NAMIC Wiki - User contributions [en]

Linear Mixed-effects shape model to explore Huntington's Disease Data

2013-06-13T16:46:16Z

Manasi:

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
Image:fixedEffects01.png|fixed-effects (slope) [blue denotes expansion, yellow denotes contraction]
Image:fixedEffects02.png|fixed-effects (slope) [blue denotes expansion, yellow denotes contraction]
</gallery>

==Key Investigators==
* UIowa: Hans Johnson, Dave Welch
* Utah: Manasi Datar, Ross Whitaker

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
Make the linear mixed-effects shape model accessible for further exploration of Huntington's Disease Data

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Meet with UIowa team to:
* give an overview of results from the linear mixed-effects shape model
* explain the ShapeWorks command line tools to optimze and analyze correspondences
* discuss next steps toward making these tools accessible to UIowa team, to facilitate further exploration of the Huntington's disease Data

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Meeting on Tuesday (06/18), 9:00am - 10:00am
Attendees: Manasi, Hans, Dave, Ross

</div>
</div>

==References==
M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher, [http://www.cs.utah.edu/~manasi/pubs/MixedEffectsSTIA2012.pdf Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy], STIA 2012

File:FixedEffects02.png

2013-06-13T16:41:10Z

Manasi: fixed-effects trend (slope) for the sub-cortical structures from a subset of the HD data [10 sub-cortical structures for 13 subjects with 3 time-points each]

fixed-effects trend (slope) for the sub-cortical structures from a subset of the HD data [10 sub-cortical structures for 13 subjects with 3 time-points each]

File:FixedEffects01.png

2013-06-13T16:40:54Z

Manasi: fixed-effects trend (slope) for the sub-cortical structures from a subset of the HD data [10 sub-cortical structures for 13 subjects with 3 time-points each]

fixed-effects trend (slope) for the sub-cortical structures from a subset of the HD data [10 sub-cortical structures for 13 subjects with 3 time-points each]

2013 Summer Project Week

2013-06-13T16:26:54Z

Manasi: /* Huntington's Disease */

Back to [[Events]]
[[image:PW-MIT2013.png|300px]]

Dates: June 17-21, 2013.

Location: MIT, Cambridge, MA.

==Agenda==

{|border="1"
|-style="background:#b0d5e6;color:#02186f"
!style="width:10%" |Time
!style="width:18%" |Monday, June 17
!style="width:18%" |Tuesday, June 18
!style="width:18%" |Wednesday, June 19
!style="width:18%" |Thursday, June 20
!style="width:18%" |Friday, June 21
|-
|
|bgcolor="#dbdbdb"|'''Project Presentations'''
|bgcolor="#6494ec"|'''NA-MIC Update Day'''
|
|bgcolor="#88aaae"|'''IGT and RT Day'''
|bgcolor="#faedb6"|'''Reporting Day'''
|-
|bgcolor="#ffffdd"|'''8:30am'''
|
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|-
|bgcolor="#ffffdd"|'''9am-12pm'''
|
|'''10-11am''' [[2013 Project Week Breakout Session:Slicer4Python|Slicer4 Python Modules, Testing, Q&A]] 
[[MIT_Project_Week_Rooms|Grier Room (Left)]]
|'''9:30-11pm: Breakout Session:''' [[2013 Project Week Breakout Session: SimpleITK|Slicer and SimpleITK]] (Hans)
[[MIT_Project_Week_Rooms#32-D507|32-D507]]
|'''10am-12pm: Breakout Session:''' [[2013 Project Week Breakout Session: IGT|Image-Guided Therapy]] (Tina)
[[MIT_Project_Week_Rooms#32-D407|32-D407]]
|'''10am-12pm:''' [[#Projects|Project Progress Updates]]
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
|-
|bgcolor="#ffffdd"|'''12pm-1pm'''
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch boxes; Adjourn by 1:30pm
|-
|bgcolor="#ffffdd"|'''1pm-5:30pm'''
|'''1-1:05pm: Ron Kikinis: Welcome'''
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
 ---------------------------------------- 
'''1:05-3:30pm:''' [[#Projects|Project Introductions]] (all Project Leads)
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
 ---------------------------------------- 
'''3:30-4:30pm''' [[2013 Summer Project Week Breakout Session:SlicerExtensions|Slicer4 Extensions]] (Jean-Christophe Fillion-Robin) 
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Room (Left)]]
|'''1-3pm:''' [[Renewal-06-2013|NA-MIC Renewal]] PIs Closed Door Session with Ron
[[MIT_Project_Week_Rooms#32-D407|32-D407]]
 ---------------------------------------- 
'''3-4pm:''' [[2013_Tutorial_Contest|Tutorial Contest Presentations]] 
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
|'''12:45-1pm:''' [[Events:TutorialContestJune2013|Tutorial Contest Winner Announcement]]
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
| ---------------------------------------- '''3-5:30pm: Breakout Session:''' [[2013 Summer Project Week Breakout Session:RT|Radiation Therapy]] (Greg, Csaba)
[[MIT_Project_Week_Rooms#32-D407|32-D407]]
|
|-
|bgcolor="#ffffdd"|'''5:30pm'''
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|
|}

