the "Collaborations with NCBC PAR". NA-MIC also
collaborates on the Slicer3 platform with the NIH funded Neuroimage
Analysis Center and the National Center for Image-Guided Therapy. The
NIH funded "BRAINS Morphology and Image Analysis" project is also
leveraging NA-MIC and Slicer3 technology. NA-MIC collaborates with the
NIH funded Neuroimaging Informatics Tools and Resources Clearinghouse
on distribution of Slicer3 plugin modules.

===National and International Impact===
NA-MIC events and tools garner national and international interest.
Over 100 researchers participated in the NA-MIC All Hands Meeting and
Winter Project Week in January 2008. Many of these participants were
from outside of NA-MIC, attending the meetings to gain access to the
NA-MIC tools and researchers. These external researchers are
contributing ideas and technology back into NA-MIC. In fact, a
breakout session at the Winter Project Week on "Geometry and Topology
Processing of Meshes" was organized by four researchers from outside
of NA-MIC.

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

NA-MIC science is recognized by the medical imaging community. Over
100 NA-MIC related publications are listed on PubMed. Many of these
publications are in the most prestigious journals and conferences in the
field. Portions of the DBP workflows and roadmaps are already being
utilized by researchers in the broader community and in the
development of commercial products.

NA-MIC sponsored several events to promote NA-MIC tools and
methodologies. NA-MIC co-sponsored the "Third Annual Open Source
Workshop" at the Medical Image Computing and Computer-Assisted
Intervention (MICCAI) 2007 conference. The proceedings of the
workshop are published on the electronic Insight Journal, another
NIH-funded activity. NA-MIC sponsored three training workshops on
NA-MIC tools for the Biocomputing community in this fiscal year and
plans to hold sessions at upcoming MICCAI and RSNA conferences.

==NA-MIC Timeline (Whitaker)==

==Appendix A Publications (Kapur)==
These 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.

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

2008 Annual Scientific Report

2008-05-14T16:46:28Z

Hazlett: /* Overview (Hazlett) */

Back to [[2008_Progress_Report]]

=Guidelines for preparation=

*[[2008_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 2007 report, which is provided here for reference: [[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 in its fourth year. This Center is comprised of a multi-institutional, interdisciplinary team of computer scientists, software engineers, and medical investigators who have come together to develop and apply computational tools for the analysis and visualization of medical imaging data. A further purpose of the Center is to provide infrastructure and environmental support for the development of computational algorithms and open source technologies, and to oversee the training and dissemination of these tools to the medical research community. The first driving biological projects (DBPs) three years for Center were inspired by schizophrenia research. In the fourth year new DBPs have been added. Three are centered around diseases of the brain: (a) brain lesion analysis in neuropschiatric systemic lupus erythematosus; (b) a study of cortical thickness for autism; and (c) stochastic tractography for VCFS. In an very new direction, we have added DBP on the prostate: brachytherapy needle positioning robot integration.

We briefly summarize the work of NAMIC during the four years of its existence. In the year one of the Center, alliances were forged amongst the cores and constituent groups in order to integrate the efforts of the cores and to define the kinds of tools needed for specific imaging applications. The second year emphasized the identification of the key research thrusts that cut across cores and were driven by the needs and requirements of the DBPs. This led to the formulation of the Center's four main 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 the collaborative efforts in order to give solutions to the various brain-oriented DBPs.

Year four has seen progress with the work of our new DBPs. As alluded to above these include work on neuropsychiatric disorders such as Systemic Lupus Erythematosis (MIND Institute, University of New Mexico), Velocardiofacial Syndrome (Harvard), and Autism (University of North Carolina, Chapel Hill), as well as the prostate interventional work (Johns Hopkins and Queens Universities). We already have a number of publications as is indicated on our publications page, and software development is continuing as well.

In the next section (Section 3), we summarize this year’s progress on the four roadmap projects listed above: Section 3.1 stochastic tractography for Velocardiofacial Syndrome, Section 3.2 brachytherapy needle positioning for the prostate, Section 3.3 brain lesion analysis in neuropschiatric systemic lupus erythematosus, and Section 3.4 cortical thickness for autism. Next in Section 4, we describe recent 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). In Section 4.5, we outline some of the other key projects, in Section 4.6 some key highlights including the integration of the EM Segmentor into Slicer, and in Section 4.7 the impact of biocomputing at three different levels: within the center, within the NIH-funded research community, and externally to a national and international community. The final section of this report, Section 4.8, provides a timeline of Center activities.

=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 would be usefull in evaluating anatomical connectivity between segmented cortical regions of the brain. The ultimate goal of our program is to understand anatomical connectivity similarities and differences between genetically related schizophrenia and velocardio-fatial syndrome. Thus we plan to use the "stochastic tractography" tool for the analysis of abnormalities in integrity, or connectivity, provided by arcuate fasciculus, fiber bundle involved in language processing, in schizophrenia and VCFS.

===Algorithm Component (Golland)===
Over the last year, algorythm has been tested first on the already available to NAMIC dataset of schizophrenia subjects acquired on 1.5T. This step allowed us to optimize algorythm to our dataset, as well as to develop the pipeline for data analysis that would be then easilly transferable to other datasets, and structures. For this, as well as other applications, we use gray matter labels derived from either automatic or manual segmentation of structural MRI. Thus the first step was to obtain registration between diffusion and anatomical data. Right now, demon's registration alogythm that is part of slicer is being used, but we are also testing b-spline registration, as well as fluid? registration for this purpose. Next step, also accomplished this last year, was to apply the alogythm to new, higher resolution NAMIC dataset, and study smaller white matter connections including cingulum bundle, arcuate fasciculus, uncinate fasciculus and internal capsule. This step was accomplished and data presented at the Santa Fee meeting in October 2007. Algorythm was also additionally tested on the phantom, where differences in coordinate systems were debugged. Upon the completion of testing phase, we started analysis of arcuate fasciculus, language related fiber bundle, in new 3T, high resolution dataset. Additional step turned out to be required in preprocessing, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high, thus white matter sesgmentation was performed using free surfer, and co-registered with DTI. Our current work focuses on better registration alogythms, as well as the way to parametrize tracts, in order to obtain FA measurements along the tracts.

===Engineering Component (Davis)===
Stochastic Tractography slicer module has been finished, and presented at the AHM in SLC. Its now part of the slicer2.8 and slicer3. Module documentation have been also created.
Current engeneering effords are concentrated on maintaining the module, optimizing it for working with other data formats, and adding new functionality, such as better registration, distortion correction and ways of extracting and measuring FA along the tracts.

===Clinical Component (Kubicki)===
Over the last year, algorythm has been tested first on the already available to NAMIC dataset of schizophrenia subjects acquired on 1.5T. Anterior Limb of the internal capsule, large structure connecting thalamus with frontal lobe, have been extracvted, and analyzed in group of 20 schizphrenics, and 20 control subjects, and results showing group differences in Fractional Anisotropy presented at the ACNP symposium in December 2007. Next, stochastic tractography was tested, and optimized for new, high resolution DTI dataset acquired on 3T GE magnet. Upon the completion of testing phase, we started analysis of arcuate fasciculus, language related fiber bundle, in 20 controls and 20 chronic schizphrenics. Whole brain as well as white matter segmentations, using freesurfer, as well as automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Supperior Temporal Gyri), as well as another ROI that would guide the tract ("waypoint" ROI) have been generated for all subjects. Finally, paths of interest were generated, and averaged FA extracted for each tract. Preliminary data based on 7 patients and 12 controls were presented at the AHM in January 2008, study is currently under way.

===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: Brachytherapy Needle Positioning Robot Integration (Fichtinger)==
===Overview (Fichtinger)===
===Algorithm Component (Tannenbaum)===
We have worked on both the segmentation and the registration of the prostate from MRI and ultrasound data. We explain each of the steps now.

====Prostate Segmentation====

We first must extract the prostate. We have considered three possible methods: a combination of a combination of Cellular Automata(CA also known as Grow Cut) with Geometric Active Contour(GAC) methods; employing an ellipsoid to match the prostate in 3D image; shape based approach using spherical wavelets. More details are given below and images and further details may be found at [[Projects:ProstateSegmentation|GaTech Algorithm Prostate Segmentation]].

1. A cellular automata algorithm is used to give an initial segmentation. It begins with a rough manual initialization and then iteratively classifies all pixels into object and bacground until convergence. It effectively overcomes the problems of weak boundaries and inhomogeneity within the object or background. This in turn is fed into Geometric Active Contour for finer tuning. We are initially using the edge-based minimal surface pproach (the generalization of the standard Geodesic Active Contour model) which seems to give very reasonable results. Both steps of the algorithm algorithm are implemented in 3D. A ITK-Cellular Automata filter, dealing with N-D data, has already been completed and submitted to the NA-MIC SandBox.

2. Spherical wavelets have proven to be a very natural way of representing 3D shapes which are compact and simply connected (topological spheres). We developed a segmentation framework using this 3D wavelet representation and multiscale prior. The parameters of our model are the learned shape parameters based on the spherical wavelet coefficients}, as well as pose parameters that accommodate for shape variability due to a similarity transformation (rotation, scale, translation) which is not explicitly modeled with the shape parameters. The transformed surface based on the pose parameters. We used a region-based energy to drive the evolution of the parametric deformable surface for segmentation. Our segmentation algorithm deforms an initial surface according to the gradient flow that minimizes the energy functional in terms of the pose and shape parameters. Additionally, the optimization method can be applied in a coarse to fine manner. Spherical wavelets and conformal mappings are
already part of the NA-MIC SandBox.

3. The third method is very closely related to the second. It is based on the observation that the prostate may be roughly modelled as an ellipsoid. One can then employing this ellipsoid model coupled with a local/global segmentation energy approach which we have developed this year, as the basis of a segmentation procedure. Because of the local/global nature of the functional and the implicit introduction of scale this methodology may be very useful for MRI prostate data.

====Prostate Registration====

The registration and segmentation elements of our algorithm are difficult to separate. Thus for the 3D shape-driven segmentation part, the shapes must first be aligned through a conformal and area-correction alignment process. The prostate presents a number of difficulties for traditional approaches since there are no easily discernable landmarks. On the other hand, we observed that the surface of the prostate is almost half convex and half concave. The concave region may be captured and used to register the shapes, thus we register the whole shape by registering a certain region on it. Such concave region is characterized by its negative mean curvature. We treat the mean curvature as a scalar field defined on the surface, and we have extended the Chan-Vese method (in which one wants to separate the means with respect to the regions defined by the interior and exterior of the evolving active contour) to the case at hand on the prostate surface. The method is implemented in C++ and it successfully extracts the concave surface region. This method could also be used to exact regions on surface according to any feature charactered by a scalar field defined on the surface.

In order incorporate the extracted region as landmarks into the registration process, instead of matching two binary images directly, we transform the binary images into a form to highlight the boundary region. This is done by applying a Gauss function on the (narrow band) of the signed distance function of the binary image. The transformed image enjoys the advantages of both the parametric and implicit representations of shapes. Namely it has compact description, as the parametric representation does, and as in the implicit representation it avoids the correspondence problem. Moreover we incorporate the extracted concave regions into such images for registration which leads to a better result.

Finally, in the past year we have developed a particle filtering approach for the general problem of registering two point sets that differ by a rigid body transformation which may be very useful for this project. Typically, registration algorithms compute the transformation parameters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem, in which the corresponding distribution can naturally be estimated using a particle filter. We treat motion as a local variation in pose parameters obtained from running several iterations of the standard Iterative Closest Point (ICP) algorithm. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence often found in local optimizer functions used to tackle the registration task. In contrast with other techniques, this approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temporal 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.

===Engineering Component (Hayes)===
===Clinical Component (Fichtinger)===
===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)===
===Algorithm Component (Whitaker)===
===Engineering Component (Pieper)===
===Clinical Component (Bockholt)===
===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, but also examine longitudinal changes 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.
Tissue segmentation: We have successfully adapted the UNC segmentation tool called itkEMS to Slicer, which we have for segmentations of the young brain. We also created a young brain atlas for the current Slicer3 EM Segment module. Tests have been successful and a comparative study to itkEMS has shown that further parameter optimization is needed to reach the same quality.

====Cortical thickness measurement====
The UNC algorithm for the measurement of local cortical thickness given a labeling of white matter and gray matter has been developed into a Slicer3 external module. This module lends itself well for regional analysis of cortical thickness, but less so for local analysis due to its non-symmetric and sparse measurements. Ongoing development is focusing on a symmetric, Laplacian based cortical thickness suitable for local analysis.

====Cortical correspondence (regional)====

For regional correspondence, an existing lobar parcellation atlas is deformably registered using a b-spline registration tool. First tests have been very promising and the release of the corresponding Slicer 3 registration module is schedule to be finished within the next month and thus the regional analysis workflow will be available at that time.

====Cortical correspondence (local)====
Local cortical correspondence requires a two-step process of white/gray surface inflation followed by group-wise correspondence computation. White matter surface extraction and inflation is currently achieved with an external tool and developing a Slicer 3 based solution is a goal in the next year. The group-wise correspondence step has been fully solved, and a Slicer 3 module is already available. Evaluation on real data has shown that our method outperforms the currently widely employed Freesurfer framework.

====Statistical analysis/Hypothesis testing====
Regional analysis can be done with standard statistical tools such as MANOVA as there are a limited, relatively small number of regions. Local analysis on the other hand needs local non-parametric testing, multiple-comparison correction, and correlative analysis that is not routinely available. We are currently extending the current Slicer 3 module designed for statistical shape analysis to be used for this purpose incorporating a local applied General Linear Module and MANCOVA based testing framework.

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

Several of the algorithms for this Clinical Roadmap project were already in software tools utilizing ITK. These tools have been refactored to be NA-MIC compatible and repackaged as Slicer3 plugins. Slicer3 has been extended to support this Clinical Roadmap by adding transforms as a parameter type that can be passed to and returned by plugins. Slicer3 registration and resampling modules have been refactored to produce and accept transforms as parameters. Slicer3 has also been extended to support nonlinear transformation types (B-Spline and deformation fields) in its data model.

===Clinical Component (Hazlett)===
===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)==
===Progress===
===Key Investigators===
===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 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.

Structural Segmentation

* Directional Based Segmentation
We have proposed a directional segmentation framework for Direction-weighted Magnetic Resonance imagery by augmenting the Geodesic Active Contour framework with directional information. The classical scalar conformal factor is replaced by a factor that incorporates directionality. We mathematically showed that the optimization problem is well-defined when the factor is a Finsler metric. The calculus of variations or dynamic programming may be used to find the optimal curves. This past year we have applied this methodology in extracting the anchor tract (or centerline) of neural fiber bundles. Further we have applied this in conjunction with the Bayes’ rule into volumetric segmentation for extracting the entire fiber bundles. We have also proposed a novel shape prior in the volumetric segmentation to extract tubular fiber bundles.

* Stochastic Segmentation

We have continued work this year on developing new stochastic methods for implementing curvature-driven flows for medical tasks like segmentation. We can now generalize our results to an arbitrary Riemannian surface which includes the geodesic active contours as a special case. We are also implementing the directional flows based on the anisotropic conformal factor described above using this stochastic methodology. Our stochastic snakes’ models are based on the theory of interacting particle systems. This brings together the theories of curve evolution and hydrodynamic limits, and as such impacts our growing use of joint methods from probability and partial differential in image processing and computer vision. We now have working code written in C++ for the two dimensional case and have worked out the stochastic model of the general geodesic active contour model.

* Statistical PDE Methods for Segmentation

Our objective is to add various statistical measures into our PDE flows for medical imaging. This will allow the incorporation of global image information into the locally defined PDE framework. This year, we developed flows which can separate the distributions inside and outside the evolving contour, and we have also been including shape information in the flows. We have completed a statistically based flow for segmentation using fast marching, and the code has been integrated into Slicer.

* Atlas Renormalization for Improved Brain MR Image Segmentation

Atlas-based approaches can automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. However, the accuracy often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this project, we work to 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 shows that the new procedure improves 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.

*Multiscale Shape Segmentation Techniques

The goal of this project is to represent multiscale variations in a shape population in order to drive the segmentation of deep brain structures, such as the caudate nucleus or the hippocampus. Our technique defines a multiscale parametric model of surfaces belonging to the same population using a compact set of spherical wavelets targeted to that population. We derived a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior for segmentation. Additionally, the optimization method can be applied in a coarse-to-fine manner. We applied our algorithm to the caudate nucleus, a brain structure of interest in the study of schizophrenia. Our validation shows that our algorithm is computationally efficient and outperforms the Active Shape Model (ASM) algorithm, by capturing finer shape details.

Registration

* Optimal Mass Transport Registration
The aim of this project is to provide a computationally 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 gradient flow PDE approach using multi-resolution and multi-grid techniques to speed up the convergence. We also leverage the computational power of general purpose graphics processing units available on standard desktop computing machines to exploit the inherent parallelism in our algorithm. We have implemented 2D and 3D multi-resolution registration using Optimal Mass Transport and are currently working on the registration of 3D datasets.

* Diffusion Tensor Image Processing Tools

We aim to provide methods for computing geodesics and distances between diffusion tensors. One goal is to provide hypothesis testing for differences between groups. This will involve interpolation techniques for diffusion tensors as weighted averages in the metric framework. We will also provide filtering and eddy current correction. This year, we developed a Slicer module for DT-MRI Rician noise removal, developed prototypes of DTI geometry and statistical packages, and began work on a general method for hypothesis testing between diffusion tensor groups.

* Point Set Rigid Registration

We propose a particle filtering scheme for the registration of 2D and 3D point set undergoing a rigid body transformation where we incorporate stochastic dynamics to model the uncertainty of the registration process. Typically, registration algorithms compute the transformations parameters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem, in which the corresponding distribution can naturally be estimated using a particle filter. In this work, we treat motion as a local variation in the pose parameters obtained 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 uncertainty. In contrast with other techniques, our approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temporal 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.

* Cortical Correspondence using Particle System

In this project, we want to compute cortical correspondence on populations, using various features such as cortical structure, DTI connectivity, vascular structure, and functional data (fMRI). This presents a challenge because of the highly convoluted surface of the cortex, as well as because of the different properties of the data features we want to incorporate together. We would like to use a particle based entropy minimizing system for the correspondence computation, in a population-based manner. This is advantageous because it does not require a spherical parameterization of the surface, and does not require the surface to be of spherical topology. It would also eventually enable correspondence computation on the subcortical structures and on the cortical surface using the same framework. To circumvent the disadvantage that particles are assumed to lie on local tangent planes, we plan to first ‘inflate’ the cortex surface. Currently, we are at testing stage using structural data, namely, point locations and sulcal depth (as computed by FreeSurfer).

* Multimodal Atlas

In this work, we propose and investigate an algorithm that jointly co-registers a collection of images while computing multiple templates. The algorithm, called iCluster for Image Clustering, is based on the following idea: given the templates, the co-registration problem becomes simple, reducing to a number of pairwise registration instances. On the other hand, given a collection of images that have been co-registered, an off-the shelf clustering or averaging algorithm can be used to compute the templates. The algorithm assumed a fixed and known number of template images. We formulate the problem as a maximum likelihood solution and employ a Generalized Maximum Likelihood algorithm to solve it. In the E-step, we compute membership probabilities. In the M-step, we update the template images as weighted averages of the images, where weights are the memberships and the template priors are updated, and then perform a collection of independent pairwise registration instances. The algorithm is currently implemented in the Insight ToolKit (ITK) and we next plan to integrate it into Slicer.

* Groupwise Registration

We aim at providing efficient groupwise registration algorithms for population analysis of anatomical structures. Here we extend a previously demonstrated entropy based groupwise registration method to include a free-form deformation model based on B-splines. We provide an efficient implementation using stochastic gradient descents in a multi-resolution setting. We demonstrate the method in application to a set of 50 MRI brain scans and compare the results to a pairwise approach using segmentation labels to evaluate the quality of alignment. Our results indicate that increasing the complexity of the deformation model improves registration accuracy significantly, especially at cortical regions.

Shape Analysis

* Shape Analysis Framework Using SPHARM-PDM

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described by sampled spherical harmonics SPHARM-PDM. The input of the proposed shape analysis is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. Group tests can be visualized by P-values and by mean difference magnitude and vector maps, as well as maps of the group covariance information. The implementation has reached a stable framework and has been disseminated to several collaborating labs within NAMIC (BWH, Georgia Tech, Utah). The current development focuses on integrating the current command line tools into the Slicer (v3) via the Slicer execution model. The whole shape analysis pipeline is encapsulated and accessible to the trained clinical collaborator. The current toolset distribution (via NeuroLib) now also contains open data for other researchers to evaluate their shape analysis enhancements.