== '''Projects''' ==

Please use [http://wiki.na-mic.org/Wiki/index.php/Project_Week/Template this template] to create wiki pages for your project. Then link the page here with a list of key personnel.

===Huntington's Disease===
* [[Dynamically Configurable Quality Assurance Module for Large Huntington's Disease Database Frontend]] (Dave)
* [[DWIConvert]] (Kent Williams)
* [[Learn and Apply FiberBundleLabelSelect for Huntington's Disease Data]] (Hans, Demian)
* [[Investigate Potential Tensor Computation Improvement via Positive Semi-Definite (PSD) Tensor Estimation]] (Hans)
* [[Enhance and update SPL atlas]] (Dave, Hans)
* [[Linear Mixed-effects shape model to explore Huntington's Disease Data]] (Manasi, Dave, Hans, Ross)

===Traumatic Brain Injury===
* [[Validation and testing of 3D Slicer modules implementing the Utah segmentation algorithm for traumatic brain injury]] (Bo Wang, Marcel Prastawa, Andrei Irimia, Micah Chambers, Guido Gerig, Jack van Horn)
* [[Visualization and quantification of peri-contusional white matter bundles in traumatic brain injury using diffusion tensor imaging]] (Andrei Irimia, Micah Chambers, Ron Kikinis, Jack van Horn)
* [[Clinically oriented assessment of local changes in the properties of white matter affected by intra-cranial hemorrhage]] (Andrei Irimia, Micah Chambers, Ron Kikinis, Jack van Horn)
* [[2013_Summer_Project_Week: A Portable Ultrasound Device for Intracranial Hemorrhage Detection|A Portable Ultrasound Device for Intracranial Hemorrhage Detection]] (Jason White, Vicki Noble, Kirby Vosburgh)

===Atrial Fibrillation===
* [[2013_Summer_Project_Week:CARMA_workflow_wizard|Cardiac MRI Toolkit LA segmentation and enhancement quantification workflow wizard]] (Salma Bengali, Alan Morris, Brian Zenger, Josh Cates, Rob MacLeod)
* [[2013_Summer_Project_Week:CARMA_Documentataion|Cardiac MRI Toolkit Documentation Project]] (Salma Bengali, Alan Morris, Brian Zenger, Josh Cates, Rob MacLeod)
* [[2013_Summer_Project_Week:CARMA_Visualization|LA model visualization]] (Salma Bengali, Alan Morris, Josh Cates, Rob MacLeod)
* [[2013_Summer_Project_Week:CARMA_AutoLASeg|Cardiac MRI Toolkit: Automatic LA Segmentation with Graph Cuts Module]] (Salma Bengali, Alan Morris, Josh Cates, Gopal, Ross Whitaker, Rob MacLeod)
* [[2013_Summer_Project_Week:Sobolev_Segmenter|Medical Volume Segmentation Using Sobolev Active Contours]] (Arie Nakhmani, Yi Gao, LiangJia Zhu, Rob MacLeod, Josh Cates, Ron Kikinis, Allen Tannenbaum)
* [[2013_Summer_Project_Week:Fibrosis_analysis|Fibrosis distribution analysis]] (Yi Gao, LiangJia Zhu, Rob MacLeod, Josh Cates, Ron Kikinis, Allen Tannenbaum)

===Radiation Therapy===
* [[2013_Summer_Project_Week:Landmark_Registration| Landmark Registration]] (Steve, Nadya, Greg, Paolo, Erol)
* [[2013_Summer_Project_Week:Slicer_RT:_DICOM-RT_Export | SlicerRT: Dicom-RT Export]] (Greg Sharp, Kevin Wang, Csaba Pinter)
* [[2013_Summer_Project_Week:Proton_dose_calculation | Proton dose calculation]] (Greg Sharp, Kevin Wang, Maxime Desplanques)
* [[2013_Summer_Project_Week:Deformable_registration_validation_toolkit | Deformable registration validation toolkit]] (Greg Sharp, Kevin Wang, Andrey Fedorov, anyone else?)
* [[Analysis of different atlas-based segmentation techniques for parotid glands]] (Christian Wachinger, Karl Fritscher, Greg Sharp, Matthew Brennan)
* [[2013_Summer_Project_Week:Deformable_transforms | Deformable transform handling in Transforms module]] (Csaba Pinter, Alex Yarmarkovich, Andras Lasso, ?)
* [[2013_Summer_Project_Week:CMFReg | Cranio-Maxillofacial Registration]] (Vinicius Boen)

===Device Integration with Slicer===
* [[2013_Summer_Project_Week:Open_source_electromagnetic_trackers_using_OpenIGTLink| Open-source electromagnetic trackers using OpenIGTLink]] (Peter Traneus Anderson, Tina Kapur, Sonia Pujol)
* [[2013_Summer_Project_Week:kukarobot| Interface for the integration of a KUKA robot using OpnIGTLink]] (Sebastian Tauscher, Nobuhiko Hata)

===IGT===
* [[2013_Summer_Project_Week:SlicerIGT_Extension| SlicerIGT extension]] (Tamas, Junichi, Laurent)
* [[2013_Summer_Project_Week:Ultrasound_Calibration| Ultrasound Calibration]] (Matthew Toews, Daniel Kostro, William Wells, Steven Aylward, Tamas Ungi)
* [[2013_Summer_Project_Week:Application of Statistical Shape Modeling to Robot Assisted Spine Surgery | Application of Statistical Shape Modeling to Robot Assisted Spine Surgery]] (Marine Clogenson)
* [[2013_Summer_Project_Week:Epilepsy_Surgery|Identification of MRI Blurring in Temporal Lobe Epilepsy Surgery]] (Luiz Murta)
* [[2013_Summer_Project_Week:_Is_Neurosurgical_Rigid_Registration_Really_Rigid%3F| Is Neurosurgical Rigid Registration Really Rigid?]] (Athena)
* [[2013_Summer_Project_Week:Liver_Trajectory_Management| Liver Trajectory Management]] (Laurent, Junichi)
* [[2013_Summer_Project_Week:4DUltrasound| 4D Ultrasound]] (Laurent, Junichi)
* [[2013_Summer_Project_Week: Individualized Neuroimaging Content Analysis using 3D Slicer in Alzheimer's Disease| Individualized Neuroimaging Content Analysis using 3D Slicer]] (Sidong Liu, Weidong Cai, Sonia Pujol, Ron Kikinis)
* [[2013_Summer_Project_Week: Computer Assisted Surgery| Computer Assisted Reconstruction of Complex Bone Fractures]] (Karl Fritscher, Peter Karasev, Ron Kikinis)
* [[2013_Summer_Project_Week:PerkTutorExtension| Perk Tutor Extension]] (Matthew Holden, Tamas Ungi)
* [[2013_Summer_Project_Week:TractAtlasCluster| Tract Atlas and Clustering]] (Lauren O'Donnell)