* Multiscale Shape Analysis

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. As an application, we analyze two brain structures, the caudate nucleus and the hippocampus. We show that the results nicely complement the results obtained with shape analysis using a sampled point representation (SPHARM-PDM). We used the UNC pipeline to pre-process the images, and for each triangulated SPHARM-PDM surface, a spherical wavelet description is computed. We then use the UNC statistical toolbox to analyze differences between two groups of surfaces described by the features of choice that is the 3D spherical wavelet coefficients. This year, we conducted statistical shape analysis of the two brain structures and compared the results obtained to shape analysis using a SPHARM-PDM representation.

* Population Analysis of Anatomical Variability

In contrast to shape-based segmentation that utilizes a statistical model of the shape variability in one population (typically based on Principal Component Analysis), we are interested in identifying and characterizing differences between two sets of shape examples. We use the discriminative framework to characterize the differences in shape by training a classifier function and studying its sensitivity to small perturbations in the input data. An additional benefit is that the resulting classifier function can be used to label new examples into one of the two populations, e.g., for early detection in population screening or prediction in longitudinal studies. We have implemented stand alone code for training a classifier, jackknifing and permutation testing, and are currently porting the software into ITK. We have also started exploring alternative, surface-based descriptors which are promising in improving our ability to detect and characterize subtle differences in the shape of anatomical structures due to diseases such as schizophrenia.

* Shape Analysis with Overcomplete Wavelets

In this work, we extend the Euclidean wavelets to the sphere. The resulting over-complete spherical wavelets are invariant to the rotation of the spherical image parameterization. We apply the over-complete spherical wavelet to cortical folding development and show significantly consistent results as well as improved sensitivity compared with the previously used bi-orthogonal spherical wavelet. In particular, we are able to detect developmental asymmetry in the left and right hemispheres.

*Shape based Segmentation and Registration

When there is little or no contrast along boundaries of different regions, standard image segmentation algorithms perform poorly and segmentation is done manually using prior knowledge of shape and relative location of underlying structures. We have proposed 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. Our method filters out the noise as compared to thresholding using initial likelihoods, and it captures multiple structures as in the brain (where both major brain compartments and subcortical structures are obtained) because it naturally evolves families of curves. The algorithm is currently implemented in 3D Slicer Version 2.6 and a beta version is available in 3D Slicer Version 3.

*Spherical Wavelets

In this project, we apply a spherical wavelet transformation to extract shape features of cortical surfaces reconstructed from magnetic resonance images (MRI) of a set of subjects. The spherical wavelet transformation can characterize the underlying functions in a local fashion in both space and frequency, in contrast to spherical harmonics that have a global basis set. We perform principal component analysis (PCA) on these wavelet shape features to study patterns of shape variation within normal population from coarse to fine resolution. In addition, we study the development of cortical folding in newborns using the Gompertz model in the wavelet domain, allowing us to characterize the order of development of large-scale and finer folding patterns independently. We develop an efficient method to estimate the regularized Gompertz model based on the Broyden–Fletcher–Goldfarb–Shannon (BFGS) approximation. Promising results are presented using both PCA and the folding development model in the wavelet domain. The cortical folding development model provides quantitative anatomical information regarding macroscopic cortical folding development and may be of potential use as a biomarker for early diagnosis of neurological deficits in newborns.

===Key Investigators===
* 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
* 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===
===Key Investigators===
===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===
===Key Investigators===
* Kitware - Will Schroeder (Core 2 PI), Sebastien Barre, Luis Ibanez, Bill Hoffman
* GE - Jim Miller, Xiaodong Tao
* Isomics - Steve Pieper

===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].
==Other Projects==
Any Project(s) not covered by the 8 sections above

==Highlights(Schroeder)==
===EM Segmenter or TBD===
===DTI progress or TBD===
===Outreach (Gollub)===

NAMIC outreach is a joint effort of Cores 4, 5 and 6. The various mechanisms by which we ensure that the tools developed by NAMIC are rapidly and successfully deployed to the widest possible extent within the scientific community are closely integrated. This begins with the immediate posting of all software tools, interim updates and associated documentation via the NAMIC and Slicer wiki pages (links). The concerted effort to provide a harmonious visualization and analysis platform (Slicer 3) that enables the integration of the software algorithms of all Core 1 laboratories drives the sequence of development of training materials. With the January 2008 release of Slicer 3 in beta format, we prepared the first of the Slicer 3 based Powerpoint tutorials that guide new users through the process of loading, interacting with and saving data in Slicer 3. Given the intense and successful effort at engineering this platform to facilitate the process of integrating new command-line modules of image analysis software into the platform, our second tutorial targeted software developers . The "Hello World" tutorial guides a programmer, step-by-step through the process of integrating a command line tool into Slicer 3. Both these tutorials are available via the web (link). These tutorials have been thoroughly tested by using them in large Workshops (see next) to ensure that they are robust across platform (Linux, Mac, PC) and can be used successfully by users across a wide range of training backgrounds.
 
In June of 2007 as a satellite event to the international Organization for Human Brain Mapping annual meeting in Chicago, IL we ran an 8 hour workshop on analysis of Diffusion Imaging Data (link); it was our final Slicer workshop based on the Slicer 2.7 release. The Workshop rapidly filled after posting, the 50 participants represented 9 countries from around the world, 14 states within the US and 40 different laboratories including 2 NIH institutes. The single "no-show" was due to a European flight cancellation. The attendees, with backgrounds in basic or clinical neurosciences, physics, image processing or computer science, ranging from full professors to new graduate students were very comfortable learning together. The feedback from the workshop attendees was uniformly positive with 100% reporting that they would recommend the workshop to others and 50% planning to apply the tools and information they learned to their own work.
 
In January 2008 we debuted the "Hello World" tutorial at the NAMIC AHM in Salt Lake City to an audience of our project members and collaborators. This very constructive presentation was used to make significant improvements in the presentation and delivery of this material. In February 2008 we debuted the users tutorial at a workshop hosted by the Surgical Planning Laboratory at BWH. Again, this presentation was used to make significant improvements in the presentation and delivery of the material. In April of 2008 we ran an all day workshop, hosted by UNC (get details right) for users and developers that incorporated both tutorials. This was attended by approximately 20 individuals coming from a wide range of backgrounds. Time was taken to ensure that all participants gained significant understanding of the new software, sufficient to ensure their successful use of it following the workshop.
 
This year saw the publication of a peer-reviewed manuscript that describes the NAMIC approach to outreach including our multi-disciplinary approach, our integration of theory into practice as driven by a clinical goal, and the translation of concepts into skills through interactive instructor led training sessions (Pujol S, Kikinis R, Gollub R: Lowering the barriers inherent in translating advances in neuroimage analysis to clinical research applications, Academic Radiology 15: 114-118, 2008, add link to Publication DB).
* Text here about Project Events 5 & 6 from Tina if not already included elsewhere.
* Text here about the MICCAI Open Source Workshop if not already included elsewhere (Steve?)
* Slicer IGT event December 2007 (tina?)
* Wiki to web
* Impact as measured by number of downloads of tutorial materials (help someone)
* Should the DTI tractography validation project be written up somewhere, if so where? I will do it if it isn't already assigned.

==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===
Within the center, NA-MIC has formed a community around its software
engineering tools, imaging platforms, algorithms, and clinical
workflows. The NA-MIC calendar includes the All Hands Meeting and
Winter Project Week, the Spring Algorithm Meeting, the Summer Project
Week, Slicer3 Mini-Retreats, Core Site Visits, Training Workshops, and weekly telephone
conferences.

The NA-MIC software engineering tools (CMake, Dart, CTest, CPack) have
enabled the development and distribution of a cross-platform, nightly
tested, end-user application, Slicer3, that is a complex union of
novel application code, visualization tools (VTK), imaging libraries
(ITK, TEEM), user interface libraries (Tk, KWWidgets), and scripting
languages (TCL, Python). The NA-MIC software engineering tools have been
essential in the development and distribution of the Slicer3 imaging
platform to the NA-MIC community.

NA-MIC's end-user application, Slicer3, supports the research within
NA-MIC by providing a base application for visualization and data
management. Slicer3 also supports the research within NA-MIC by
providing plugin mechanisms which allow researchers to quickly and
easily integrate and distribute their technology with Slicer3. Slicer3
is available to all center participants and the external community
through its source code repository, official binary releases, and
unofficial nightly binary snapshots.

NA-MIC drives the development of platforms and algorithms through the
needs and research of its DBPs. Each DBP has selected specific
workflows and roadmaps as focal points for development with a goal of
providing the community with complete end-to-end solutions using
NA-MIC tools. The community will be able to reproduce these workflows
and roadmaps in their own research programs.

NA-MIC algorithms are designed and used to address specific needs of
the DBPs. Multiple solution paths are explored and compared within
NA-MIC, resulting in recommendations to the field. The NA-MIC
algorithm groups collaborate and orchestrate the solutions to the
DBP workflows and roadmaps.

===Impact within NIH Funded Research===
Within NIH funded research, NA-MIC is the NCBC collaborating center for three R01's: "Automated FE Mesh Development", "Measuring Alcohol and Stress Interactions with Structural and Perfusion MRI", and "An Integrated System for Image-Guided Radiofrequency Ablation of Liver Tumors". Several other proposals have been submitted and are under
evaluation for the "Collaborations with NCBC PAR". NA-MIC also
collaborates on the Slicer3 platform with the NIH funded Neuroimage
Analysis Center and the National Center for Image-Guided Therapy. The
NIH funded "BRAINS Morphology and Image Analysis" project is also
leveraging NA-MIC and Slicer3 technology. NA-MIC collaborates with the
NIH funded Neuroimaging Informatics Tools and Resources Clearinghouse
on distribution of Slicer3 plugin modules.

===National and International Impact===
NA-MIC events and tools garner national and international interest.
Over 100 researchers participated in the NA-MIC All Hands Meeting and
Winter Project Week in January 2008. Many of these participants were
from outside of NA-MIC, attending the meetings to gain access to the
NA-MIC tools and researchers. These external researchers are
contributing ideas and technology back into NA-MIC. In fact, a
breakout session at the Winter Project Week on "Geometry and Topology
Processing of Meshes" was organized by four researchers from outside
of NA-MIC.

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

NA-MIC science is recognized by the medical imaging community. Over
100 NA-MIC related publications are listed on PubMed. Many of these
publications are in the most prestigious journals and conferences in the
field. Portions of the DBP workflows and roadmaps are already being
utilized by researchers in the broader community and in the
development of commercial products.

NA-MIC sponsored several events to promote NA-MIC tools and
methodologies. NA-MIC co-sponsored the "Third Annual Open Source
Workshop" at the Medical Image Computing and Computer-Assisted
Intervention (MICCAI) 2007 conference. The proceedings of the
workshop are published on the electronic Insight Journal, another
NIH-funded activity. NA-MIC sponsored three training workshops on
NA-MIC tools for the Biocomputing community in this fiscal year and
plans to hold sessions at upcoming MICCAI and RSNA conferences.

==NA-MIC Timeline (Whitaker)==

==Appendix A Publications (Kapur)==
These 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.

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

AHM 2008

2008-01-10T14:24:54Z

Hazlett:

__NOTOC__
== Introduction ==

{| border="00" cellpadding="8" cellspacing="0"
|-
| rowspan="2"| '''This is the home page for the 2008 NA-MIC all hands meeting (AHM).''' NA-MIC participants meet for a AHM once a year. The purpose of the AHM is to coordinate, discuss plans and report to NIH officers and the external advisory board (EAB). The external advisory board meets with the NA-MIC leadership immediately after the AHM. In parallel, NA-MIC is organizing a project week. These events, with the exception of the EAB meeting, are open to collaborators and potential collaborators.

For more information about the project weeks in general, click [[Engineering:Programming_Events|'''here''']].

For information about the January 2008 project week, see below or click [[2008_Winter_Project_Week|'''here''']].

For information about Utah as a travel destination click [http://www.utah.com '''here'''].
| style="background: #ebeced" colspan="2" align="center"| [[Image:SLC.jpg|center|350px|View of the City]]
|-
| style="background: #ebeced"| SLC
| style="background: #ebeced"|The 2008 AHM, EAB and Project Week will be held in Salt Lake City, UT, January 7-11 2008.
|}
 

We have three wireless access points at the AHM.

Two of them are located in the capital ballroom. One is named capital-ballroom, the other is named capital-ballroom2. If one access point doesn't let you connect it is probably overloaded. In that case, please try connecting to the other one.

The wireless in the amethyst ballroom is named linksys.

None of these access points require a password to connect.

== Agenda ==

{| border="1" cellpadding="5"
|- style="background:#ebeced; color:black" align="left"
| style="width:4%" | '''Time'''
| style="width:12%" | '''Monday, January 7'''
| style="width:12%" | '''Tuesday, January 8'''
| style="width:12%" | '''Wednesday, January 9'''
| style="width:32%" | '''Thursday, January 10 '''
| style="width:12%" | '''Friday, January 11'''
|-
| style="background:#ffffdd; color:black"|
| style="background:#b4d597; color:#522200"| '''[[2008_Winter_Project_Week|Project Activities]] ''' in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Capitol B-C]
| style="background:#b4d597; color:#522200"| '''[[2008_Winter_Project_Week|Project Activities]] ''' in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Capitol B-C]
| style="background:#b4d597; color:#522200"| '''[[2008_Winter_Project_Week|Project Activities]] ''' in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Capitol B-C]
| style="background:#fff6a6; color:#522200"| '''AHM''' in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Capitol A-B], [[2008_EAB|'''EAB''']] in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Olympus B]
'''[[2008_Winter_Project_Week|Project Activities]] ''' in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Olympus A]
| style="background:#b4d597; color:#522200"|'''[[2008_Winter_Project_Week|Project Activities]] ''' in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Capitol B-C]
|-
| style="background:#ffffdd; color:black"|'''7:30-'''
| style="background:#ebeced; color:black"|
| style="background:#b4d597; color:black"| Breakfast
| style="background:#b4d597; color:black"| Breakfast
| style="background:#fff6a6; color:black"| Breakfast
| style="background:#b4d597; color:black"| Breakfast
|-
| style="background:#ffffdd; color:black"|'''8:00-10:00'''
| style="background:#ebeced; color:black"|'''9:30''' Core 1 and 2 PI closed session in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Capitol A]
| style="background:#b4d597; color:black"|Project Work [[2008 Winter Project Week Plug-ins for Slicer3|Plug-ins for Slicer3]] in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Amethyst 1]
| style="background:#b4d597; color:black"|Project Work '''8:00-9:00''' [[2008 Winter Project Week IGT|IGT Breakout Session]] [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Amethyst 1]
 
9am: [[2008 Winter Project Week Tractography|Tractography Breakout Session]] in [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Amethyst 2]
| style="background:#fff6a6; color:black"| 
'''8:00''' [[AHM 2008 Introduction|Introduction]], Ron Kikinis 

'''Core 1 and 2 Presentations''' 
'''8:05''' [[Media:NAMIC-AHM2008-Utah1.ppt | Utah 1]], Ross Whitaker 
'''8:20''' [[Media:Utah-Gerig-EAB.ppt‎ | Utah 2]], Guido Gerig 
'''8:30''': [[Media:MIT_NAMIC_AHM2008.ppt‎ | MIT]], Polina Golland 
'''8:45''': [[Media:20080110-AHM-GeorgiaTech.zip|Georgia Tech]], Allen Tannenbaum 
'''9:00''': [[Media:2008 UNC Core1 NAMIC AHM.ppt | UNC]], Martin Styner 
'''9:10''': MGH/WUSTL, Dan Marcus 
'''9:25''': Kitware, Will Schroeder 
'''9:40''': [[Media:GE Research AHM 2008.ZIP | GE Research]], Jim Miller
| style="background:#b4d597; color:black"|Project Work
|-
| style="background:#ffffdd; color:black"|'''10:00-10:30'''
| style="background:#ebeced; color:black"| Core 1 and 2 PI closed session
| style="background:#b4d597; color:black"| Coffee
| style="background:#b4d597; color:black"| Coffee
| style="background:#fff6a6; color:black"| Coffee
| style="background:#b4d597; color:black"| Coffee
|-
| style="background:#ffffdd; color:black"|'''10:30-12:00'''
| style="background:#ebeced; color:black"| Core 1 and 2 PI closed session
| style="background:#b4d597; color:black"|Project Work
| style="background:#b4d597; color:black"|Project Work 
[[2008 Winter Project Week Tractography|Tractography Breakout Session contd]]
| style="background:#fff6a6; color:black"|
'''10:30''': [[media:2008 NA-MIC AHM Isomics.ppt | Isomics]], Steve Pieper 
'''10:45''': [[media:NA-MIC_2008AHM_UCSD.ppt | UCSD]], Mark Ellisman 
'''11:00''': UCLA, Arthur Toga 
'''11:15''': Outreach: Training & [[media:2008 NA-MIC AHM Dissemination.ppt|Dissemination]], Randy Gollub 
'''11:25''': Invited speaker: Mike Sherman, Stanford NCBC Simbios, SimTK Architect
| style="background:#b4d597; color:black"|Project Work
|-
| style="background:#ffffdd; color:black"|'''12:00-1:00'''
| style="background:#b4d597; color:black"| Lunch
| style="background:#b4d597; color:black"| Lunch [[DBP Engineering Resource|DPB Engineers Lunch meeting]]
| style="background:#b4d597; color:black"| Lunch [[AHM 2008 fMRI|Birds of a Feather: fMRI]]
| style="background:#fff6a6; color:black"| Lunch
| style="background:#b4d597; color:black"| Adjourn
|-
| style="background:#ffffdd; color:black"|'''1:00-3:00'''
| style="background:#b4d597; color:black"|Introduce Projects and Participants
| style="background:#b4d597; color:black"|Breakout: [[2008 Winter Project Week Geometry and Topology processing of Meshes|Geometry and Topology processing of Meshes]] [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Amthyst 1]
| style="background:#b4d597; color:black"|Project Work [[2008 Winter Project Week Tractography|Tractography Breakout Session contd]]
| style="background:#fff6a6; color:black"|
'''DBP results''' 
'''1:00''': [[Media:DBP2-Prostate-AHM2008.pdf | Queens/JHU]], David Gobbi 
'''1:15'''::[[Media:UNC DBP 2_longitudinal autism study_AHM jan08.ppt | UNC]], Heather Cody Hazlett 
'''1:30''':[[Media:NA-MIC_Kubicki.ppt | Harvard]], Marek Kubicki 
'''1:45''':MIND/UNM, Jeremy Bockholt 

'''NCBC Collaborations''' 
'''2:00''': [[Image:NA-MIC_Cleary_RF_Ablation_Georgetown.pdf | Georgetown, Kevin Cleary]] 
'''2:15''': [[Media:External-Wyatt-WFUSM.ppt‎|Wake Forest/VT, Chris Wyatt]] 
'''2:30''': [[Media:NA-MIC Mesh Collaboration - 2008.ppt| UIowa, Nicole Grosland,Vincent Magnotta]] 
| style="background:#ebeced; color:black"|
|-
| style="background:#ffffdd; color:black"|'''3:30-4:00'''
| style="background:#b4d597; color:black"| Coffee
| style="background:#b4d597; color:black"| Coffee
| style="background:#b4d597; color:black"| Coffee
| style="background:#fff6a6; color:black"| Coffee
| style="background:#ebeced; color:black"|
|-
| style="background:#ffffdd; color:black"|'''3:00-5:00'''
| style="background:#b4d597; color:black"|'''3:00-4:00''' Slicer Update [[Media:2008 NA-MIC AHM Slicer3.ppt | Slides]] 
'''4:00-5:00''' Breakout: [[2008 Winter Project Week EM Segmenter User Group|EM Segmenter User Group]] [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Amethyst 1]
| style="background:#b4d597; color:black"|Project Work [[2008_Winter_Project_Week_Image_Registration_Update| Registration Breakout]] [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Amethyst 1]
| style="background:#b4d597; color:black"|Project Work '''3:00-4:00''' [[2008 Winter Project Week Batchmake Update|Batchmake Update]] [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Amethyst 1]
[[2008 Winter Project Week Tractography|Tractography Breakout Session contd]] 
| style="background:#fff6a6; color:black"|[[2008 EAB|EAB]] [http://www.marriott.com/hotels/event-planning/floor-plans/slccc-salt-lake-city-marriott-city-center/ Olympus B] '''3:00-4:00''' Discussion with NA-MIC Leadership '''4:00-5:00''' Closed Session
| style="background:#ebeced; color:black"|
|-
| style="background:#ffffdd; color:black"|'''05:00-07:00'''
| style="background:#b4d597; color:black"|
| style="background:#b4d597; color:black"|
| style="background:#b4d597; color:black"|'''5:00''' Optional: [[2008-SCI-tour|Tour]] of the new SCI building
| style="background:#fff6a6; color:black"|'''6:00''' Optional: [http://www.skisaltlake.com/murphys.htm Beer at Murphy's] (like last year)
| style="background:#ebeced; color:black"|
|}

Please note that there will be a Core 1&2 Site PI Retreat on the morning of Monday, January 7th. This is a closed session for Core 1&2 Site PIs, with no delegates. The topic is the competitive renewal.