=== '''Informatics'''===
* [[2013_Summer_Project_Week:Biomedical_Image_Computing_Teaching_Modules|3D Slicer based Biomedical image computing teaching modules]] (A.Vilchis, J-C. Avila-Vilchis, S.Pujol)
* [[2013_Summer_Project_Week:Robot_Control| Robot Control]] (A.Vilchis, J-C. Avila-Vilchis, S.Pujol)

==='''Infrastructure'''===
* [[2013_Summer_Project_Week:MarkupsModuleSummer2013| Markups/Annotations rewrite]] (Nicole Aucoin)
* Provenance
* [[2013_Summer_Project_Week:Patient_hierarchy | Patient hierarchy]] (Csaba Pinter, Andras Lasso, Steve Pieper)
* [[2013_Summer_Project_Week:CLI_Improvements | CLI Improvements (hierarchy nodes, related nodes, roles)]] (Andras Lasso, Csaba Pinter, Jc, Steve, Jim, ?)
* [[2013_Summer_Project_Week:Sample_Data | Sample Data]] (Steve Pieper, Jim Miller, Bill Lorensen, Jc)
* [[Plastimatch in NiPype]] (Paolo, Dave, Hans)
** look for commonalities/reuse of CompareVolumes
* iPython in Slicer (Hans, Jc, Dave)
* [[2013_Summer_Project_Week:Optimizing start time of slicer| Optimizing start time of slicer]] (Jc, Steve)
* [[Common resampling and conversion utility functions in Slicer]] (Csaba Pinter, Steve Pieper, Hans, Kevin Wang)
* [[2013_Summer_Project_Week:CLI_modules_in_MeVisLab| Integrating CTK CLI modules into MeVisLab]] (Hans Meine, Steve, Jc)
*[[2013_Project_Week:WebbasedAnatomicalTeachingFrameworkSummer2013|Web-based anatomical teaching framework]] (Daniel Haehn, Steve Pieper, Rudolph Pienaar, Lilla Zollei, Nathaniel Reynolds)

==='''Brain & Spine Segmentation'''===
* [[2013_Summer_Project_Week:Multi_Atlas_Based_Multi_Image_Segmentation | Multi-Atlas-Based Multi-Image Segmentation for Brain MR Images]] (Minjeong Kim, Xiaofeng Liu, Jim Miller, Dinggang Shen)
* [[2013_Summer_Project_Week:Radnostics |Spine Segmentation & Osteoporosis Detection In CT Imaging Studies]] (Anthony Blumfield, Ron Kikinis)

===""Chronic Obstructive Pulmonary Disease""===
* [[2013_Summer_Project_Week:Airway_Inspector_Porting | Porting Airway Inspector to Slicer 4]] (Raul San Jose, Demian Wassermann, Rola Harmouche)
* [[2013_Summer_Project_Week:MRML_Infrastructure_Airway_Inspector | Airway Inspector: Slicer Extension and MRML Infrastructure]] (Demian Wassermann, Raul San Jose, Rola Harmouche)
* [[2013_Summer_Project_Week:Nipype_CLI_Integration | Integration of Nipype with CLI modules in the Chest Imaging Platform Library ]] (Rola Harmouche,Demian Wassermann, Raul San Jose)

== '''Background''' ==

We are pleased to announce the 17th PROJECT WEEK of hands-on research and development activity for applications in Neuroscience, Image-Guided Therapy and several additional areas of biomedical research that enable personalized medicine. Participants will engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational research deliverables of the sponsoring centers and their collaborators. Active and potential collaborators are encouraged and welcome to attend this event. This event will be set up to maximize informal interaction between participants. If you would like to learn more about this event, please [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week click here to join our mailing list].

Active preparation begins on Thursday, April 25th at 3pm ET, with a kick-off teleconference. Invitations to this call will be sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties who have expressed an interest in working with these centers. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient coverage for all. Subsequent teleconferences will allow for more focused discussions on individual projects and allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams will be asked to fill in a template page on this wiki that describes the objectives and plan of their projects.

The event itself will start off with a short presentation by each project team, driven using their previously created description, and will help all participants get acquainted with others who are doing similar work. In the rest of the week, about half the time will be spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half will be spent in project teams, doing hands-on project work. The hands-on activities will be done in 40-50 small teams of size 2-4, each with a mix of multi-disciplinary expertise. To facilitate this work, a large room at MIT will be setup with several tables, with internet and power access, and each computer software development based team will gather on a table with their individual laptops, connect to the internet to download their software and data, and be able to work on their projects. Teams working on projects that require the use of medical devices will proceed to Brigham and Women's Hospital and carry out their experiments there. On the last day of the event, a closing presentation session will be held in which each project team will present a summary of what they accomplished during the week.

This event is part of the translational research efforts of [http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu/ NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], [http://www.cimit.org CIMIT], and OCAIRO. It is an expansion of the NA-MIC Summer Project Week that has been held annually since 2005. It will be held every summer at MIT and Brigham and Womens Hospital in Boston, typically during the last full week of June, and in Salt Lake City in the winter, typically during the second week of January.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

== '''Logistics''' ==

*'''Dates:''' June 17-21, 2013.
*'''Location:''' MIT.
*'''REGISTRATION:''' http://www.regonline.com/namic2013summerprojweek. Please note that as you proceed to the checkout portion of the registration process, RegOnline will offer you a chance to opt into a free trial of ACTIVEAdvantage -- click on "No thanks" in order to finish your Project Week registration.
*'''Registration Fee:''' $300.
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.
*'''Room sharing''': If interested, add your name to the list before May 27th. See [[2013_Summer_Project_Week/RoomSharing|here]]

== '''Preparation''' ==

# Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list
# The NA-MIC engineering team will be discussing projects in a their [http://wiki.na-mic.org/Wiki/index.php/Engineering:TCON_2013 weekly teleconferences]. Participants from the above mailing list will be invited to join to discuss their projects, so please make sure you are on it!
# By 3pm ET on Thursday May 8, all participants to add a one line title of their project to #Projects
# By 3pm ET on Thursday June 6, all project leads to complete [[Project_Week/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.
# By 3pm on June 13: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Matt)
## 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. XNAT/MIDAS). 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.)
## Where possible, 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. (Matt)
# 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...
# People doing Slicer related projects should come to project week with slicer built on your laptop.
## See the [http://www.slicer.org/slicerWiki/index.php/Documentation/4.0/Developers Developer Section of slicer.org] for information.
## Projects to develop extension modules should be built against the latest Slicer4 trunk.

== '''Registrants''' ==

Do not add your name to this list - it is maintained by the organizers based on your paid registration. ([http://www.regonline.com/Register/Checkin.aspx?EventID=1233699 Please click here to register.])