== Dates. Venue. Registration ==

'''Dates:'''
* The All Hands Meeting and External Advisory Board Meeting will be held on '''Thursday, January 10th'''.
* Project Activities will be held rest of the week between '''Monday, January 7th and Friday, January 11th'''.

'''Venue:''' The venue for the meeting is [http://www.marriott.com/hotels/travel/slccc-salt-lake-city-marriott-city-center/ Marriot City Center, Salt Lake City, Utah] Mariott City Center, Salt Lake City, Utah. [http://marriott.com/property/meetingsandevents/floorplans/slccc (Floorplan)]. To reserve rooms at the meeting rate of $129/night, please either call the hotel at 1-801-961-8700 or 1-866-961-8700 (toll free) and mention that you are attending the NAMIC meeting or book online by using the code SCISCIA. Please note that we do need attendees to use this hotel in order to not incur additional charges for the use of conference rooms.

<big> '''Registration:''' We are charging a registration fee to all participants. The fee covers the costs of the facilities and food provided. In order to keep the fee low, we need to get a sufficient number of hotel nights by our participants. See above for more on this. Please click [http://www.sci.utah.edu/namic2008/registration.html '''here'''] for online registration. This registration must be completed by Friday, December 14, 2007. </big>

== Attendees ==

Please note that unlike past events, registration will not be done on the wiki. Instead you need to follow [http://www.sci.utah.edu/namic2008/registration.html this registration link] to complete your online registration. The organizers will periodically publish the list of registered attendees in the space below. Attendees should '''NOT''' add their names in this section.

This is the list of attendees who have registered as of January 5, 2008

===AHM===
# Michael J. Ackerman, Ph.D. National Library of Medicine
# Everette Burdette Acoustic MedSystems, Inc.
# German Cavelier NIHNIMH
# Kevin Cleary Georgetown University collaborator
# Zohara Cohen National Institutes of Health
# Jack Collins SAIC-Frederick, Inc.
# Gabor Fichtinger Queens University
# Chris Johnson External Advisory Board EAB member
# Danial Lashkari MIT MIT
# Mikhail Milchenko Washington University at St Louis 2
# Sandy Napel Stanford EAB member
# Godfrey Pearlson Yale EAB member
# Fred Prior External Advisory Board EAB member
# Mike Sherman Stanford Invited Speaker
# Arthur Toga Laboratory of Neuro Imaging, UCLA
# Chris Wyatt Virginia Tech Collaborator
# Terry S. Yoo National Library of Medicine

===AHM and Project Event===
# Douglas Alan Harvard University IIC Collaborator
# Luca Antiga Mario Negri Institute
# Kevin Archie Washington University School of Medicine
# Nicole Aucoin Brigham and Women's Hospital 2
# Stephen Aylward Kitware, Inc.
# Serdar Balci MIT
# Sebastien Barre Kitware, Inc.
# Jack Blevins Acoustic MedSystems, Inc.
# Daniel Blezek Mayo Clinic External
# H Jeremy Bockholt The MIND Research Network MIND DBP2
# Sylvain Bouix Brigham and Womens Hospital Psychiatry Neuroimaging Lab Core 3
# Francois Budin Brigham and Womens Hospital
# Patrick Cheng Georgetown University collaborator
# Kiyoyuki Chinzei AIST, Japan
# Nikos Chrisochoides College of William and Mary
# Csaba Csoma Johns Hopkins University
# Brad Davis Kitware, Inc.
# Preston Tom Fletcher SCI Institute
# Andreas Freudling Brigham and Women's Hospital Leadership Core
# Yi Gao Georgia Tech
# Guido Gerig SCI Institute
# David Gobbi Queens University DBP2
# Polina Golland MIT Core 1
# Randy Gollub MGH Department of Psychiatry Core 5
# Casey Goodlett SCI Institute
# Alexandre Gouaillard Caltech
# Sylvain Gouttard SCI Institute
# Benjamin Grauer Brigham and Women's Hospital Leadership Core
# Jeffrey Grethe UCSD 2
# Nicole Grosland The University of Iowa, CCAD
# Nathan Hageman UCLA
# Michael Halle Brigham and Women's Hospital Leadership Core
# Nobuhiko Hata Brigham and Women's Hospital Leadership Core
# Nobuhiko Hata Brigham and Women's Hospital
# Kathryn Hayes Brigham and Womens Hospital 2 - Engineering
# Heather Cody Hazlett University of North Carolina DBP Core2
# Bill Hoffman Kitware, Inc.
# Jaesung Hong Kyushu University
# Luis Ibanez Kitware, Inc.
# Mustafa Okan Irfanoglu OSU
# Firdaus Janoos ohio-state univ
# Julien Jomier Kitware, Inc.
# Usman Khan Brigham and Womens Hospital
# Ron Kikinis Brigham and Womens Hospital Leadership Core
# Marek Kubicki Brigham and Womens Hospital
# Curtis Lisle KnowledgeVis, LLC 2
# Haiying Liu Brigham and Womens Hospital
# William Lorensen External Advisory Board EAB
# Raghu Machiraju The Ohio State University
# Vincent Magnotta The University of Iowa, CCAD
# Daniel Marcus Washington University Core 2
# Doug Markant Brigham and Women's Hospital Leadership Core
# Katie Mastrogiacomo SPL, Brigham and Women's Hospital Leadership Core
# Sean Megason Caltech
# John Melonakos Georgia Tech
# James V Miller GE Global Research
# Vandana Mohan Georgia Inst of Technology 1
# Kishore Mosaliganti The Ohio State University
# Tri Ngo MIT
# Ipek Oguz UNC 2
# Steve Pieper Isomics, Inc. 2, 6
# Carlo Pierpaoli NIH
# Wendy Plesniak Brigham and Women's Hospital Leadership Core
# Kilian Pohl Brigham and Women's Hospital 1
# Marcel Prastawa SCI Institute
# Sonia Pujol Brigham and Women's Hospital Leadership Core
# Katharina Quintus Brigham and Women's Hospital Leadership Core
# Yogesh Rathi Brigham and Womens Hospital
# Mert Sabuncu MIT
# Will Schroeder Kitware, Inc.
# Mark Scully The MIND Research Network
# Li Shen Indiana University
# Kiran Shivanna The University of Iowa, CCAD
# Martin Styner UNC Chapel Hill 1
# Padma Sundaram Brigham and Womens Hospital Brigham and Womens
# Xiaodong Tao GE
# Kinh Tieu Brigham and Women's Hospital
# Junichi Tokuda Brigham and Women's Hospital Leadership Core
# Clement Vachet UNC Chapel Hill 3
# Koen Van Leemput MIT
# Sandy Wells Brigham and Womens Hospital
# Carl-Fredrik Westin Brigham and Womens Hospital NAC
# Nathan Wilson Cardiovascular Simulation, Inc.
# Alexander Yarmarkovich Isomics, Inc. Engineering Core 2
# Anastasia Yendiki MGH
# Boon Thye Yeo MIT
# Tim Yin Stony Brook University

This is the list of attendees who have registered as of January 5, 2008. Please note that unlike past events, registration will not be done on the wiki. Instead you need to follow [http://www.sci.utah.edu/namic2008/registration.html this registration link] to complete your online registration. The organizers will periodically publish the list of registered attendees in the space above. Attendees should '''NOT''' add their names in this section.

File:UNC DBP 2 longitudinal autism study AHM jan08.ppt

2008-01-10T14:09:52Z

Hazlett:

2007 Programming/Project Week MIT

2007-06-14T19:01:35Z

Hazlett: /* Attendee List */

Back to [[Engineering:Programming_Events|Programming/Project Events]]
[[Image:ProjectWeek-2007.png|thumb|450px|right|Summer 2007]]
== Logistics ==

'''Dates:''' June 25-29, 2007

'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].

'''Registration Fee:''' $200 (this will cover the cost of breakfast,lunch, and coffee breaks for the week). Due by Tuesday, June 19, 2007. Please make checks out to "Massachusetts Institute of Technology" and mail to:
Donna Kaufman, MIT, 77 Massachusetts Ave., 38-409b, Cambridge, MA 02139

If you are attending for one day only, the registration fee is not required.

'''Hotel:''' We have a group rate of $209/night at the [http://www.hotelatmit.com Hotel at MIT]. (Use group code NAM.) Here is some information about several other Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Summer is tourist season in Boston, so please book your rooms early.

([[Project Week Logistics Checklist|This is a checklist for the onsite planning items]])

===Introduction to NA-MIC Project Week===

This is a week of hands on activity -- programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, and clinical application -- that has become one of the major events in the [[NA-MIC-Kit|NA-MIC Kit]] calendar. A full week of hands on activities is held in the summer at MIT (typically the last week of June), and for half a week in Salt Lake City in the winter (typically the second week of January).
The main goal of these events if to move forward the deliverables of NA-MIC. NA-MIC participants and their collaborators are welcome to attend.

* NA-MIC Members: Participation in this event is voluntary -- if you don't think this will help you move forward in your work, there is no obligation to attend.
* Ideal candidates are those who want to contribute to the [[NA-MIC-Kit|NA-MIC Kit]], and those who can help make it happen.
* This is not an introduction to the components of the [[NA-MIC-Kit|NA-MIC Kit]].
* NA-MIC Core 1 (Algorithms) - bring your algorithms and code to work on in the company of Core 2 engineers and Core 3 scientists.
* NA-MIC Core 2 (Engineering) - bring your code for infrastructure and applications to extend the [[NA-MIC-Kit|NA-MIC Kit]] capabiliities, integrate Core 1 algorithms, and refine worflows for Core 3.
* NA-MIC Core 3 (DBP) - bring your data to work on with the [[NA-MIC-Kit|NA-MIC Kit]]and get assistance and provide feedback to Core 1 scientists and Core 2 engineers.
* External Collaborators - if you are working on a project that uses the [[NA-MIC-Kit|NA-MIC kit]], and want to participate to get help from NA-MIC Engineering, please send an email to Tina Kapur (tkapur at bwh.harvard.edu). Please note that the event is open to people outside NA-MIC, subject to availability.
* Everyone should '''bring a laptop'''. We will have three or four projectors.
* About half the time will be spent working on projects and the other half in project related discussions.
* You '''do''' need to be actively working on a NA-MIC related project in order to make this investment worthwhile for everyone.

=== Agenda===
Please note that this agenda is a draft and will be finalized by June 15th.

* Monday June 25
** noon-1pm lunch
** 1-3:30pm Introduce [[#Projects|Projects]] using templated wiki pages (all Project Leads) ([[NA-MIC/Projects/Theme/Template|Wiki Template]])
** 3:30-5:30pm Start project work
** 4:30-5:30pm [[Special topic breakout: New DBP Introduction to NA-MIC Engineering]]
* Tuesday June 26
** 8:30am breakfast
** 9:00-9:45am: NA-MIC Software Process (Bill Hoffman)
** 10-10:30am Slicer 3.0 Update(Jim Miller, Steve Pieper)
** 11-12pm: [[Special topic breakout: IGT for Prostate]]
** noon lunch
** 1-2pm: [[Special topic breakout: KWWidgets]]
** 2-3pm: [[Special topic breakout: Non-Linear Registration]]
** 3-4pm: [[Special topic breakout: Atlases]]
** 4-5pm: [[Special topic breakout: DWI/DTI]]
** 5:30pm adjourn for day
* Wednesday June 27
** 8:30am breakfast
** noon lunch
** 1-2pm: Special topic breakout: [[NA-MIC:2007 Plan for Long-Lead Time Items| Plan for Long Lead Time Items]]
** 5:30pm adjourn for day
* Thursday June 28
** 8:30am breakfast
** noon lunch
** 3:30-5pm Special topic breakout: TBD
** 5:30pm adjourn for day
* Friday June 29
** 8:30am breakfast
** 10am-noon: Project Progress using update [[#Projects|Project Wiki pages]]
** noon lunch boxes and adjourn. (Next one [[AHM_2008| in Utah the week of Jan 7, 2008]])

=== Preparation ===

# Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-programming-week na-mic-programming-week mailing list]
# [[Engineering:TCON_2007#2007-05-03|May 3, 2007: Kickoff TCON#1 to discuss Engr Core Projects and Assign/Verify Teams]]
# [[Engineering:TCON_2007#2007-05-10|May 10, 2007: TCON#2 to discuss Projects and Assign/Verify Teams]]
# [[Engineering:TCON_2007#2007-05-17|May 17, 2007: TCON#3 to discuss outstanding projects and teams from previous week]]
# May 17, 2007: Create a Wiki page per project (the participants must do this, hopefully jointly)
# May 31, 2007: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Andy)
## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)
## Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Andy)
# By 3pm ET on June 21, 2007: [[NA-MIC/Projects/Theme/Template|Complete a templated wiki page for your project]]. Please do not edit the template page itself, but create a new page for your project and cut-and-paste the text from this template page. If you have questions, please send an email to tkapur at bwh.harvard.edu.
# [[Engineering:TCON_2007#2005-06-21|June 21, 2007: TCON#4 Final Call before showtime...]]
# Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...

== Projects ==

=== Template ===

http://www.na-mic.org/Wiki/index.php/NA-MIC/Projects/Theme/Template - Please use this template intead of the 4-block ppts.

===DBP II===
These are projects by the new set of DBPS:
#[[DBP2:MIND | Longitudinal Classification of White Matter Lesions in Lupus]] (MIND/UNM)
#[[DBP2:JHU | Segmentation and Registration Tools for Robotic Prostate Interventions]] (JHU/Queen's)
#[[DBP2:UNC |Longitudinal MRI study of early brain development in neuropsychiatric disorder: UNC Autism Study]] (UNC)
#[[DBP2:Harvard|Velocardiofacial Syndrome (VCFS) as a genetic model for schizophrenia]] (Harvard)

===Structural Analysis===
#[[Projects/Structural/2007_Project_Week_EMSegmentation_Validation| EMSegmentation Validation]] (Brad Davis Kitware, Sylvain Bouix BWH)
#[[Projects/Structural/2007_Project_Week_vtkITK Wrapper for Rule Based Segmentation|vtkITK wrapper for rule based segmentation]] (John Melonakos GATech, Tauseef Rehman GATech, Brad Davis Kitware, Marek Kubicki BWH)

===Diffusion Image Analysis===
# [[Projects/Diffusion/2007_Project_Week_DTI_Population_Analysis| DTI population analysis]] (Casey Goodlett UNC, Jim Miller GE, Marek Kubicki BWH)
#[[Projects/Diffusion/2007_Project_Week_Geodesic_Tractography| Geodesic Tractography]] (John Melonakos GATech, Marc Niethammer BWH, Marek Kubicki BWH)
# [[Projects/Diffusion/2007_Project_Week_Slicer 3 Whole Brain Seeding|Slicer3 Whole brain Seeding platform: data representation and pipeline execution]] (Raul San Jose, Lauren O'Donnell, Alex Yarmarkovich)
# [[Projects/Diffusion/2007_Project_Week_Slicer3 Tractography Editor|Slicer 3 Tractography Editor]] (Lauren O'Donnell, Raul San Jose, Alex Yarmarkovich)

===NA-MIC Kit===
#Slicer3
##[[Projects/Slicer3/2007_Project_Week_QDEC_Slicer3_Integration|QDEC Integration into Slicer3]](Nicole Aucoin BWH, Kevin Teich MGH, Nick Schmansky MGH, Doug Greve MGH, Gheorghe Postelnicu MGH, Steve Pieper Isomics)
##[[Projects/Slicer3/2007_Project_Week_Display_Optimization| Display Optimization]] (Raimundo Sierra, David Gobbi, Steve Pieper)
## [[Projects/Slicer3/2007_Project_Week_MRML_Scenes_for_the_Execution_Model_including Transforms|MRML Scenes for the Execution Model including Transforms]] (Jim Miller, Brad Davis, Nicole Aucoin, Alex Yarmarkovich, Steve Pieper)
## [[Projects/Slicer3/2007_Project_Week_Support for Unstructured Grids|Support for Unstructured Grids]] (Steve, Nicole, Alex, Curt)
## [[Projects/Slicer3/2007_Project_Week_Python support in Slicer3|Python Support in Slicer 3]] (Luca, Steve, Dan)
## [[Projects/Slicer3/2007_Project_Week_CPack, Ctest infrastructure improvements|CPack, CTest infrastructure Improvements]] (Bill Hoffman, Katie, Steve)
## [[Projects/Slicer3/2007_Project_Week_Drafting Human Interface and Slicer Style|Drafting Human Interface and Slicer Style]] Guidelines (Wendy,Yumin)
## [[Projects/Slicer3/2007_Project_Week_Slicer Matlab Pipeline for scalars and tensors|Slicer Matlab Pipeline for Scalars and Tensors]] (Katharina, Sylvain, Steve)
## [[Projects/Slicer3/2007_Project_Week_Support for electron microscopy | Dendritic Spine Morphometrics]] (Bryan Smith)
# Slicer2

===External Collaborations===
# Meshing
## [[Collaboration/Iowa/Meshing/Adding VTK Interactive WWidgets to Slicer3]] (Will, Vince, Kiran, Curt)
## [[Collaboration/Iowa/Meshing/Migrate Iowa Neural Net code to pure ITK]] (Vince, Stephen)
# [[Collaboration/WFU/NonHuman Primate Neuroimaging| Applying EMSegmenter to NonHuman Primate Neuroimaging]](Chris Wyatt VT, Kilian Pohl BWH)
# Image-Guided Therapy
## [[Collaboration/BWH/Tracker Integration]] (Noby, Haiying, Katie Hayes BWH)
## [[Collaboration/MGH/Radiation Therapy Radiation Therapy Planning]] (Greg Sharp MGH, Tina Kapur BWH, Sandy Wells BWH, Steve Pieper Isomics, Katie Hayes BWH)
## [[Collaboration/JHU/Brachytherapy needle positioning robot integration|Brachytherapy needle positioning robot integration]] (Csaba Csoma, Peter Kazanzides JHU, David Gobbi Queen's, Katie Hayes BWH)
## [[Collaboration/BWH/RadVision and Tracker Integration|RadVision and Tracker Integration]] (Jack Blevins, Noby)
# Registration
## [[Collaboration/UIowa/Non_Rigid_Registration|Implementing Non-rigid Image Registration and Evaluation Project (NIREP) software using NA-MIC Kit]] (Gary Christensen UIowa, Stephen Aylward, Kitware, Sandy Wells BWH)
## [[Collaboration/UIowa/Developing Electronic Atlas Software using NA-MIC Kit|Developing Electronic Atlas Software using NA-MIC Kit]] (Gary Christensen UIowa, Jeff Grethe, Wendy)
## [[Collaboration/UIowa/Developing a GUI for non-rigid image registration programs using NA-MIC Kit|Developing a GUI for non-rigid image registration programs using NA-MIC Kit]] (Gary Christensen UIowa, Yumin Kitware)
# [[Collaboration/VMTK |vmtk (vmtk.sourceforge.net) integration within Slicer3]] (Luca Antiga, MNI, Dan Blezek GE)
# [[Collaboration/NWU/Radiology Workstation| A Translation Station]](Skip, Alex, Vlad, Pat, Alex, Steve)
# [[NA-MIC_NCBC_Collaboration:3D%2Bt_Cells_Lineage:GoFigure|3D+t Cells Lineage:GoFigure]] (Alex G, Yumin)

===Non-Medical Collaborations===
* [[Collaboration/Harvard IIC/AstroMed]] (Michael Halle, Douglas Alan)

==Attendee List==
#Kilian Pohl, BWH, Core 1
#John Melonakos, Georgia Tech, Core 1
#Tauseef Rehman, Georgia Tech, Core 1
#Casey Goodlett, UNC, Core 1, (Hotel at MIT request)
#W. Bryan Smith, UCSD/NCMIR, Core 2
#Jim Miller, GE Core 2, Booked at the Hotel At MIT
#Steve Pieper, Isomics, Core 2
#Katie Hayes, BWH, Core 2
#Dan Blezek, GE Core 2, Booked at the Hotel At MIT
#Tina Kapur, BWH, Core 6
#Ron Kikinis, Core 7, PI
#Peter Kazanzides, JHU, Collaborator
#Wendy Plesniak, BWH, Collaborator
#Luca Antiga, Mario Negri Institute, Collaborator
#Sylvain Bouix, BWH, Core 3
#Marek Kubicki, BWH, Core 3
#Chris Wyatt, Virginia Tech, Collaborator
#Nicole Aucoin, BWH, Core 2
#Will Schroeder, Kitware, Core 2
#Yumin Yuan, Kitware, Core 2
#Brad Davis, Kitware, Core 2
#Stephen Aylward, Kitware, Collaborator
#Luis Ibanez, Kitware, Core 2
#Bill Hoffman, Kitware, Core 2
#Zack Galbreath, Kitware, Core 4
#Raimundo Sierra, BWH, Core 2
#Clare Tempany, BWH Collaborator (Tuesday, June 26th only)
#Noby Hata, BWH Collaborator (Monday, June 25th only)
#Haiying Liu, BWH Collaborator
#Alex Yarmarkovich, Isomics, Core 2
#Vincent Magnotta, University of Iowa, Collaborator (Hotel at MIT request)
#Hans Johnson, University of Iowa, Collaborator
#Gary E. Christensen, University of Iowa, Collaborator
#Joo Hyun (Paul) Song, University of Iowa, Gary's student
#Xiujuan Geng, University of Iowa, Gary's student
#Jake Nickel, University of Iowa, Gary's student
#Nick Kiguta, University of Iowa, Gary's student
#Kunlin Cao, University of Iowa, Gary's student
#James Harris, University of Iowa, Gary's student
#Kai Ding, University of Iowa, Gary's student
#Jeff Hawley, University of Iowa, Gary's student
#Skip Talbot, Northwestern University, Collaborator (Hotel at MIT request)
#Alex Kogan, Northwestern University, Collaborator (Hotel at MIT request)
#Vladimir Kleper, Northwestern University, Collaborator (Hotel at MIT request)
#Pat Mongkolwat, Northwestern University, Collaborator (Hotel at MIT request)
#Csaba Csoma, Johns Hopkins University, Collaborator (Hotel at MIT request)
#David Gobbi, Queen's University, Collaborator (Hotel at MIT request)
#H. Jeremy Bockholt, The MIND Institute, DBP2:MIND PI
#Mark Scully, The MIND Institute, DBP2:MIND software engineer
#Sumner Williams, The MIND Institute, Magnotta/Johnson/Bockholt BRAINS grant software engineer
#Greg Sharp, MGH, Collaborator
#Lauren O'Donnell, BWH
#Raul San Jose, BWH
#Katharina Quintus, BWH, Core 3
#Marc Niethammer, BWH. Core 3
#Kevin Teich, MGH
#Michael Halle, BWH/IIC
#James Ross, GE
#Kiran Shivanna, University of Iowa, Collaborator (Hotel at MIT request)
#Douglas Alan, Harvard IIC
#Clement Vachet, UNC Core 3 (CS programmer) - full week (Hotel @ MIT request)
#Ran Tao, Utah, Core 1
#Alex. Gouaillard, CEGS caltech Software Engineer, QuadEdgeMesh project Lead (ITK)
#Sean Megason, CEGS caltech PI, (not full week)
#Clif Burdette (Acousticmed, Collaborator)
#Jack Blevins (Acousticmed, Collaborator)
#Lilla Zollei (MGH)
#Serdar K Balci (MIT)
No more attendees. 70 is the last one we can accommodate.

2007 Core 1 Meeting

2007-05-30T19:26:43Z

Hazlett: /* Agenda */

== Agenda ==

*May 30 -
** 8:30am Remarks (Whitaker)
*** Introductions
*** Discuss goals and agenda
** 9:00am Driving Biological problems '''[[2007_Core_1_Meeting_DBP_Guidlines|(Guidelines for talks)]]'''
*** Marek Kubicki - Harvard/B&W '''[[Media:VCFS-Core1.ppt | VCFS DBP]]'''
*** Gabor Fichtinger - Johns Hopkins
*** (break)
*** H. Jeremy Bockholt - Mind Institute '''[[Media:Na-mic core1 200705 meeting lupus progress report.ppt.pdf | lupus DBP]]'''
*** Heather Cody Hazlett - UNC '''[[Media:namic_core1 may07_longitud aut study.ppt| slides]]'''
**11:30pm Core 1 Coordinate Discussion
*** A preliminary discussion in preparation for agenda items on Thurs afternoon. For example --- software dissemination.
**12:00pm Lunch
** 1:00pm Invited Talk: Daniel Rueckert. "Quantification of brain development during early childhood using non-rigid registration". ([http://www.csail.mit.edu/events/eventcalendar/calendar.php?show=event&id=1484 talk announcement])
** 2:00-4:30pm Core 1 presentations
*** John Melonakos (Georgia Tech) - "PDE methods for Image Segmentation and Shape Analysis: From the Brain to the Prostate and Back"
*** Bruce Fischl (MGH) "Predicting architectonic boundaries from folding patterns"
*** Serdar Balci (MIT), "Group-wise registration in NAMIC-kit"
*** Break
*** Guido Gerig (UNC), "DTI atlas building with application to PNL SZ study (C. Goodlett)"
*** Tom Fletcher (Utah), "Volumetric Connectivity for analysis of DTI"
** 4:30-6:00pm Break out sessions with DPBs
*May 31
** 8:30am Core 1 presentations
*** Utah "Nonparametric Shape Modeling of Object Complexes" (Speaker TBD)
*** Guido Gerig (UNC) "UNC Shape Analysis (M. Styner, I. Oguz)" / "Constructing Image Graphs for Lesion Segmention (M. Prastawa)"
*** (break)
*** Mert Sabuncu (MIT), "Population Analysis of Anatomical Variability"
*** Gheorghe Postelnicu (MGH), "5D warping: integrating volume and surface information"
*** John Melonakos (Georgia Tech) - "PDE methods for DWMRI Analysis and Image Registration"
** 12:30pm Lunch
** 1pm Strategic planning, software
*** Progress/interactions with DBPs
*** Software contributions and Core 2 interactions
*** Performance assessment and renewal
** 5pm Finish

== Logistics ==

'''Dates:''' May 30-31, 2007.

Please plan to travel on May 29, so that we can start in the morning of May 30.
The meeting will be two full days; please plan to stay until 5pm on May31.

'''Location:''' MIT.

The meeting will take place in the Kiva conference room, on the 4th floor of Stata Center. We will post signs in the building, directing you to the conference room.

'''Food:''' Breakfast and lunch will be provided on May 30-31. We will also organize people into groups to go out for dinner on May 30. There are plenty of restaurants around Central Square and MIT.

'''Hotel:''' There is no official hotel for the meeting. Here is some information about Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Please book your rooms early.

== Attendees ==

#Ross Whitaker (Utah)
#Polina Golland (MIT)
#Ron Kikinis (BWH)
#Guido Gerig(UNC)
#Heather Cody Hazlett (UNC Autism DBP)
#Allen Tannenbaum (Georgia Tech)
#John Melonakos (Georgia Tech)
#H. Jeremy Bockholt (MIND/UNM)
#Harvard Core 3: Marek Kubicki
#Mert R Sabuncu (MIT CSAIL)
#Lilla Zollei (NMR, MGH)
#Serdar K Balci (MIT CSAIL)
#Ulas Ziyan (MIT)
#Tom Fletcher (Utah)
#Josh Cates (Utah - tentative)
#Gabor Fichtinger (JHU)
#Jim Miller (GE Research)
#Dennis Jen (MGH)
#Sandy Wells (BWH, MIT)
#Marc Niethammer (BWH, Core 3)
#Wanmei Ou (MIT)
# Purang Abolmaesumi (Queen's)
# Steve Pieper (Isomics, Core 2)
# Peng Yu (HST/MIT)
# Gheorghe Postelnicu (MGH)
# Bruce Fischl (MGH)

File:Namic core1 may07 longitud aut study.ppt

2007-05-30T19:23:25Z

Hazlett:

DBP2:UNC

2007-05-18T17:07:38Z

Hazlett: /* Team and Institute */

Back to [[DBP2]]

* '''Title:''' Longitudinal MRI study of early brain development in neuropsychiatric disorder: UNC Autism Study

==Team and Institute==
*Co-PI: Heather Cody Hazlett, PhD, (heather_cody at med.unc.edu, Ph: 919-966-4099)
*Co-PI: Joseph Piven, MD
*NA-MIC Engineering Contact: Jim Miller, GE Research
*NA-MIC Algorithms Contact: Martin Styner, UNC

* '''Affiliation/Institution:''' UNC Chapel Hill, Department of Psychiatry and the Neruodevelopmental Disorders Research Center NDRC

* '''Science:''' Multiple lines of converging evidence (from MRI, post-mortem and head circumference studies) indicate that brain enlargement in autism is a real phenomenon. However, the onset, trajectory and pattern of this enlargement (in brain tissues, regions and structures), relationship to developing neural circuitry and clinical features; and, pathogenesis, are not yet clear. Results from our longitudinal MRI study of brain development (2 years with follow-up at 4 years) demonstrate robust generalized enlargement of white and gray matter volume in cerebral cortex in autistic individuals (N= 51) by age 2 yrs. The MRI and earlier head circumference data strongly suggest a period of substantial brain overgrowth in autistic individuals between age 12 and 24 months, continuing into ages 4 and 5. This study will provide critical information about the trajectory of brain growth (regions, tissues, structures and fiber tracts) as measured on MRI and DTI, the potential relationship to clinical features; and underlying genetic etiology. These results will provide important insights into developmental brain and behavioral phenotypes, and neurobiological mechanisms in autism.

* ''' Benefits to NA-MIC'''<nowiki>: There is a lack of appropriate tools for processing of pediatric MRI, a challenging topic since pediatric MRI differs significantly from adult MRI due to variable brain shape and the process of maturation/myelination which are reflected in nonlinear shape/volume changes but also regional change of white matter:. Working on a toolkit for the community would have a large impact, in particular also in view of existing and soon to be available databases of normative pediatric MRI (PI Alan Evans). Access to the UNC longitudinal pediatric MRI data representing a period of moderate but significant brain growth can spawn off interesting new software methodology developments from Core-1. Besides existing multi-modal MRI data, the UNC group has a very large set of segmented data (subcortical structures measured with very high reliability (0.92 up to 0.99) for over 140 MRI data sets(hippocampus, amygdale, putamen, pallide globe, caudate, ventricles) - to our knowledge the largest segmentation database of such high quality. These data could be used for shape analysis of growth trajectory and can also serve as a benchmark for novel semi-automated processing. The group has profound experience with the development of novel segmentation protocols (</nowiki>http://www.psychiatry.unc.edu/autismresearch/MRI_PAGE.htm) and the design of large-scale validation of segmentation methodology (see Yushkevich et al., 2006, NeuroImage, http://dx.doi.org/10.1016/j.neuroimage.2006.01.015). Moreover, the groups experience with state-of-the-art ITK/vtk processing tools will help to critically assess and improve the NA-MIC toolkit’s development from the viewpoint of users involved in large clinical studies. The processing of a relatively large database needs highly automated processing “pipelines”, i.e. co-registration of multi-modal data, atlas-to-template registration, automatic tissue segmentation, lobe parcellation, MRI-DTI registration, ROI analysis, and statistical analysis. This data therefore would be an excellent testbed for new automated Slicer 3 processing. A growth-rate analysis might have to include new methods for longitudinal image analysis, cortical thickness and cortical folding pattern analysis, methods not yet developed for the NA-MIC toolkit but required for human brain studies.

* ''' Benefits to UNC NDRC group: ''' The UNC autism research group will have access to NAMIC tools not yet available for analysis, which will expose them to new tools and procedures beyond the ones locally developed. This will significantly expand their processing capabilities but also will allow them to do research within a larger team of leading image analysis research groups. New tools applied to the existing longitdudinal autism pediatric study, including raw image data and processed anatomical structures, are most likely lead to publications demonstrating the processing capabilities and versatility of the NAMIC toolkit.

===Research Goals===

Multiple lines of converging evidence (from MRI, post-mortem and head circumference studies) indicate that brain enlargement in autism is a real phenomenon. However, the onset, trajectory and pattern of this enlargement (in brain tissues, regions and structures), relationship to developing neural circuitry and clinical features; and, pathogenesis, are not yet clear. Results from our longitudinal MRI study of brain development (2 years with follow-up at 4 years) demonstrate robust generalized enlargement of white and gray matter volume in cerebral cortex in autistic individuals (N= 51) by age 2 years. The MRI and earlier head circumference data strongly suggest a period of substantial brain overgrowth in autistic individuals between age 12 and 24 months, continuing into ages 4 and 5. This study will provide critical information about the trajectory of brain growth (regions, tissues, structures and fiber tracts) as measured on structural MRI and DTI, the potential relationship to clinical features; and underlying genetic etiology. These results will provide important insights into developmental brain and behavioral phenotypes, and neurobiological mechanisms in autism.
Our project has the following specific aims:

1) To characterize the pattern of brain size and development at two cross-sectional time periods (ages 18-35 and 42-66 months) in autism and in comparison to controls (children with developmental delay and typical development).

2) To examine the cross-sectional and longitudinal relationships between selected morphological brain features and the pattern of selected cognitive characteristics and behavioral abnormalities reported to be abnormal in autism.

===Description of Data===

'''Subjects:''' Participants in this study include children with autism and controls. Controls include children with developmental delay (DD) and typical development (TYP). The initial age at entry for this longitudinal study (time point 1) is between 18 and 35 months. At this time a clinical and behavioral assessment (see below) and MRI scan is performed. A follow-up visit is conducted when subjects are between 42 and 66 months of age (approximately 24 months after their initial visit). At this time (time point 2) a repeat assessment and MRI scan is performed.

'''Inclusion/Exclusion Criteria for subjects with Autism:''' Subjects are eligible to enter the study at time 1 if they are between 18 and 35 months of age, and receive a clinical diagnosis of DSM-IV Autistic Disorder based on the following information: review of the patient’s symptoms, medical history and assessment data, observation (e.g., videotapes of Autism Diagnostic Observation Schedule-G (ADOS-G); and a physical exam. We exclude cases with: 1) medical conditions thought to be associated with autism (i.e., neurofibromatosis, tuberous sclerosis, PKU, and Fragile X Syndrome (FraX)); and, 2) gross CNS injury (e.g., Cerebral Palsy). Subjects are also excluded from the final analyses (and the longitudinal study and MRI scan at time 2) if they do not meet ADI-R criteria for autism at age 42-66 months or if they develop any of the exclusion criteria in the interval.

'''Inclusion/Exclusion Criteria for Controls:''' Controls are excluded at time 1 and 2 if they have evidence of a pervasive developmental disorder, a history of a neurological condition including CNS injury (e.g. Cerebral Palsy, severe closed head trauma) or significant perinatal suboptimality. Mentally retarded controls will be excluded if there is evidence of an identifiable or presumed etiology for their mental retardation (e.g., PKU, tuberous sclerosis), including Fragile X, a clear history of familial mental retardation, or if their mental retardation is part of a recognizable syndrome (e.g., Williams Syndrome).

'''MR Protocol:''' All scans are conducted on a 1.5 T Siemens scanner. Sequences include: a T1 weighted inversion recovery magnetization preparation (IR Prep) sequence with a slice thickness of 1.5 mms in the coronal plane, TE=5.4, TR=12.3, NEX=1, FOV=20 cm; flip angle = 40o; 256 X 192 matrix. PD/ T2 weighted fast spin-echo images will be acquired with the following parameters: 3.0 mm coronal slices, TE = 17/75, TR = 7200 ms, NEX = 1, FOV=20, 256 X 192 matrix. Tensor diffusion spin echo in the coronal and axial planes EPI, PSD-tensor, 4 shot, min TE, TR=12,000 ms, T1=2200, NEX=1, b value=1000, 128 X 64 matrix.

'''Assessment:''' For all the subjects with autism, diagnosis will be reviewed by interviewing the primary caretaker regarding the subject’s current and past behavior using the Autism Diagnostic Interview-Revised (ADI-R). The ADI-R is a semi-structured interview for autism. Items have been shown to be reliable and the accompanying algorithm adequately discriminates autistic individuals from mental-age matched non-autistic comparison subjects. Behavior: The Autism Diagnostic Observation Schedule -Generic (ADOS-G), is a semi-structured assessment of communication, social interaction and play or imaginative use of materials for individuals suspected of having autism or other pervasive developmental disorders (PDD). It will be administered to the subjects in the autism group. For purposes of this study, the ADOS-G will be used as a check on diagnoses from the ADI-R at both age periods and will provide additional information for the clinical diagnosis at age of entry. All controls will be screened (and excluded) for evidence of an autism spectrum disorder with the CARS. Cognitive: The Mullen Scales of Early Learning provide a single, reliable and valid instrument for estimation of IQ at both age periods in this study. At Time 2 cognitive development will also be assessed using the Differential Abilities Scale (DAS). Parents will also complete the Vineland Adaptive Behavior Scales with the clinician. This information will be incorporated into the clinical evaluation and the final judgment regarding diagnosis at entry. Language: All subjects will be administered the PreSchool Language Scale-4 (PLS-4) (at Time 1 and Time 2). The PLS-4 is a reliable and valid measure of receptive and expressive language from birth to 6 years of age. The MacArthur CDI Short Form Vocabulary Checklist (parent questionnaire) will be given at Time 1 and 2 to assess vocabulary use. Atypical Behavior: Behavior will be further characterized at Time 2 using the Aberrant Behavior Checklist (ABC), Repetitive Behavior Scale-Revised, Child Behavior Checklist (parent and teacher), and the Conners Parent and Teacher Rating Scales. These forms are parent questionnaires that report problem behaviors, such as hyperactivity and stereotyped behavior. Excellent convergence has also been shown between the CBCL and a number of discrete psychiatric syndromes demonstrating its usefulness as a rapid and useful screening instrument for selected psychiatric conditions. Use of this instrument will provide further descriptive information regarding the controls in this study. The Sensory Processing Assessment for Young Children (SPA) is a play-based observational assessment to be given at Time 1 to judge the child’s approach or avoidance to novel sensory toys, orientation and habituation to social and non-social sensory stimuli, and generation of novel action strategies with toys. The Sensory Experiences Questionnaire (SSQ) is a 25-item Likert-type parent questionnaire that asks about a child’s responses to various sensory stimuli (e.g., startling to loud noises, avoiding messy textures, etc.) in the context of functional daily activities. This will be given at T1 and T2. The SPA will be performed with the autism cases only, while the SSQ will be administered to all subjects. Physical Examination: A standardized physical examination will include examination for neurological abnormalities (asymmetries of the motor exam, hypotonia and reflexes), neurocutaneous abnormalities (including woods lamp examination) and dysmorphic features, and standardized measurement of head circumference and height. The ABC will be administered to controls, again as a way of obtaining further behavioral information on this sample. Similarly, data from the Connors Scales will provide comparable measures of attention, hyperactivity and impulsivity in both the case and control groups. Physical Exam: A standardized physical examination (including woods lamp) at entry and time 2 will include examination for neurological abnormalities (asymmetries of the motor exam, hypotonia and reflexes), neurocutaneous abnormalities (associated with CNS abnormalities such as tuberous sclerosis and neurofibromatosis), dysmorphic features (suggestive of a possible syndrome) and, standardized measurement of head circumference and height. In addition, handedness will be assessed at time 2, using a modification of the Edinborough Handedness Questionnaire. Laboratory: All autism and developmentally delayed children entering this study will have a Fragile X test (if not previously performed) to evaluate for any evidence of this chromosomal abnormality that will lead to exclusion from the study. DNA is collected at this time for autism and DD cases, and salivary DNA is collected from typical controls. Additional parent/family information: the Wechsler Abbreviated Scale of Intelligence (WASI) Verbal Scale only, Symptom Checklist-90-Revised, Family Economic/Educational Status, and the Family Environment Scale (FES) are used to obtain estimates of parental IQ, SES, and family functioning.