#Charles Anderson, BWH, canderson26@partners.org
#Peter Anderson, retired, traneus@verizon.net
#Nicole Aucoin, BWH, nicole@bwh.harvard.edu
#Juan Carlos Avila Vilchis, Univ del Estado de Mexico, jc.avila.vilchis@hotmail.com
#Salma Bengali, Univ UT, salma.bengali@carma.utah.edu
#Anthony Blumfield, Radnostics, Anthony.Blumfield@Radnostics.com
#Vinicius Boen, Univ Michigan, vboen@umich.edu
#Matthew Brennan, MIT, brennanm@mit.edu
#Francois Budin, NIRAL-UNC, fbudin@unc.edu
#Ivan Buzurovic, BWH/HMS, ibuzurovic@lroc.harvard.edu
#Josh Cates, Univ UT, cates@sci.utah.edu
#Micah Chambers, UCLA, micahcc@ucla.edu
#Marine Clogenson, Ecole Polytechnique Federale de Lausanne (Switzerland), marine.clogenson@epfl.ch
#Adrian Dalca, MIT, adalca@MIT.EDU
#Manasi Datar, Univ UT-SCI Institute, datar@sci.utah.edu
#Sneha Durgapal, BWH, durgapalsneha@gmail.com
#Luping Fang, Zhejiang Univ of Technology (China), flp@zjut.edu.cn
#Andriy Fedorov, BWH, fedorov@bwh.harvard.edu
#Jean-Christophe Fillion-Robin, Kitware, jchris.fillionr@kitware.com
#Gregory Fischer, WPI, gfischer@wpi.edu
#Karl Fritscher, MGH, kfritscher@gmail.com
#Yi Gao, Univ AL Birmingham, gaoyi.cn@gmail.com
#Daniel Haehn, Boston Childrens Hospital, daniel.haehn@childrens.harvard.edu
#Michael Halle, BWH-SPL, mhalle@bwh.harvard.edu
#Rola Harmouche, BWH, rharmo@bwh.harvard.edu
#Nobuhiko Hata, BWH, hata@bwh.harvard.edu
#Nicholas Herlambang, AZE Technology Inc, nicholas.herlambang@azetech.com
#Matthew Holden, Queen's Univ (Canada), mholden8@cs.queensu.ca
#Andrei Irimia, UCLA, andrei.irimia@loni.ucla.edu
#Jayender Jagadeesan, BWH-SPL, jayender@bwh.harvard.edu
#Hans Johnson, Univ Iowa, hans-johnson@uiowa.edu
#Tina Kapur, BWH/HMS, tkapur@bwh.harvard.edu
#Ron Kikinis, HMS, kikinis@bwh.harvard.edu
#Daniel Kostro, BWH, dkostro@bwh.harvard.edu
#Andras Lasso, Queen's Univ (Canada), lasso@cs.queensu.ca
#Rui Li, GE Global Research, li.rui@ge.com
#Xu Li, BWH, lixu0103@gmail.com
#Sidong Liu, Univ Sydney (Australia), sliu7418@uni.sydney.edu.au
#William Lorensen, Bill's Basement, bill.lorensen@gmail.com
#Bradley Lowekamp, Medical Science & Computing Inc, bradley.lowekamp@nih.gov
#Athena Lyons, Univ Western Australia, 20359511@student.uwa.edu.au
#Katie Mastrogiacomo, BWH - SPL, kmast@bwh.harvard.edu
#Alireza Mehrtash, BWH - SPL, mehrtash@bwh.harvard.edu
#Hans Meine, Fraunhofer MEVIS (Germany), hans.meine@mevis.fraunhofer.de
#Jim Miller, GE Global Research, millerjv@ge.com
#Luis Murta, Univ Sao Paulo (Brazil), lomurta@gmail.com
#Arie Nakhmani, Univ AL Birmingham, anry@uab.edu
#Isaiah Norton, BWH, inorton@bwh.harvard.edu
#Lauren O'Donnell, BWH, odonnell@bwh.harvard.edu
#Dirk Padfield, GE Global Research, padfield@research.ge.com
#Jian Pan, Zhejiang Univ of Technology (China), pj@zjut.edu.cn
#Steve Pieper, Isomics Inc, pieper@isomics.com
#Csaba Pinter, Queen's Univ (Canada), pinter@cs.queensu.ca
#Sonia Pujol, HMS, spujol@bwh.harvard.edu
#Adam Rankin, Queen's Univ (Canada), rankin@cs.queensu.ca
#Nathaniel Reynolds, MGH, reynolds@nmr.mgh.harvard.edu
#Raul San Jose, BWH, rjosest@bwh.harvard.edu
#Anuja Sharma, Univ UT-SCI Institute, anuja@cs.utah.edu
#Greg Sharp, MGH, gcsharp@partners.org
#Nadya Shusharina, MGH, nshusharina@partners.org
#Sebastian Tauscher, Leibniz Univ Hannover (Germany), sebastian.tauscher@imes.uni-hannover.de
#Matthew Toews, BWH/HMS, mt@bwh.harvard.edu
#Junichi Tokuda, BWH, tokuda@bwh.harvard.edu
#Tamas Ungi, Queen's Univ (Canada), ungi@cs.queensu.ca
#Adriana Vilchis González, Univ del Estado de Mexico, hvigady@hotmail.com
#Christian Wachinger, MIT, wachinge@mit.edu
#Bo Wang, Univ UT-SCI Institute, bowang@sci.utah.edu
#Demian Wassermann, BWH, demian@bwh.harvard.edu
#David Welch, Univ Iowa, david-welch@uiowa.edu
#William Wells, BWH/HMS, sw@bwh.harvard.edu
#Phillip White, BWH/HMS, white@bwh.harvard.edu
#Alex Yarmarkovich, Isomics Inc, alexy@bwh.harvard.edu
#Yang Yu, Rutgers Univ, yyu@cs.rutgers.edu
#Paolo Zaffino, Univ Magna Graecia of Catanzaro (Italy), p.zaffino@unicz.it
#Lilla Zollei, MGH, lzollei@nmr.mgh.harvard.edu