To date we have successfully scanned (i.e., usable MRIs) the following subjects:

Group Time1 Time2 (Follow-ups)
Autism 56 28
DD 10 6
TYP 18 8

Note that these cases have the behavioral and clinical assessment data described above in addition to their MRI data. Also, we are actively conducting follow-up (time 2) scans so these totals continue to increase.

===Image Processing Needs===
There is a lack of appropriate tools for processing of pediatric MRI and DTI and our UNC lab has had to develop specific tools for this purpose. Pediatric datasets present a challenging since pediatric MRI differs significantly from adult MRI due to variable brain shape and the process of maturation (i.e., myelination) which is reflected not only in nonlinear shape/volume changes but also regional change of white matter. Working with a toolkit such as Slicer would have a large benefit to our group. Access to the UNC longitudinal pediatric MRI data can spawn interesting new software methodology developments from Core-1. Besides existing multi-modal MRI data, the UNC group already has a very large set of segmented data (subcortical structures measured with very high reliability (0.92 up to 0.99) for over 140 MRI data sets (hippocampus, amygdale, putamen, globus pallidus, caudate, ventricles) - to our knowledge the largest segmentation database of such high quality. These data could be used for shape analysis of growth trajectory and can also serve as a benchmark for novel semi-automated processing. Our group has experience with the development of segmentation tools (http://www.psychiatry.unc.edu/autismresearch/MRI_PAGE.htm) and the design of large-scale validation of segmentation methodology (see Yushkevich et al., 2006, NeuroImage, (http://dx.doi.org/10.1016/j.neuroimage.2006.01.015). Moreover, the group’s experience with state-of-the-art ITK/vtk processing tools will help to critically assess and improve the NA-MIC toolkit’s development from the viewpoint of users involved in large clinical studies.

Specific image analysis projects where we envision NA-MIC supporting our group include the following:

(1) Automated Segmentation. While we already have segmented data of many brain substructures, but only a handful of these are automated. The remaining protocols represent manual tracing projects. We would benefit from the development of semi-automated or fully automated segmentation tools for these structures. Given that we already have excellent reliability in the protocols we currently employ, our data could be useful to NA-MIC as a unique training, testing or validation dataset.

(2) Cortical Thickness. We currently have no means to perform localized cortical thickness analysis. Our existing method only provides a global or regional (based on cortical lobes) means to examine our data. Our project would benefit from NA-MIC support in that we could examine group differences in specific cortical regions of interest. The ability to study changes in cortical thickness over time, and between groups, would also be a needed area of support.

(3) DTI atlas matching. We currently have a tool that allows us to do automatic region based DTI analysis. However, we are restricted to white matter tracts that we have defined. With support from NA-MIC, we could add to our ability to examine differences in white matter between cases and controls by the development of an enhanced atlas-based tool that could allow us to do both regional and localized full brain DTI analysis.

(4) Shape analysis. Our existing shape analysis tools do not allow us to do quantitatively examine shape differences over time in correlation with clinical variables of interest (such as IQ, gender, and age). The ability to study shape changes in substructures along with the influence of clinical and/or behavioral differences in cases versus controls would be extremely valuable.

===Current Image Processing===

A number of image processing tools have been created during the course of this study to assist in the image processing for this grant. These methods were created by our colleague’s (Dr. Guido Gerig) image processing lab at UNC through direct support from this project. The tools include:

1. Imagine – a tool used to create image processing pipelines

2. Intensity Rescaler – performs intensity windowing to make tissue values in two images the same range (e.g., class matching program)

3. ImConvert – image converter to change images from one format to another

4. Atlas Builder – generate average atlas, obtain deformation fields for individual images

5. Circumference – to obtain circumference of perimeter labels (e.g. brain mask)

6. DTI Checker – to check quality, correct minor alignment problems, remove slices with artifact

7. FiberTracking - for tractography of Diffusion Tensor data (DTI)

8. FiberViewer - for quantitative analysis of fibers

9. CC Segmenter – an automatic corpus callosum segementation and parcellation tool. Currently segements the corpus callosum into 4 regions associated with cortical lobes.

10. DicomImsel, DicomConvert, Xmedcon – other image conversion tools

11. Substructure shape analysis – tools for conducting shape analysis of the hippocampus and caudate.

These tools work successfully to assist in the processing of our 2 and 4 year old image datasets. However, the generation of automated pipelines incorporating these tools would be advantageous. This would decrease labor needed and potential ‘user error” and increase productivity in our lab.

Current limitations of some tools, such as shape processing of hippocampus and caudate, include the inability to correlate shape with our clinical data and interpret this in a quantitative way. For example, we would be interested in examining longitudinal shape changes in relation to cognitive ability (IQ) and variables related to severity of autism (e.g., presence or absence of ritualistic repetitive behaviors).

===Plans for the NA-MIC kit===
The processing of our relatively large pediatric database will benefit from the development of highly automated processing “pipelines” (i.e. co-registration of multi-modal data, atlas-to-template registration, automatic tissue segmentation, lobe parcellation, MRI-DTI registration, ROI analysis) that can generate quantitative data used in statistical analyses. This dataset therefore would be an excellent testing ground for the automated 3D Slicer version 3.
Plans to develop pipelines for growth-rate analysis might include new methods for longitudinal image analysis, cortical thickness and cortical folding pattern analysis, methods not yet developed for the NA-MIC toolkit but required for human brain studies. Additional projects would include a more regionally defined DTI analysis where properties of specific regions of interest could be measured. Automating the DTI processing pipeline currently used by our lab would also be desirable. Finally, the development of new segmentation protocols for specific cortical regions, such as the dorsolateral prefrontal cortex, would also advance the goals of this project.
As part of this effort, we would support a computer science engineer who will be responsible for communicating with NA-MIC developers and applying new tools to our data. We also have the support of image processing research assistants in our lab to assist with the work of this CS engineer. We anticipate efficient and timely communication between our lab and NA-MIC so that new tools are being tested in a collaborative working relationship.

===Future Directions===

We are in the process of submitting new grant proposals to conduct follow-up assessments and scans on the sample described in this proposal (2 and 4 year olds with autism, DD, and TYP). This will provide a third longitudinal data point between ages 6-8 years old for this existing sample. For this endeavor, we will require new tools to perform automated segmentation (e.g., brain tissue, substructures) of the MRI scans of the 6-8 year olds, as well as the creation of new tools to perform longitudinal data analysis (e.g., shape, cortical thickness) from the 2 to 8 year old age range.

===VII. Summary===

The UNC autism research group will benefit from access to NA-MIC tools and support from the NA-MIC group. This will significantly expand our image processing capabilities and will allow our team to do research within a larger team of leading image analysis research groups. NA-MIC will benefit from our unique longitudinal dataset of structural and DTI data. Our site will work with NA-MIC to produce image processing tools that (1) are applicable for pediatric datasets, (2) address the need to examine longitudinal brain development, (3) automate pipelines for image processing, and (3) incorporate existing tools into the NA-MIC toolkit.

DBP2:UNC

2007-05-18T17:00:30Z

Hazlett: /* Team and Institute */

Back to [[DBP2]]

* '''Title:''' Longitudinal MRI study of early brain development in neuropsychiatric disorder: UNC Autism Study

==Team and Institute==
*Co-PI: Heather Cody Hazlett, PhD, (heather_cody at med.unc.edu, Ph: 919-966-4099)
*Co-PI: Joseph Piven, MD
*NA-MIC Engineering Contact: Jim Miller, GE Research
*NA-MIC Algorithms Contact: Martin Styner, UNC

* '''Affiliation/Institution:''' UNC Chapel Hill, Department of Psychiatry and the Neruodevelopmental Disorders Research Center NDRC

* '''Science:''' Multiple lines of converging evidence (from MRI, post-mortem and head circumference studies) indicate that brain enlargement in autism is a real phenomenon. However, the onset, trajectory and pattern of this enlargement (in brain tissues, regions and structures), relationship to developing neural circuitry and clinical features; and, pathogenesis, are not yet clear. Results from our longitudinal MRI study of brain development (2 years with follow-up at 4 years) demonstrate robust generalized enlargement of white and gray matter volume in cerebral cortex in autistic individuals (N= 51) by age 2 yrs. The MRI and earlier head circumference data strongly suggest a period of substantial brain overgrowth in autistic individuals between age 12 and 24 months, continuing into ages 4 and 5. This study will provide critical information about the trajectory of brain growth (regions, tissues, structures and fiber tracts) as measured on MRI and DTI, the potential relationship to clinical features; and underlying genetic etiology. These results will provide important insights into developmental brain and behavioral phenotypes, and neurobiological mechanisms in autism.

* ''' Benefits to NA-MIC'''<nowiki>: There is a lack of appropriate tools for processing of pediatric MRI, a challenging topic since pediatric MRI differs significantly from adult MRI due to variable brain shape and the process of maturation/myelination which are reflected in nonlinear shape/volume changes but also regional change of white matter:. Working on a toolkit for the community would have a large impact, in particular also in view of existing and soon to be available databases of normative pediatric MRI (PI Alan Evans). Access to the UNC longitudinal pediatric MRI data representing a period of moderate but significant brain growth can spawn off interesting new software methodology developments from Core-1. Besides existing multi-modal MRI data, the UNC group has a very large set of segmented data (subcortical structures measured with very high reliability (0.92 up to 0.99) for over 140 MRI data sets(hippocampus, amygdale, putamen, pallide globe, caudate, ventricles) - to our knowledge the largest segmentation database of such high quality. These data could be used for shape analysis of growth trajectory and can also serve as a benchmark for novel semi-automated processing. The group has profound experience with the development of novel segmentation protocols (</nowiki>http://www.psychiatry.unc.edu/autismresearch/MRI_PAGE.htm) and the design of large-scale validation of segmentation methodology (see Yushkevich et al., 2006, NeuroImage, http://dx.doi.org/10.1016/j.neuroimage.2006.01.015). Moreover, the groups experience with state-of-the-art ITK/vtk processing tools will help to critically assess and improve the NA-MIC toolkit’s development from the viewpoint of users involved in large clinical studies. The processing of a relatively large database needs highly automated processing “pipelines”, i.e. co-registration of multi-modal data, atlas-to-template registration, automatic tissue segmentation, lobe parcellation, MRI-DTI registration, ROI analysis, and statistical analysis. This data therefore would be an excellent testbed for new automated Slicer 3 processing. A growth-rate analysis might have to include new methods for longitudinal image analysis, cortical thickness and cortical folding pattern analysis, methods not yet developed for the NA-MIC toolkit but required for human brain studies.

* ''' Benefits to UNC NDRC group: ''' The UNC autism research group will have access to NAMIC tools not yet available for analysis, which will expose them to new tools and procedures beyond the ones locally developed. This will significantly expand their processing capabilities but also will allow them to do research within a larger team of leading image analysis research groups. New tools applied to the existing longitdudinal autism pediatric study, including raw image data and processed anatomical structures, are most likely lead to publications demonstrating the processing capabilities and versatility of the NAMIC toolkit.
Title: Longitudinal MRI study of early brain development in neuropsychiatric disorder: UNC Autism Study
Name of the PI: Heather Cody Hazlett, PhD (Co-PI Joseph Piven, MD)
Affiliation/Institution: UNC Chapel Hill, Department of Psychiatry and the Neruodevelopmental Disorders Research Center NDRC
I. Research Goals
Multiple lines of converging evidence (from MRI, post-mortem and head circumference studies) indicate that brain enlargement in autism is a real phenomenon. However, the onset, trajectory and pattern of this enlargement (in brain tissues, regions and structures), relationship to developing neural circuitry and clinical features; and, pathogenesis, are not yet clear. Results from our longitudinal MRI study of brain development (2 years with follow-up at 4 years) demonstrate robust generalized enlargement of white and gray matter volume in cerebral cortex in autistic individuals (N= 51) by age 2 years. The MRI and earlier head circumference data strongly suggest a period of substantial brain overgrowth in autistic individuals between age 12 and 24 months, continuing into ages 4 and 5. This study will provide critical information about the trajectory of brain growth (regions, tissues, structures and fiber tracts) as measured on structural MRI and DTI, the potential relationship to clinical features; and underlying genetic etiology. These results will provide important insights into developmental brain and behavioral phenotypes, and neurobiological mechanisms in autism.
Our project has the following specific aims:
1) To characterize the pattern of brain size and development at two cross-sectional time periods (ages 18-35 and 42-66 months) in autism and in comparison to controls (children with developmental delay and typical development).

2) To examine the cross-sectional and longitudinal relationships between selected morphological brain features and the pattern of selected cognitive characteristics and behavioral abnormalities reported to be abnormal in autism.

II. Description of Data

Subjects: Participants in this study include children with autism and controls. Controls include children with developmental delay (DD) and typical development (TYP). The initial age at entry for this longitudinal study (time point 1) is between 18 and 35 months. At this time a clinical and behavioral assessment (see below) and MRI scan is performed. A follow-up visit is conducted when subjects are between 42 and 66 months of age (approximately 24 months after their initial visit). At this time (time point 2) a repeat assessment and MRI scan is performed.

Inclusion/Exclusion Criteria for subjects with Autism: Subjects are eligible to enter the study at time 1 if they are between 18 and 35 months of age, and receive a clinical diagnosis of DSM-IV Autistic Disorder based on the following information: review of the patient’s symptoms, medical history and assessment data, observation (e.g., videotapes of Autism Diagnostic Observation Schedule-G (ADOS-G); and a physical exam. We exclude cases with: 1) medical conditions thought to be associated with autism (i.e., neurofibromatosis, tuberous sclerosis, PKU, and Fragile X Syndrome (FraX)); and, 2) gross CNS injury (e.g., Cerebral Palsy). Subjects are also excluded from the final analyses (and the longitudinal study and MRI scan at time 2) if they do not meet ADI-R criteria for autism at age 42-66 months or if they develop any of the exclusion criteria in the interval.

Inclusion/Exclusion Criteria for Controls: Controls are excluded at time 1 and 2 if they have evidence of a pervasive developmental disorder, a history of a neurological condition including CNS injury (e.g. Cerebral Palsy, severe closed head trauma) or significant perinatal suboptimality. Mentally retarded controls will be excluded if there is evidence of an identifiable or presumed etiology for their mental retardation (e.g., PKU, tuberous sclerosis), including Fragile X, a clear history of familial mental retardation, or if their mental retardation is part of a recognizable syndrome (e.g., Williams Syndrome).

MR Protocol: All scans are conducted on a 1.5 T Siemens scanner. Sequences include: a T1 weighted inversion recovery magnetization preparation (IR Prep) sequence with a slice thickness of 1.5 mms in the coronal plane, TE=5.4, TR=12.3, NEX=1, FOV=20 cm; flip angle = 40o; 256 X 192 matrix. PD/ T2 weighted fast spin-echo images will be acquired with the following parameters: 3.0 mm coronal slices, TE = 17/75, TR = 7200 ms, NEX = 1, FOV=20, 256 X 192 matrix. Tensor diffusion spin echo in the coronal and axial planes EPI, PSD-tensor, 4 shot, min TE, TR=12,000 ms, T1=2200, NEX=1, b value=1000, 128 X 64 matrix.

Assessment: For all the subjects with autism, diagnosis will be reviewed by interviewing the primary caretaker regarding the subject’s current and past behavior using the Autism Diagnostic Interview-Revised (ADI-R). The ADI-R is a semi-structured interview for autism. Items have been shown to be reliable and the accompanying algorithm adequately discriminates autistic individuals from mental-age matched non-autistic comparison subjects. Behavior: The Autism Diagnostic Observation Schedule -Generic (ADOS-G), is a semi-structured assessment of communication, social interaction and play or imaginative use of materials for individuals suspected of having autism or other pervasive developmental disorders (PDD). It will be administered to the subjects in the autism group. For purposes of this study, the ADOS-G will be used as a check on diagnoses from the ADI-R at both age periods and will provide additional information for the clinical diagnosis at age of entry. All controls will be screened (and excluded) for evidence of an autism spectrum disorder with the CARS. Cognitive: The Mullen Scales of Early Learning provide a single, reliable and valid instrument for estimation of IQ at both age periods in this study. At Time 2 cognitive development will also be assessed using the Differential Abilities Scale (DAS). Parents will also complete the Vineland Adaptive Behavior Scales with the clinician. This information will be incorporated into the clinical evaluation and the final judgment regarding diagnosis at entry. Language: All subjects will be administered the PreSchool Language Scale-4 (PLS-4) (at Time 1 and Time 2). The PLS-4 is a reliable and valid measure of receptive and expressive language from birth to 6 years of age. The MacArthur CDI Short Form Vocabulary Checklist (parent questionnaire) will be given at Time 1 and 2 to assess vocabulary use. Atypical Behavior: Behavior will be further characterized at Time 2 using the Aberrant Behavior Checklist (ABC), Repetitive Behavior Scale-Revised, Child Behavior Checklist (parent and teacher), and the Conners Parent and Teacher Rating Scales. These forms are parent questionnaires that report problem behaviors, such as hyperactivity and stereotyped behavior. Excellent convergence has also been shown between the CBCL and a number of discrete psychiatric syndromes demonstrating its usefulness as a rapid and useful screening instrument for selected psychiatric conditions. Use of this instrument will provide further descriptive information regarding the controls in this study. The Sensory Processing Assessment for Young Children (SPA) is a play-based observational assessment to be given at Time 1 to judge the child’s approach or avoidance to novel sensory toys, orientation and habituation to social and non-social sensory stimuli, and generation of novel action strategies with toys. The Sensory Experiences Questionnaire (SSQ) is a 25-item Likert-type parent questionnaire that asks about a child’s responses to various sensory stimuli (e.g., startling to loud noises, avoiding messy textures, etc.) in the context of functional daily activities. This will be given at T1 and T2. The SPA will be performed with the autism cases only, while the SSQ will be administered to all subjects. Physical Examination: A standardized physical examination will include examination for neurological abnormalities (asymmetries of the motor exam, hypotonia and reflexes), neurocutaneous abnormalities (including woods lamp examination) and dysmorphic features, and standardized measurement of head circumference and height. The ABC will be administered to controls, again as a way of obtaining further behavioral information on this sample. Similarly, data from the Connors Scales will provide comparable measures of attention, hyperactivity and impulsivity in both the case and control groups. Physical Exam: A standardized physical examination (including woods lamp) at entry and time 2 will include examination for neurological abnormalities (asymmetries of the motor exam, hypotonia and reflexes), neurocutaneous abnormalities (associated with CNS abnormalities such as tuberous sclerosis and neurofibromatosis), dysmorphic features (suggestive of a possible syndrome) and, standardized measurement of head circumference and height. In addition, handedness will be assessed at time 2, using a modification of the Edinborough Handedness Questionnaire. Laboratory: All autism and developmentally delayed children entering this study will have a Fragile X test (if not previously performed) to evaluate for any evidence of this chromosomal abnormality that will lead to exclusion from the study. DNA is collected at this time for autism and DD cases, and salivary DNA is collected from typical controls. Additional parent/family information: the Wechsler Abbreviated Scale of Intelligence (WASI) Verbal Scale only, Symptom Checklist-90-Revised, Family Economic/Educational Status, and the Family Environment Scale (FES) are used to obtain estimates of parental IQ, SES, and family functioning.

To date we have successfully scanned (i.e., usable MRIs) the following subjects:

Group Time1 Time2 (Follow-ups)
Autism 56 28
DD 10 6
TYP 18 8

Note that these cases have the behavioral and clinical assessment data described above in addition to their MRI data. Also, we are actively conducting follow-up (time 2) scans so these totals continue to increase.

III. Image Processing Needs
There is a lack of appropriate tools for processing of pediatric MRI and DTI and our UNC lab has had to develop specific tools for this purpose. Pediatric datasets present a challenging since pediatric MRI differs significantly from adult MRI due to variable brain shape and the process of maturation (i.e., myelination) which is reflected not only in nonlinear shape/volume changes but also regional change of white matter. Working with a toolkit such as Slicer would have a large benefit to our group. Access to the UNC longitudinal pediatric MRI data can spawn interesting new software methodology developments from Core-1. Besides existing multi-modal MRI data, the UNC group already has a very large set of segmented data (subcortical structures measured with very high reliability (0.92 up to 0.99) for over 140 MRI data sets (hippocampus, amygdale, putamen, globus pallidus, caudate, ventricles) - to our knowledge the largest segmentation database of such high quality. These data could be used for shape analysis of growth trajectory and can also serve as a benchmark for novel semi-automated processing. Our group has experience with the development of segmentation tools (http://www.psychiatry.unc.edu/autismresearch/MRI_PAGE.htm) and the design of large-scale validation of segmentation methodology (see Yushkevich et al., 2006, NeuroImage, (http://dx.doi.org/10.1016/j.neuroimage.2006.01.015). Moreover, the group’s experience with state-of-the-art ITK/vtk processing tools will help to critically assess and improve the NA-MIC toolkit’s development from the viewpoint of users involved in large clinical studies.
Specific image analysis projects where we envision NA-MIC supporting our group include the following:
(1) Automated Segmentation. While we already have segmented data of many brain substructures, but only a handful of these are automated. The remaining protocols represent manual tracing projects. We would benefit from the development of semi-automated or fully automated segmentation tools for these structures. Given that we already have excellent reliability in the protocols we currently employ, our data could be useful to NA-MIC as a unique training, testing or validation dataset.
(2) Cortical Thickness. We currently have no means to perform localized cortical thickness analysis. Our existing method only provides a global or regional (based on cortical lobes) means to examine our data. Our project would benefit from NA-MIC support in that we could examine group differences in specific cortical regions of interest. The ability to study changes in cortical thickness over time, and between groups, would also be a needed area of support.
(3) DTI atlas matching. We currently have a tool that allows us to do automatic region based DTI analysis. However, we are restricted to white matter tracts that we have defined. With support from NA-MIC, we could add to our ability to examine differences in white matter between cases and controls by the development of an enhanced atlas-based tool that could allow us to do both regional and localized full brain DTI analysis.
(4) Shape analysis. Our existing shape analysis tools do not allow us to do quantitatively examine shape differences over time in correlation with clinical variables of interest (such as IQ, gender, and age). The ability to study shape changes in substructures along with the influence of clinical and/or behavioral differences in cases versus controls would be extremely valuable.
IV. Current Image Processing

A number of image processing tools have been created during the course of this study to assist in the image processing for this grant. These methods were created by our colleague’s (Dr. Guido Gerig) image processing lab at UNC through direct support from this project. The tools include:

1. Imagine – a tool used to create image processing pipelines
2. Intensity Rescaler – performs intensity windowing to make tissue values in two images the same range (e.g., class matching program)
3. ImConvert – image converter to change images from one format to another
4. Atlas Builder – generate average atlas, obtain deformation fields for individual images
5. Circumference – to obtain circumference of perimeter labels (e.g. brain mask)
6. DTI Checker – to check quality, correct minor alignment problems, remove slices with artifact
7. FiberTracking - for tractography of Diffusion Tensor data (DTI)
8. FiberViewer - for quantitative analysis of fibers
9. CC Segmenter – an automatic corpus callosum segementation and parcellation tool. Currently segements the corpus callosum into 4 regions associated with cortical lobes.
10. DicomImsel, DicomConvert, Xmedcon – other image conversion tools
11. Substructure shape analysis – tools for conducting shape analysis of the hippocampus and caudate.

These tools work successfully to assist in the processing of our 2 and 4 year old image datasets. However, the generation of automated pipelines incorporating these tools would be advantageous. This would decrease labor needed and potential ‘user error” and increase productivity in our lab.

Current limitations of some tools, such as shape processing of hippocampus and caudate, include the inability to correlate shape with our clinical data and interpret this in a quantitative way. For example, we would be interested in examining longitudinal shape changes in relation to cognitive ability (IQ) and variables related to severity of autism (e.g., presence or absence of ritualistic repetitive behaviors).

V. Plans for the NA-MIC kit
The processing of our relatively large pediatric database will benefit from the development of highly automated processing “pipelines” (i.e. co-registration of multi-modal data, atlas-to-template registration, automatic tissue segmentation, lobe parcellation, MRI-DTI registration, ROI analysis) that can generate quantitative data used in statistical analyses. This dataset therefore would be an excellent testing ground for the automated 3D Slicer version 3.
Plans to develop pipelines for growth-rate analysis might include new methods for longitudinal image analysis, cortical thickness and cortical folding pattern analysis, methods not yet developed for the NA-MIC toolkit but required for human brain studies. Additional projects would include a more regionally defined DTI analysis where properties of specific regions of interest could be measured. Automating the DTI processing pipeline currently used by our lab would also be desirable. Finally, the development of new segmentation protocols for specific cortical regions, such as the dorsolateral prefrontal cortex, would also advance the goals of this project.
As part of this effort, we would support a computer science engineer who will be responsible for communicating with NA-MIC developers and applying new tools to our data. We also have the support of image processing research assistants in our lab to assist with the work of this CS engineer. We anticipate efficient and timely communication between our lab and NA-MIC so that new tools are being tested in a collaborative working relationship.

VI. Future Directions

We are in the process of submitting new grant proposals to conduct follow-up assessments and scans on the sample described in this proposal (2 and 4 year olds with autism, DD, and TYP). This will provide a third longitudinal data point between ages 6-8 years old for this existing sample. For this endeavor, we will require new tools to perform automated segmentation (e.g., brain tissue, substructures) of the MRI scans of the 6-8 year olds, as well as the creation of new tools to perform longitudinal data analysis (e.g., shape, cortical thickness) from the 2 to 8 year old age range.

VII. Summary

The UNC autism research group will benefit from access to NA-MIC tools and support from the NA-MIC group. This will significantly expand our image processing capabilities and will allow our team to do research within a larger team of leading image analysis research groups. NA-MIC will benefit from our unique longitudinal dataset of structural and DTI data. Our site will work with NA-MIC to produce image processing tools that (1) are applicable for pediatric datasets, (2) address the need to examine longitudinal brain development, (3) automate pipelines for image processing, and (3) incorporate existing tools into the NA-MIC toolkit.

2007 Core 1 Meeting

2007-05-18T16:58:30Z

Hazlett: /* Attendees */

== Agenda ==

*May 30 -
** 8:30am Remarks (Whitaker)
*** Introductions
*** Discuss goals and agenda
** 9:00am Driving Biological problems '''[[2007_Core_1_Meeting_DBP_Guidlines|(Guidelines for talks)]]'''
*** Marek Kubicki - Harvard/B&W
*** Gabor Fichtinger - Johns Hopkins
*** (break)
*** H. Jeremy Bockholt - Mind Institute
*** Heather Cody - UNC
**12:00pm Lunch
** 1:00pm Invited Talk: Daniel Rueckert. "Quantification of brain development during early childhood using non-rigid registration". ([http://www.csail.mit.edu/events/eventcalendar/calendar.php?show=event&id=1484 talk announcement])
** 2:00pm Break out sessions with DPBs
** 3:30pm Break
** 3:45-6:00pm Core 1 presentations
*** John Melonakos (Georgia Tech) - TBD
*** MGH (name)
*** MIT (name)
*** UNC (name)
*** Tom Fletcher (Utah), "Volumetric Connectivity for analysis of DTI"
*May 31
** 8:30am Core 1 presentations
*** Utah "Nonparametric Shape Modeling of Object Complexes" (Speaker TBD)
*** UNC (name)
*** (break)
*** Mert Sabuncu (MIT), "Population Analysis of Anatomical Variability"
*** MGH (name)
*** John Melonakos (Georgia Tech) - TBD
** 12:30pm Lunch
** 1pm Strategic planning, software
*** Progress/interactions with DBPs
*** Software contributions and Core 2 interactions
*** Performance assessment and renewal
** 5pm Finish

== Logistics ==

'''Dates:''' May 30-31, 2007.

Please plan to travel on May 29, so that we can start in the morning of May 30.
The meeting will be two full days; please plan to stay until 5pm on May31.

'''Location:''' MIT.

The meeting will take place in the Kiva conference room, on the 4th floor of Stata Center. We will post signs in the building, directing you to the conference room.

'''Food:''' Breakfast and lunch will be provided on May 30-31. We will also organize people into groups to go out for dinner on May 30. There are plenty of restaurants around Central Square and MIT.

'''Hotel:''' There is no official hotel for the meeting. Here is some information about Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Please book your rooms early.

== Attendees ==

* Ross Whitaker
* Polina Golland (MIT)
* Ron Kikinis
* Guido Gerig(UNC)
* Heather Cody Hazlett (UNC Autism DBP)
* Allen Tannenbaum
* John Melonakos
* H. Jeremy Bockholt (MIND/UNM)
* Harvard Core 3: Marek Kubicki
* Mert R Sabuncu (MIT CSAIL)
* Lilla Zollei (NMR, MGH)
* Serdar K Balci (MIT CSAIL)
* Ulas Ziyan (MIT)
* Tom Fletcher (Utah)
* Josh Cates (Utah - tentative)
* Gabor Fichtinger (JHU)
* Jim Miller (GE Research)
* Dennis Jen
* Sandy Wells (BWH, MIT)

2007 Programming/Project Week MIT

2007-05-18T16:43:52Z

Hazlett: /* Attendee List */

Back to [[Engineering:Programming_Events|Programming/Project Events]]

== Logistics ==

'''Dates:''' June 25-29, 2007

'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].

'''Registration Fee:''' $200 (this will cover the cost of breakfast and lunch for the week). Due by Tuesday, June 19, 2007. Please make checks out to "Massachusetts Institute of Technology" and mail to:
Donna Kaufman, MIT, 77 Massachusetts Ave., 38-409b, Cambridge, MA 02139

If you are attending for one day only, the registration fee is not required.

'''Hotel:''' There is no official hotel for the meeting. Here is some information about Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Summer is tourist season in Boston, so please book your rooms early.

([[Project Week Logistics Checklist|This is a checklist for the onsite planning items]])

===Introduction to NA-MIC Project Week===

This is a week of hands on activity -- programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, and clinical application -- that has become one of the major events in the NA-MIC calendar. A full week of hands on activities is held in the summer at MIT (typically the last week of June), and for half a week in Salt Lake City in the winter (typically the second week of January).
The main goal of these events if to move forward the deliverables of NA-MIC. NA-MIC participants and their collaborators are welcome to attend.

* NA-MIC Members: Participation in this event is voluntary -- if you don't think this will help you move forward in your work, there is no obligation to attend.
* Ideal candidates are those who want to contribute to the NA-MIC Kit, and those who can help make it happen.
* This is not an introduction to the components of the NA-MIC Kit.
* NA-MIC Core 1 (Algorithms) - bring your algorithms and code to work on in the company of Core 2 engineers and Core 3 scientists.
* NA-MIC Core 2 (Engineering) - bring your code for infrastructure and applications to extend the NA-MIC Kit capabiliities, integrate Core 1 algorithms, and refine worflows for Core 3.
* NA-MIC Core 3 (DBP) - bring your data to work on with the NA-MIC Kit and get assistance and provide feedback to Core 1 scientists and Core 2 engineers.
* External Collaborators - if you are working on a project that uses the [[NA-MIC-Kit|NA-MIC kit]], and want to participate to get help from NA-MIC Engineering, please send an email to Tina Kapur (tkapur at bwh.harvard.edu). Please note that the event is open to people outside NA-MIC, subject to availability.
* Everyone should '''bring a laptop'''. We will have three or four projectors.
* About half the time will be spent working on projects and the other half in project related discussions.
* You '''do''' need to be actively working on a NA-MIC related project in order to make this investment worthwhile for everyone.

=== Draft Agenda===
Please note that this agenda is a draft and will be finalized by June 15th.

* Monday
** noon-1pm lunch
** 1-3:30pm Introduce Projects using 4-block slides (all Project Leads)
** 3:30-5:30pm Start project work
* Tuesday
** 8:30am breakfast
** 9-10am: NA-MIC Software Process (Bill Hoffman - TBC)
** 10-10:30am Slicer 3.0 Update(Jim Miller, Steve Pieper)
** 11-12pm: [[Special topic breakout: IGT for Prostate]]
** noon lunch
** 1-2pm: [[Special topic breakout: Non-Linear Registration]]
** 2-3pm: [[Special topic breakout: DWI/DTI]]
** 3-4pm: [[Special topic breakout: KWWidgets]]
** 4-5pm: [[Special topic breakout: Atlases]]
** 5:30pm adjourn for day
* Wednesday
** 8:30am breakfast
** noon lunch
** 1-2pm: Special topic breakout: [[NA-MIC:2007 Plan for Long-Lead Time Items| Plan for Long Lead Time Items]]
** 5:30pm adjourn for day
* Thursday
** 8:30am breakfast
** noon lunch
** 3:30-5pm Special topic breakout: TBD
** 5:30pm adjourn for day
* Friday
** 8:30am breakfast
** 10am-noon: Project Progress using 4-block slides
** noon lunch boxes and adjourn

=== Preparation ===

# Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-programming-week na-mic-programming-week mailing list]
# [[Engineering:TCON_2007#2007-05-03|May 3, 2007: Kickoff TCON#1 to discuss Engr Core Projects and Assign/Verify Teams]]
# [[Engineering:TCON_2007#2007-05-10|May 10, 2007: TCON#2 to discuss Projects and Assign/Verify Teams]]
# [[Engineering:TCON_2007#2007-05-17|May 17, 2007: TCON#3 to discuss outstanding projects and teams from previous week]]
# May 17, 2007: Create a Wiki page per project (the participants must do this, hopefully jointly)
# May 31, 2007: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Andy)
## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)
## Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Andy)
# By 3pm ET on June 21, 2007: Complete the top half of [[media:NA-MIC_Latest_4-Block_Template.ppt|this powerpoint template]] for each project. Upload and link to the right place.
# [[Engineering:TCON_2007#2005-06-21|June 21, 2007: TCON#4 Final Call before showtime...]]
# Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...

== Projects ==

===DBP II===
These are projects by the new set of DBPS:
*[[DBP2:MIND | Longitudinal Classification of White Matter Lesions in Lupus]] (MIND/UNM)
*[[DBP2:JHU | Segmentation and Registration Tools for Robotic Prostate Interventions]] (JHU/Queen's)
*[[DBP2:UNC |Longitudinal MRI study of early brain development in neuropsychiatric disorder: UNC Autism Study]] (UNC)
*[[DBP2:Harvard|Velocardiofacial Syndrome (VCFS) as a genetic model for schizophrenia]] (Harvard)

===Structural Analysis===
*EMSegmentation Validation (Brad Davis, Sylvain Bouix)
*vtkITK wrapper for rule based segmentation (John Melonakos, Brad Davis, Marek Kubicki)
** Application of the Slicer2 module on DBP data
** Conversion to Slicer3

===Diffusion Image Analysis===
* [[Algorithm:UNC:DTI#Population_Analysis | DTI population analysis]] (Casey Goodlett)
* Slicer3 Whole brain Seeding platform: data representation and pipeline execution (Raul San Jose, Lauren O'Donnell)
* Slicer3 Tractography editor (Lauren O'Donnell, Raul San Jose)

===NA-MIC Kit===
* Slicer3
** [[2007_Project_Week_MIT_QDEC_Slicer3_Integration | QDEC integration into Slicer3]] (Nicole Aucoin BWH, Kevin Teich MGH, Nick Schmansky MGH, Doug Greve MGH, Gheorghe Postelnicu MGH, Steve Pieper Isomics)
** Display Optimization (Raimundo Sierra, David Gobbi, Steve Pieper)
** [[2007_Project_Week_MIT_MRML_Scenes_for_the_Execution_Model]], including transforms (Jim Miller, Brad Davis, Nicole Aucoin, Alex Yarmarkovich, Steve Pieper)
** Support for Unstructured Grids (Steve, Nicole, Alex, Curt)
** Python support in Slicer3 (Luca, Steve, depends on Dan's availability)
** CPack, Ctest infrastructure improvements (Andy, Katie, Steve)
** Drafting Human Interface and Slicer Style Guidelines (Wendy)
** Slicer Matlab Pipeline for scalars and tensors (Katharina, Sylvain, Steve)
* Slicer2

===External Collaborations===
* Meshing
** Adding VTK interactive wwidgets to Slicer3 (Will, Vince, Kiran, Curt)
** Migrate Iowa Neural Net code to pure ITK (Vince, Stephen)
*IGT
** [[Tracker Integration]] (Noby, Haiying, Katie Hayes BWH)
** [[Collaboration/MGH/Radiation Therapy Radiation Therapy Planning]] (Greg Sharp MGH, Tina Kapur BWH, Sandy Wells BWH, Steve Pieper Isomics, Katie Hayes BWH)
** [[Collaboration/JHU/Brachytherapy needle positioning robot integration|Brachytherapy needle positioning robot integration]] (Csaba Csoma, Peter Kazanzides JHU, David Gobbi Queen's, Katie Hayes BWH)
** [[Collaboration/BWH/RadVision and Tracker Integration|RadVision and Tracker Integration]] (Jack Blevins, Noby)
* Registration
** [[Collaboration/UIowa/Non_Rigid_Registration|Implementing Non-rigid Image Registration and Evaluation Project (NIREP) software using NA-MIC Kit]] (Gary Christensen UIowa, Stephen Aylward, Kitware, Sandy Wells BWH)
** [[Collaboration/UIowa/Developing Electronic Atlas Software using NA-MIC Kit|Developing Electronic Atlas Software using NA-MIC Kit]] (Gary Christensen UIowa, Jeff Grethe)
** [[Collaboration/UIowa/Developing a GUI for non-rigid image registration programs using NA-MIC Kit|Developing a GUI for non-rigid image registration programs using NA-MIC Kit]] (Gary Christensen UIowa, Yumin Kitware)
* [[Collaboration/VMTK |vmtk (vmtk.sourceforge.net) integration within Slicer3]] (Luca Antiga, MNI, Dan Blezek(GE))
* [[Collaboration/NWU/Radiology Workstation| A Translation Station]](Skip, Alex, Vlad, Pat, Alex, Steve)
* [[Collaboration/WFU/NonHuman Primate Neuroimaging| Applying EMSegmenter to NonHuman Primate Neuroimaging]](Chris Wyatt VT, Kilian Pohl BWH)
* [[NA-MIC_NCBC_Collaboration:3D%2Bt_Cells_Lineage:GoFigure|3D+t Cells Lineage:GoFigure]] (Alex G, Yumin)

===Non-Medical Collaborations===
* AstroMed (Michael Halle, Douglas Alan)

==Attendee List==
#Kilian Pohl, BWH, Core 1
#John Melonakos, Georgia Tech, Core 1, (Hotel at MIT request)
#Tauseef Rehman, Georgia Tech, Core 1
#Casey Goodlett, UNC, Core 1, (Hotel at MIT request)
#W. Bryan Smith, UCSD/NCMIR, Core 2 (Tentative)
#Jim Miller, GE Core 2, Booked at the Hotel At MIT
#Steve Pieper, Isomics, Core 2
#Katie Hayes, BWH, Core 2
#Dan Blezek, GE Core 2, Booked at the Hotel At MIT
#Tina Kapur, BWH, Core 6
#Ron Kikinis, Core 7, PI
#Peter Kazanzides, JHU, Collaborator
#Wendy Plesniak, BWH, Collaborator
#Luca Antiga, Mario Negri Institute, Collaborator
#Sylvain Bouix, BWH, Core 3
#Marek Kubicki, BWH, Core 3
#Chris Wyatt, Virginia Tech, Collaborator
#Nicole Aucoin, BWH, Core 2
#Will Schroeder, Kitware, Core 2
#Yumin Yuan, Kitware, Core 2
#Brad Davis, Kitware, Core 2
#Stephen Aylward, Kitware, Collaborator
#Raimundo Sierra, BWH, Core 2
#Clare Tempany, BWH Collaborator (Tuesday, June 26th only)
#Noby Hata, BWH Collaborator (Monday, June 25th only)
#Haiying Liu, BWH Collaborator
#Alex Yarmarkovich, Isomics, Core 2
#Vincent Magnotta, University of Iowa, Collaborator (Hotel at MIT request)
#Hans Johnson, University of Iowa, Collaborator
#Gary E. Christensen, University of Iowa, Collaborator
#Joo Hyun (Paul) Song, University of Iowa, Gary's student
#Xiujuan Geng, University of Iowa, Gary's student
#Jake Nickel, University of Iowa, Gary's student
#Nick Kiguta, University of Iowa, Gary's student
#Kunlin Cao, University of Iowa, Gary's student
#James Harris, University of Iowa, Gary's student
#Rhiannon Carlson, University of Iowa, Gary's student
#Jeff Hawley, University of Iowa, Gary's student
#Skip Talbot, Northwestern University, Collaborator
#Alex Kogan, Northwestern University, Collaborator
#Vladimir Kleper, Northwestern University, Collaborator
#Pat Mongkolwat, Northwestern University, Collaborator
#Csaba Csoma, Johns Hopkins University, Collaborator
#David Gobbi, Queen's University, Collaborator (Hotel at MIT request)
#Kai Ding, University of Iowa, Gary's student
#H. Jeremy Bockholt, The MIND Institute, DBP2:MIND PI
#Mark Scully, The MIND Institute, DBP2:MIND software engineer
#Sumner Williams, The MIND Institute, Magnotta/Johnson/Bockholt BRAINS grant software engineer
#Greg Sharp, MGH, Collaborator
#Lauren O'Donnell, BWH
#Raul San Jose, BWH
#Katharina Quintus, BWH, Core 3
#Marc Niethammer, BWH. Core 3
#Kevin Teich, MGH
#Michael Halle, BWH/IIC
#James Ross, GE
#Kiran Shivanna, University of Iowa, Collaborator (Hotel at MIT request)
#Douglas Alan, Harvard IIC
#Heather Cody Hazlett, UNC Core 3 (only part of week) (Hotel @ MIT request)
#Clement , UNC Core 3 (CS programmer) - full week (Hotel @ MIT request)
#Rachel G. Smith, UNC Core 3 (image lab manager) - tentative (may not be full week) (Hotel @ MIT request)

2007 Programming/Project Week MIT

2007-05-18T16:42:29Z

Hazlett: /* Attendee List */

Back to [[Engineering:Programming_Events|Programming/Project Events]]

== Logistics ==

'''Dates:''' June 25-29, 2007

'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].

'''Registration Fee:''' $200 (this will cover the cost of breakfast and lunch for the week). Due by Tuesday, June 19, 2007. Please make checks out to "Massachusetts Institute of Technology" and mail to:
Donna Kaufman, MIT, 77 Massachusetts Ave., 38-409b, Cambridge, MA 02139

If you are attending for one day only, the registration fee is not required.

'''Hotel:''' There is no official hotel for the meeting. Here is some information about Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Summer is tourist season in Boston, so please book your rooms early.

([[Project Week Logistics Checklist|This is a checklist for the onsite planning items]])

===Introduction to NA-MIC Project Week===

This is a week of hands on activity -- programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, and clinical application -- that has become one of the major events in the NA-MIC calendar. A full week of hands on activities is held in the summer at MIT (typically the last week of June), and for half a week in Salt Lake City in the winter (typically the second week of January).
The main goal of these events if to move forward the deliverables of NA-MIC. NA-MIC participants and their collaborators are welcome to attend.

* NA-MIC Members: Participation in this event is voluntary -- if you don't think this will help you move forward in your work, there is no obligation to attend.
* Ideal candidates are those who want to contribute to the NA-MIC Kit, and those who can help make it happen.
* This is not an introduction to the components of the NA-MIC Kit.
* NA-MIC Core 1 (Algorithms) - bring your algorithms and code to work on in the company of Core 2 engineers and Core 3 scientists.
* NA-MIC Core 2 (Engineering) - bring your code for infrastructure and applications to extend the NA-MIC Kit capabiliities, integrate Core 1 algorithms, and refine worflows for Core 3.
* NA-MIC Core 3 (DBP) - bring your data to work on with the NA-MIC Kit and get assistance and provide feedback to Core 1 scientists and Core 2 engineers.
* External Collaborators - if you are working on a project that uses the [[NA-MIC-Kit|NA-MIC kit]], and want to participate to get help from NA-MIC Engineering, please send an email to Tina Kapur (tkapur at bwh.harvard.edu). Please note that the event is open to people outside NA-MIC, subject to availability.
* Everyone should '''bring a laptop'''. We will have three or four projectors.
* About half the time will be spent working on projects and the other half in project related discussions.
* You '''do''' need to be actively working on a NA-MIC related project in order to make this investment worthwhile for everyone.

=== Draft Agenda===
Please note that this agenda is a draft and will be finalized by June 15th.

* Monday
** noon-1pm lunch
** 1-3:30pm Introduce Projects using 4-block slides (all Project Leads)
** 3:30-5:30pm Start project work
* Tuesday
** 8:30am breakfast
** 9-10am: NA-MIC Software Process (Bill Hoffman - TBC)
** 10-10:30am Slicer 3.0 Update(Jim Miller, Steve Pieper)
** 11-12pm: [[Special topic breakout: IGT for Prostate]]
** noon lunch
** 1-2pm: [[Special topic breakout: Non-Linear Registration]]
** 2-3pm: [[Special topic breakout: DWI/DTI]]
** 3-4pm: [[Special topic breakout: KWWidgets]]
** 4-5pm: [[Special topic breakout: Atlases]]
** 5:30pm adjourn for day
* Wednesday
** 8:30am breakfast
** noon lunch
** 1-2pm: Special topic breakout: [[NA-MIC:2007 Plan for Long-Lead Time Items| Plan for Long Lead Time Items]]
** 5:30pm adjourn for day
* Thursday
** 8:30am breakfast
** noon lunch
** 3:30-5pm Special topic breakout: TBD
** 5:30pm adjourn for day
* Friday
** 8:30am breakfast
** 10am-noon: Project Progress using 4-block slides
** noon lunch boxes and adjourn

=== Preparation ===

# Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-programming-week na-mic-programming-week mailing list]
# [[Engineering:TCON_2007#2007-05-03|May 3, 2007: Kickoff TCON#1 to discuss Engr Core Projects and Assign/Verify Teams]]
# [[Engineering:TCON_2007#2007-05-10|May 10, 2007: TCON#2 to discuss Projects and Assign/Verify Teams]]
# [[Engineering:TCON_2007#2007-05-17|May 17, 2007: TCON#3 to discuss outstanding projects and teams from previous week]]
# May 17, 2007: Create a Wiki page per project (the participants must do this, hopefully jointly)
# May 31, 2007: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Andy)
## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)
## Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Andy)
# By 3pm ET on June 21, 2007: Complete the top half of [[media:NA-MIC_Latest_4-Block_Template.ppt|this powerpoint template]] for each project. Upload and link to the right place.
# [[Engineering:TCON_2007#2005-06-21|June 21, 2007: TCON#4 Final Call before showtime...]]
# Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...

== Projects ==

===DBP II===
These are projects by the new set of DBPS:
*[[DBP2:MIND | Longitudinal Classification of White Matter Lesions in Lupus]] (MIND/UNM)
*[[DBP2:JHU | Segmentation and Registration Tools for Robotic Prostate Interventions]] (JHU/Queen's)
*[[DBP2:UNC |Longitudinal MRI study of early brain development in neuropsychiatric disorder: UNC Autism Study]] (UNC)
*[[DBP2:Harvard|Velocardiofacial Syndrome (VCFS) as a genetic model for schizophrenia]] (Harvard)

===Structural Analysis===
*EMSegmentation Validation (Brad Davis, Sylvain Bouix)
*vtkITK wrapper for rule based segmentation (John Melonakos, Brad Davis, Marek Kubicki)
** Application of the Slicer2 module on DBP data
** Conversion to Slicer3

===Diffusion Image Analysis===
* [[Algorithm:UNC:DTI#Population_Analysis | DTI population analysis]] (Casey Goodlett)
* Slicer3 Whole brain Seeding platform: data representation and pipeline execution (Raul San Jose, Lauren O'Donnell)
* Slicer3 Tractography editor (Lauren O'Donnell, Raul San Jose)

===NA-MIC Kit===
* Slicer3
** [[2007_Project_Week_MIT_QDEC_Slicer3_Integration | QDEC integration into Slicer3]] (Nicole Aucoin BWH, Kevin Teich MGH, Nick Schmansky MGH, Doug Greve MGH, Gheorghe Postelnicu MGH, Steve Pieper Isomics)
** Display Optimization (Raimundo Sierra, David Gobbi, Steve Pieper)
** [[2007_Project_Week_MIT_MRML_Scenes_for_the_Execution_Model]], including transforms (Jim Miller, Brad Davis, Nicole Aucoin, Alex Yarmarkovich, Steve Pieper)
** Support for Unstructured Grids (Steve, Nicole, Alex, Curt)
** Python support in Slicer3 (Luca, Steve, depends on Dan's availability)
** CPack, Ctest infrastructure improvements (Andy, Katie, Steve)
** Drafting Human Interface and Slicer Style Guidelines (Wendy)
** Slicer Matlab Pipeline for scalars and tensors (Katharina, Sylvain, Steve)
* Slicer2

===External Collaborations===
* Meshing
** Adding VTK interactive wwidgets to Slicer3 (Will, Vince, Kiran, Curt)
** Migrate Iowa Neural Net code to pure ITK (Vince, Stephen)
*IGT
** [[Tracker Integration]] (Noby, Haiying, Katie Hayes BWH)
** [[Collaboration/MGH/Radiation Therapy Radiation Therapy Planning]] (Greg Sharp MGH, Tina Kapur BWH, Sandy Wells BWH, Steve Pieper Isomics, Katie Hayes BWH)
** [[Collaboration/JHU/Brachytherapy needle positioning robot integration|Brachytherapy needle positioning robot integration]] (Csaba Csoma, Peter Kazanzides JHU, David Gobbi Queen's, Katie Hayes BWH)
** [[Collaboration/BWH/RadVision and Tracker Integration|RadVision and Tracker Integration]] (Jack Blevins, Noby)
* Registration
** [[Collaboration/UIowa/Non_Rigid_Registration|Implementing Non-rigid Image Registration and Evaluation Project (NIREP) software using NA-MIC Kit]] (Gary Christensen UIowa, Stephen Aylward, Kitware, Sandy Wells BWH)
** [[Collaboration/UIowa/Developing Electronic Atlas Software using NA-MIC Kit|Developing Electronic Atlas Software using NA-MIC Kit]] (Gary Christensen UIowa, Jeff Grethe)
** [[Collaboration/UIowa/Developing a GUI for non-rigid image registration programs using NA-MIC Kit|Developing a GUI for non-rigid image registration programs using NA-MIC Kit]] (Gary Christensen UIowa, Yumin Kitware)
* [[Collaboration/VMTK |vmtk (vmtk.sourceforge.net) integration within Slicer3]] (Luca Antiga, MNI, Dan Blezek(GE))
* [[Collaboration/NWU/Radiology Workstation| A Translation Station]](Skip, Alex, Vlad, Pat, Alex, Steve)
* [[Collaboration/WFU/NonHuman Primate Neuroimaging| Applying EMSegmenter to NonHuman Primate Neuroimaging]](Chris Wyatt VT, Kilian Pohl BWH)
* [[NA-MIC_NCBC_Collaboration:3D%2Bt_Cells_Lineage:GoFigure|3D+t Cells Lineage:GoFigure]] (Alex G, Yumin)

===Non-Medical Collaborations===
* AstroMed (Michael Halle, Douglas Alan)

==Attendee List==
#Kilian Pohl, BWH, Core 1
#John Melonakos, Georgia Tech, Core 1, (Hotel at MIT request)
#Tauseef Rehman, Georgia Tech, Core 1
#Casey Goodlett, UNC, Core 1, (Hotel at MIT request)
#W. Bryan Smith, UCSD/NCMIR, Core 2 (Tentative)
#Jim Miller, GE Core 2, Booked at the Hotel At MIT
#Steve Pieper, Isomics, Core 2
#Katie Hayes, BWH, Core 2
#Dan Blezek, GE Core 2, Booked at the Hotel At MIT
#Tina Kapur, BWH, Core 6
#Ron Kikinis, Core 7, PI
#Peter Kazanzides, JHU, Collaborator
#Wendy Plesniak, BWH, Collaborator
#Luca Antiga, Mario Negri Institute, Collaborator
#Sylvain Bouix, BWH, Core 3
#Marek Kubicki, BWH, Core 3
#Chris Wyatt, Virginia Tech, Collaborator
#Nicole Aucoin, BWH, Core 2
#Will Schroeder, Kitware, Core 2
#Yumin Yuan, Kitware, Core 2
#Brad Davis, Kitware, Core 2
#Stephen Aylward, Kitware, Collaborator
#Raimundo Sierra, BWH, Core 2
#Clare Tempany, BWH Collaborator (Tuesday, June 26th only)
#Noby Hata, BWH Collaborator (Monday, June 25th only)
#Haiying Liu, BWH Collaborator
#Alex Yarmarkovich, Isomics, Core 2
#Vincent Magnotta, University of Iowa, Collaborator (Hotel at MIT request)
#Hans Johnson, University of Iowa, Collaborator
#Gary E. Christensen, University of Iowa, Collaborator
#Joo Hyun (Paul) Song, University of Iowa, Gary's student
#Xiujuan Geng, University of Iowa, Gary's student
#Jake Nickel, University of Iowa, Gary's student
#Nick Kiguta, University of Iowa, Gary's student
#Kunlin Cao, University of Iowa, Gary's student
#James Harris, University of Iowa, Gary's student
#Rhiannon Carlson, University of Iowa, Gary's student
#Jeff Hawley, University of Iowa, Gary's student
#Skip Talbot, Northwestern University, Collaborator
#Alex Kogan, Northwestern University, Collaborator
#Vladimir Kleper, Northwestern University, Collaborator
#Pat Mongkolwat, Northwestern University, Collaborator
#Csaba Csoma, Johns Hopkins University, Collaborator
#David Gobbi, Queen's University, Collaborator (Hotel at MIT request)
#Kai Ding, University of Iowa, Gary's student
#H. Jeremy Bockholt, The MIND Institute, DBP2:MIND PI
#Mark Scully, The MIND Institute, DBP2:MIND software engineer
#Sumner Williams, The MIND Institute, Magnotta/Johnson/Bockholt BRAINS grant software engineer
#Greg Sharp, MGH, Collaborator
#Lauren O'Donnell, BWH
#Raul San Jose, BWH
#Katharina Quintus, BWH, Core 3
#Marc Niethammer, BWH. Core 3
#Kevin Teich, MGH
#Michael Halle, BWH/IIC
#James Ross, GE
#Kiran Shivanna, University of Iowa, Collaborator (Hotel at MIT request)
#Douglas Alan, Harvard IIC
#Heather Cody Hazlett, UNC Core 3 (only part of week)
#Clement , UNC Core 3 (CS programmer) - full week
#Rachel G. Smith, UNC Core 3 (image lab manager) - tentative (may not be full week)

2007 Programming/Project Week MIT

2007-05-18T16:42:04Z

Hazlett: /* Attendee List */

Back to [[Engineering:Programming_Events|Programming/Project Events]]

== Logistics ==

'''Dates:''' June 25-29, 2007

'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].

'''Registration Fee:''' $200 (this will cover the cost of breakfast and lunch for the week). Due by Tuesday, June 19, 2007. Please make checks out to "Massachusetts Institute of Technology" and mail to:
Donna Kaufman, MIT, 77 Massachusetts Ave., 38-409b, Cambridge, MA 02139

If you are attending for one day only, the registration fee is not required.

'''Hotel:''' There is no official hotel for the meeting. Here is some information about Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Summer is tourist season in Boston, so please book your rooms early.

([[Project Week Logistics Checklist|This is a checklist for the onsite planning items]])

===Introduction to NA-MIC Project Week===

This is a week of hands on activity -- programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, and clinical application -- that has become one of the major events in the NA-MIC calendar. A full week of hands on activities is held in the summer at MIT (typically the last week of June), and for half a week in Salt Lake City in the winter (typically the second week of January).
The main goal of these events if to move forward the deliverables of NA-MIC. NA-MIC participants and their collaborators are welcome to attend.

* NA-MIC Members: Participation in this event is voluntary -- if you don't think this will help you move forward in your work, there is no obligation to attend.
* Ideal candidates are those who want to contribute to the NA-MIC Kit, and those who can help make it happen.
* This is not an introduction to the components of the NA-MIC Kit.
* NA-MIC Core 1 (Algorithms) - bring your algorithms and code to work on in the company of Core 2 engineers and Core 3 scientists.
* NA-MIC Core 2 (Engineering) - bring your code for infrastructure and applications to extend the NA-MIC Kit capabiliities, integrate Core 1 algorithms, and refine worflows for Core 3.
* NA-MIC Core 3 (DBP) - bring your data to work on with the NA-MIC Kit and get assistance and provide feedback to Core 1 scientists and Core 2 engineers.
* External Collaborators - if you are working on a project that uses the [[NA-MIC-Kit|NA-MIC kit]], and want to participate to get help from NA-MIC Engineering, please send an email to Tina Kapur (tkapur at bwh.harvard.edu). Please note that the event is open to people outside NA-MIC, subject to availability.
* Everyone should '''bring a laptop'''. We will have three or four projectors.
* About half the time will be spent working on projects and the other half in project related discussions.
* You '''do''' need to be actively working on a NA-MIC related project in order to make this investment worthwhile for everyone.

=== Draft Agenda===
Please note that this agenda is a draft and will be finalized by June 15th.

* Monday
** noon-1pm lunch
** 1-3:30pm Introduce Projects using 4-block slides (all Project Leads)
** 3:30-5:30pm Start project work
* Tuesday
** 8:30am breakfast
** 9-10am: NA-MIC Software Process (Bill Hoffman - TBC)
** 10-10:30am Slicer 3.0 Update(Jim Miller, Steve Pieper)
** 11-12pm: [[Special topic breakout: IGT for Prostate]]
** noon lunch
** 1-2pm: [[Special topic breakout: Non-Linear Registration]]
** 2-3pm: [[Special topic breakout: DWI/DTI]]
** 3-4pm: [[Special topic breakout: KWWidgets]]
** 4-5pm: [[Special topic breakout: Atlases]]
** 5:30pm adjourn for day
* Wednesday
** 8:30am breakfast
** noon lunch
** 1-2pm: Special topic breakout: [[NA-MIC:2007 Plan for Long-Lead Time Items| Plan for Long Lead Time Items]]
** 5:30pm adjourn for day
* Thursday
** 8:30am breakfast
** noon lunch
** 3:30-5pm Special topic breakout: TBD
** 5:30pm adjourn for day
* Friday
** 8:30am breakfast
** 10am-noon: Project Progress using 4-block slides
** noon lunch boxes and adjourn

=== Preparation ===

# Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-programming-week na-mic-programming-week mailing list]
# [[Engineering:TCON_2007#2007-05-03|May 3, 2007: Kickoff TCON#1 to discuss Engr Core Projects and Assign/Verify Teams]]
# [[Engineering:TCON_2007#2007-05-10|May 10, 2007: TCON#2 to discuss Projects and Assign/Verify Teams]]
# [[Engineering:TCON_2007#2007-05-17|May 17, 2007: TCON#3 to discuss outstanding projects and teams from previous week]]
# May 17, 2007: Create a Wiki page per project (the participants must do this, hopefully jointly)
# May 31, 2007: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Andy)
## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)
## Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Andy)
# By 3pm ET on June 21, 2007: Complete the top half of [[media:NA-MIC_Latest_4-Block_Template.ppt|this powerpoint template]] for each project. Upload and link to the right place.
# [[Engineering:TCON_2007#2005-06-21|June 21, 2007: TCON#4 Final Call before showtime...]]
# Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...

== Projects ==

===DBP II===
These are projects by the new set of DBPS:
*[[DBP2:MIND | Longitudinal Classification of White Matter Lesions in Lupus]] (MIND/UNM)
*[[DBP2:JHU | Segmentation and Registration Tools for Robotic Prostate Interventions]] (JHU/Queen's)
*[[DBP2:UNC |Longitudinal MRI study of early brain development in neuropsychiatric disorder: UNC Autism Study]] (UNC)
*[[DBP2:Harvard|Velocardiofacial Syndrome (VCFS) as a genetic model for schizophrenia]] (Harvard)

===Structural Analysis===
*EMSegmentation Validation (Brad Davis, Sylvain Bouix)
*vtkITK wrapper for rule based segmentation (John Melonakos, Brad Davis, Marek Kubicki)
** Application of the Slicer2 module on DBP data
** Conversion to Slicer3

===Diffusion Image Analysis===
* [[Algorithm:UNC:DTI#Population_Analysis | DTI population analysis]] (Casey Goodlett)
* Slicer3 Whole brain Seeding platform: data representation and pipeline execution (Raul San Jose, Lauren O'Donnell)
* Slicer3 Tractography editor (Lauren O'Donnell, Raul San Jose)

===NA-MIC Kit===
* Slicer3
** [[2007_Project_Week_MIT_QDEC_Slicer3_Integration | QDEC integration into Slicer3]] (Nicole Aucoin BWH, Kevin Teich MGH, Nick Schmansky MGH, Doug Greve MGH, Gheorghe Postelnicu MGH, Steve Pieper Isomics)
** Display Optimization (Raimundo Sierra, David Gobbi, Steve Pieper)
** [[2007_Project_Week_MIT_MRML_Scenes_for_the_Execution_Model]], including transforms (Jim Miller, Brad Davis, Nicole Aucoin, Alex Yarmarkovich, Steve Pieper)
** Support for Unstructured Grids (Steve, Nicole, Alex, Curt)
** Python support in Slicer3 (Luca, Steve, depends on Dan's availability)
** CPack, Ctest infrastructure improvements (Andy, Katie, Steve)
** Drafting Human Interface and Slicer Style Guidelines (Wendy)
** Slicer Matlab Pipeline for scalars and tensors (Katharina, Sylvain, Steve)
* Slicer2

===External Collaborations===
* Meshing
** Adding VTK interactive wwidgets to Slicer3 (Will, Vince, Kiran, Curt)
** Migrate Iowa Neural Net code to pure ITK (Vince, Stephen)
*IGT
** [[Tracker Integration]] (Noby, Haiying, Katie Hayes BWH)
** [[Collaboration/MGH/Radiation Therapy Radiation Therapy Planning]] (Greg Sharp MGH, Tina Kapur BWH, Sandy Wells BWH, Steve Pieper Isomics, Katie Hayes BWH)
** [[Collaboration/JHU/Brachytherapy needle positioning robot integration|Brachytherapy needle positioning robot integration]] (Csaba Csoma, Peter Kazanzides JHU, David Gobbi Queen's, Katie Hayes BWH)
** [[Collaboration/BWH/RadVision and Tracker Integration|RadVision and Tracker Integration]] (Jack Blevins, Noby)
* Registration
** [[Collaboration/UIowa/Non_Rigid_Registration|Implementing Non-rigid Image Registration and Evaluation Project (NIREP) software using NA-MIC Kit]] (Gary Christensen UIowa, Stephen Aylward, Kitware, Sandy Wells BWH)
** [[Collaboration/UIowa/Developing Electronic Atlas Software using NA-MIC Kit|Developing Electronic Atlas Software using NA-MIC Kit]] (Gary Christensen UIowa, Jeff Grethe)
** [[Collaboration/UIowa/Developing a GUI for non-rigid image registration programs using NA-MIC Kit|Developing a GUI for non-rigid image registration programs using NA-MIC Kit]] (Gary Christensen UIowa, Yumin Kitware)
* [[Collaboration/VMTK |vmtk (vmtk.sourceforge.net) integration within Slicer3]] (Luca Antiga, MNI, Dan Blezek(GE))
* [[Collaboration/NWU/Radiology Workstation| A Translation Station]](Skip, Alex, Vlad, Pat, Alex, Steve)
* [[Collaboration/WFU/NonHuman Primate Neuroimaging| Applying EMSegmenter to NonHuman Primate Neuroimaging]](Chris Wyatt VT, Kilian Pohl BWH)
* [[NA-MIC_NCBC_Collaboration:3D%2Bt_Cells_Lineage:GoFigure|3D+t Cells Lineage:GoFigure]] (Alex G, Yumin)

===Non-Medical Collaborations===
* AstroMed (Michael Halle, Douglas Alan)

==Attendee List==
#Kilian Pohl, BWH, Core 1
#John Melonakos, Georgia Tech, Core 1, (Hotel at MIT request)
#Tauseef Rehman, Georgia Tech, Core 1
#Casey Goodlett, UNC, Core 1, (Hotel at MIT request)
#W. Bryan Smith, UCSD/NCMIR, Core 2 (Tentative)
#Jim Miller, GE Core 2, Booked at the Hotel At MIT
#Steve Pieper, Isomics, Core 2
#Katie Hayes, BWH, Core 2
#Dan Blezek, GE Core 2, Booked at the Hotel At MIT
#Tina Kapur, BWH, Core 6
#Ron Kikinis, Core 7, PI
#Peter Kazanzides, JHU, Collaborator
#Wendy Plesniak, BWH, Collaborator
#Luca Antiga, Mario Negri Institute, Collaborator
#Sylvain Bouix, BWH, Core 3
#Marek Kubicki, BWH, Core 3
#Chris Wyatt, Virginia Tech, Collaborator
#Nicole Aucoin, BWH, Core 2
#Will Schroeder, Kitware, Core 2
#Yumin Yuan, Kitware, Core 2
#Brad Davis, Kitware, Core 2
#Stephen Aylward, Kitware, Collaborator
#Raimundo Sierra, BWH, Core 2
#Clare Tempany, BWH Collaborator (Tuesday, June 26th only)
#Noby Hata, BWH Collaborator (Monday, June 25th only)
#Haiying Liu, BWH Collaborator
#Alex Yarmarkovich, Isomics, Core 2
#Vincent Magnotta, University of Iowa, Collaborator (Hotel at MIT request)
#Hans Johnson, University of Iowa, Collaborator
#Gary E. Christensen, University of Iowa, Collaborator
#Joo Hyun (Paul) Song, University of Iowa, Gary's student
#Xiujuan Geng, University of Iowa, Gary's student
#Jake Nickel, University of Iowa, Gary's student
#Nick Kiguta, University of Iowa, Gary's student
#Kunlin Cao, University of Iowa, Gary's student
#James Harris, University of Iowa, Gary's student
#Rhiannon Carlson, University of Iowa, Gary's student
#Jeff Hawley, University of Iowa, Gary's student
#Skip Talbot, Northwestern University, Collaborator
#Alex Kogan, Northwestern University, Collaborator
#Vladimir Kleper, Northwestern University, Collaborator
#Pat Mongkolwat, Northwestern University, Collaborator
#Csaba Csoma, Johns Hopkins University, Collaborator
#David Gobbi, Queen's University, Collaborator (Hotel at MIT request)
#Kai Ding, University of Iowa, Gary's student
#H. Jeremy Bockholt, The MIND Institute, DBP2:MIND PI
#Mark Scully, The MIND Institute, DBP2:MIND software engineer
#Sumner Williams, The MIND Institute, Magnotta/Johnson/Bockholt BRAINS grant software engineer
#Greg Sharp, MGH, Collaborator
#Lauren O'Donnell, BWH
#Raul San Jose, BWH
#Katharina Quintus, BWH, Core 3
#Marc Niethammer, BWH. Core 3
#Kevin Teich, MGH
#Michael Halle, BWH/IIC
#James Ross, GE
#Kiran Shivanna, University of Iowa, Collaborator (Hotel at MIT request)
#Douglas Alan, Harvard IIC
#Heather cody, UNC Core 3 (only part of week)
#Clement , UNC Core 3 (CS programmer) - full week
#Rachel G. Smith, UNC Core 3 (image lab manager) - tentative (may not be full week)

2007 Programming/Project Week MIT

2007-05-18T16:41:24Z

Hazlett: /* Attendee List */

Back to [[Engineering:Programming_Events|Programming/Project Events]]

== Logistics ==

'''Dates:''' June 25-29, 2007

'''Location:''' MIT. [[Meeting_Locations:MIT_Grier_A_%26B|Grier Rooms A & B: 34-401A & 34-401B]].

'''Registration Fee:''' $200 (this will cover the cost of breakfast and lunch for the week). Due by Tuesday, June 19, 2007. Please make checks out to "Massachusetts Institute of Technology" and mail to:
Donna Kaufman, MIT, 77 Massachusetts Ave., 38-409b, Cambridge, MA 02139

If you are attending for one day only, the registration fee is not required.

'''Hotel:''' There is no official hotel for the meeting. Here is some information about Boston area hotels that are convenient to NA-MIC events: [[Boston_Hotels|Boston_Hotels]]. Summer is tourist season in Boston, so please book your rooms early.

([[Project Week Logistics Checklist|This is a checklist for the onsite planning items]])

===Introduction to NA-MIC Project Week===

This is a week of hands on activity -- programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, and clinical application -- that has become one of the major events in the NA-MIC calendar. A full week of hands on activities is held in the summer at MIT (typically the last week of June), and for half a week in Salt Lake City in the winter (typically the second week of January).
The main goal of these events if to move forward the deliverables of NA-MIC. NA-MIC participants and their collaborators are welcome to attend.

* NA-MIC Members: Participation in this event is voluntary -- if you don't think this will help you move forward in your work, there is no obligation to attend.
* Ideal candidates are those who want to contribute to the NA-MIC Kit, and those who can help make it happen.
* This is not an introduction to the components of the NA-MIC Kit.
* NA-MIC Core 1 (Algorithms) - bring your algorithms and code to work on in the company of Core 2 engineers and Core 3 scientists.
* NA-MIC Core 2 (Engineering) - bring your code for infrastructure and applications to extend the NA-MIC Kit capabiliities, integrate Core 1 algorithms, and refine worflows for Core 3.
* NA-MIC Core 3 (DBP) - bring your data to work on with the NA-MIC Kit and get assistance and provide feedback to Core 1 scientists and Core 2 engineers.
* External Collaborators - if you are working on a project that uses the [[NA-MIC-Kit|NA-MIC kit]], and want to participate to get help from NA-MIC Engineering, please send an email to Tina Kapur (tkapur at bwh.harvard.edu). Please note that the event is open to people outside NA-MIC, subject to availability.
* Everyone should '''bring a laptop'''. We will have three or four projectors.
* About half the time will be spent working on projects and the other half in project related discussions.
* You '''do''' need to be actively working on a NA-MIC related project in order to make this investment worthwhile for everyone.

=== Draft Agenda===
Please note that this agenda is a draft and will be finalized by June 15th.

* Monday
** noon-1pm lunch
** 1-3:30pm Introduce Projects using 4-block slides (all Project Leads)
** 3:30-5:30pm Start project work
* Tuesday
** 8:30am breakfast
** 9-10am: NA-MIC Software Process (Bill Hoffman - TBC)
** 10-10:30am Slicer 3.0 Update(Jim Miller, Steve Pieper)
** 11-12pm: [[Special topic breakout: IGT for Prostate]]
** noon lunch
** 1-2pm: [[Special topic breakout: Non-Linear Registration]]
** 2-3pm: [[Special topic breakout: DWI/DTI]]
** 3-4pm: [[Special topic breakout: KWWidgets]]
** 4-5pm: [[Special topic breakout: Atlases]]
** 5:30pm adjourn for day
* Wednesday
** 8:30am breakfast
** noon lunch
** 1-2pm: Special topic breakout: [[NA-MIC:2007 Plan for Long-Lead Time Items| Plan for Long Lead Time Items]]
** 5:30pm adjourn for day
* Thursday
** 8:30am breakfast
** noon lunch
** 3:30-5pm Special topic breakout: TBD
** 5:30pm adjourn for day
* Friday
** 8:30am breakfast
** 10am-noon: Project Progress using 4-block slides
** noon lunch boxes and adjourn

=== Preparation ===

# Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-programming-week na-mic-programming-week mailing list]
# [[Engineering:TCON_2007#2007-05-03|May 3, 2007: Kickoff TCON#1 to discuss Engr Core Projects and Assign/Verify Teams]]
# [[Engineering:TCON_2007#2007-05-10|May 10, 2007: TCON#2 to discuss Projects and Assign/Verify Teams]]
# [[Engineering:TCON_2007#2007-05-17|May 17, 2007: TCON#3 to discuss outstanding projects and teams from previous week]]
# May 17, 2007: Create a Wiki page per project (the participants must do this, hopefully jointly)
# May 31, 2007: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Andy)
## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)
## Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Andy)
# By 3pm ET on June 21, 2007: Complete the top half of [[media:NA-MIC_Latest_4-Block_Template.ppt|this powerpoint template]] for each project. Upload and link to the right place.
# [[Engineering:TCON_2007#2005-06-21|June 21, 2007: TCON#4 Final Call before showtime...]]
# Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...

== Projects ==

===DBP II===
These are projects by the new set of DBPS:
*[[DBP2:MIND | Longitudinal Classification of White Matter Lesions in Lupus]] (MIND/UNM)
*[[DBP2:JHU | Segmentation and Registration Tools for Robotic Prostate Interventions]] (JHU/Queen's)
*[[DBP2:UNC |Longitudinal MRI study of early brain development in neuropsychiatric disorder: UNC Autism Study]] (UNC)
*[[DBP2:Harvard|Velocardiofacial Syndrome (VCFS) as a genetic model for schizophrenia]] (Harvard)

===Structural Analysis===
*EMSegmentation Validation (Brad Davis, Sylvain Bouix)
*vtkITK wrapper for rule based segmentation (John Melonakos, Brad Davis, Marek Kubicki)
** Application of the Slicer2 module on DBP data
** Conversion to Slicer3

===Diffusion Image Analysis===
* [[Algorithm:UNC:DTI#Population_Analysis | DTI population analysis]] (Casey Goodlett)
* Slicer3 Whole brain Seeding platform: data representation and pipeline execution (Raul San Jose, Lauren O'Donnell)
* Slicer3 Tractography editor (Lauren O'Donnell, Raul San Jose)

===NA-MIC Kit===
* Slicer3
** [[2007_Project_Week_MIT_QDEC_Slicer3_Integration | QDEC integration into Slicer3]] (Nicole Aucoin BWH, Kevin Teich MGH, Nick Schmansky MGH, Doug Greve MGH, Gheorghe Postelnicu MGH, Steve Pieper Isomics)
** Display Optimization (Raimundo Sierra, David Gobbi, Steve Pieper)
** [[2007_Project_Week_MIT_MRML_Scenes_for_the_Execution_Model]], including transforms (Jim Miller, Brad Davis, Nicole Aucoin, Alex Yarmarkovich, Steve Pieper)
** Support for Unstructured Grids (Steve, Nicole, Alex, Curt)
** Python support in Slicer3 (Luca, Steve, depends on Dan's availability)
** CPack, Ctest infrastructure improvements (Andy, Katie, Steve)
** Drafting Human Interface and Slicer Style Guidelines (Wendy)
** Slicer Matlab Pipeline for scalars and tensors (Katharina, Sylvain, Steve)
* Slicer2

===External Collaborations===
* Meshing
** Adding VTK interactive wwidgets to Slicer3 (Will, Vince, Kiran, Curt)
** Migrate Iowa Neural Net code to pure ITK (Vince, Stephen)
*IGT
** [[Tracker Integration]] (Noby, Haiying, Katie Hayes BWH)
** [[Collaboration/MGH/Radiation Therapy Radiation Therapy Planning]] (Greg Sharp MGH, Tina Kapur BWH, Sandy Wells BWH, Steve Pieper Isomics, Katie Hayes BWH)
** [[Collaboration/JHU/Brachytherapy needle positioning robot integration|Brachytherapy needle positioning robot integration]] (Csaba Csoma, Peter Kazanzides JHU, David Gobbi Queen's, Katie Hayes BWH)
** [[Collaboration/BWH/RadVision and Tracker Integration|RadVision and Tracker Integration]] (Jack Blevins, Noby)
* Registration
** [[Collaboration/UIowa/Non_Rigid_Registration|Implementing Non-rigid Image Registration and Evaluation Project (NIREP) software using NA-MIC Kit]] (Gary Christensen UIowa, Stephen Aylward, Kitware, Sandy Wells BWH)
** [[Collaboration/UIowa/Developing Electronic Atlas Software using NA-MIC Kit|Developing Electronic Atlas Software using NA-MIC Kit]] (Gary Christensen UIowa, Jeff Grethe)
** [[Collaboration/UIowa/Developing a GUI for non-rigid image registration programs using NA-MIC Kit|Developing a GUI for non-rigid image registration programs using NA-MIC Kit]] (Gary Christensen UIowa, Yumin Kitware)
* [[Collaboration/VMTK |vmtk (vmtk.sourceforge.net) integration within Slicer3]] (Luca Antiga, MNI, Dan Blezek(GE))
* [[Collaboration/NWU/Radiology Workstation| A Translation Station]](Skip, Alex, Vlad, Pat, Alex, Steve)
* [[Collaboration/WFU/NonHuman Primate Neuroimaging| Applying EMSegmenter to NonHuman Primate Neuroimaging]](Chris Wyatt VT, Kilian Pohl BWH)
* [[NA-MIC_NCBC_Collaboration:3D%2Bt_Cells_Lineage:GoFigure|3D+t Cells Lineage:GoFigure]] (Alex G, Yumin)

===Non-Medical Collaborations===
* AstroMed (Michael Halle, Douglas Alan)

==Attendee List==
#Kilian Pohl, BWH, Core 1
#John Melonakos, Georgia Tech, Core 1, (Hotel at MIT request)
#Tauseef Rehman, Georgia Tech, Core 1
#Casey Goodlett, UNC, Core 1, (Hotel at MIT request)
#W. Bryan Smith, UCSD/NCMIR, Core 2 (Tentative)
#Jim Miller, GE Core 2, Booked at the Hotel At MIT
#Steve Pieper, Isomics, Core 2
#Katie Hayes, BWH, Core 2
#Dan Blezek, GE Core 2, Booked at the Hotel At MIT
#Tina Kapur, BWH, Core 6
#Ron Kikinis, Core 7, PI
#Peter Kazanzides, JHU, Collaborator
#Wendy Plesniak, BWH, Collaborator
#Luca Antiga, Mario Negri Institute, Collaborator
#Sylvain Bouix, BWH, Core 3
#Marek Kubicki, BWH, Core 3
#Chris Wyatt, Virginia Tech, Collaborator
#Nicole Aucoin, BWH, Core 2
#Will Schroeder, Kitware, Core 2
#Yumin Yuan, Kitware, Core 2
#Brad Davis, Kitware, Core 2
#Stephen Aylward, Kitware, Collaborator
#Raimundo Sierra, BWH, Core 2
#Clare Tempany, BWH Collaborator (Tuesday, June 26th only)
#Noby Hata, BWH Collaborator (Monday, June 25th only)
#Haiying Liu, BWH Collaborator
#Alex Yarmarkovich, Isomics, Core 2
#Vincent Magnotta, University of Iowa, Collaborator (Hotel at MIT request)
#Hans Johnson, University of Iowa, Collaborator
#Gary E. Christensen, University of Iowa, Collaborator
#Joo Hyun (Paul) Song, University of Iowa, Gary's student
#Xiujuan Geng, University of Iowa, Gary's student
#Jake Nickel, University of Iowa, Gary's student
#Nick Kiguta, University of Iowa, Gary's student
#Kunlin Cao, University of Iowa, Gary's student
#James Harris, University of Iowa, Gary's student
#Rhiannon Carlson, University of Iowa, Gary's student
#Jeff Hawley, University of Iowa, Gary's student
#Skip Talbot, Northwestern University, Collaborator
#Alex Kogan, Northwestern University, Collaborator
#Vladimir Kleper, Northwestern University, Collaborator
#Pat Mongkolwat, Northwestern University, Collaborator
#Csaba Csoma, Johns Hopkins University, Collaborator
#David Gobbi, Queen's University, Collaborator (Hotel at MIT request)
#Kai Ding, University of Iowa, Gary's student
#H. Jeremy Bockholt, The MIND Institute, DBP2:MIND PI
#Mark Scully, The MIND Institute, DBP2:MIND software engineer
#Sumner Williams, The MIND Institute, Magnotta/Johnson/Bockholt BRAINS grant software engineer
#Greg Sharp, MGH, Collaborator
#Lauren O'Donnell, BWH
#Raul San Jose, BWH
#Katharina Quintus, BWH, Core 3
#Marc Niethammer, BWH. Core 3
#Kevin Teich, MGH
#Michael Halle, BWH/IIC
#James Ross, GE
#Kiran Shivanna, University of Iowa, Collaborator (Hotel at MIT request)
#Douglas Alan, Harvard IIC
#Heather cody, UNC Core 3 (only part of week)
Clement , UNC Core 3 (CS programmer) - full week
Rachel G. Smith, UNC Core 3 (image lab manager) - tentative (may not be full week)