Linear Mixed-effects shape model to explore Huntington's Disease Data

2013-06-13T16:26:21Z

Manasi: Created page with '__NOTOC__ <gallery> Image:PW-MIT2013.png|Projects List </gallery> '''TODO: add images''' ==Key Investigators== * UIowa: Hans Johnson, Dave …'

__NOTOC__
<gallery>
Image:PW-MIT2013.png|[[2013_Summer_Project_Week#Projects|Projects List]]
</gallery>
'''TODO: add images'''

==Key Investigators==
* UIowa: Hans Johnson, Dave Welch
* Utah: Manasi Datar, Ross Whitaker

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
Make the linear mixed-effects shape model accessible for further exploration of Huntington's Disease Data

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Meet with UIowa team to:
* give an overview of results from the linear mixed-effects shape model
* explain the ShapeWorks command line tools to optimze and analyze correspondences
* discuss next steps toward making these tools accessible to UIowa team, to facilitate further exploration of the Huntington's disease Data

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Meeting on Tuesday (06/18), 9:00am - 10:00am
Attendees: Manasi, Hans, Dave, Ross

</div>
</div>

==References==
M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher, [http://www.cs.utah.edu/~manasi/pubs/MixedEffectsSTIA2012.pdf Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy], STIA 2012

2013 Summer Project Week

2013-06-13T16:12:23Z

Manasi: /* Huntington's Disease */

Back to [[Events]]
[[image:PW-MIT2013.png|300px]]

Dates: June 17-21, 2013.

Location: MIT, Cambridge, MA.

==Agenda==

{|border="1"
|-style="background:#b0d5e6;color:#02186f"
!style="width:10%" |Time
!style="width:18%" |Monday, June 17
!style="width:18%" |Tuesday, June 18
!style="width:18%" |Wednesday, June 19
!style="width:18%" |Thursday, June 20
!style="width:18%" |Friday, June 21
|-
|
|bgcolor="#dbdbdb"|'''Project Presentations'''
|bgcolor="#6494ec"|'''NA-MIC Update Day'''
|
|bgcolor="#88aaae"|'''IGT and RT Day'''
|bgcolor="#faedb6"|'''Reporting Day'''
|-
|bgcolor="#ffffdd"|'''8:30am'''
|
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|bgcolor="#ffffaa"|Breakfast
|-
|bgcolor="#ffffdd"|'''9am-12pm'''
|
|'''10-11am''' [[2013 Project Week Breakout Session:Slicer4Python|Slicer4 Python Modules, Testing, Q&A]] 
[[MIT_Project_Week_Rooms|Grier Room (Left)]]
|'''9:30-11pm: Breakout Session:''' [[2013 Project Week Breakout Session: SimpleITK|Slicer and SimpleITK]] (Hans)
[[MIT_Project_Week_Rooms#32-D507|32-D507]]
|'''10am-12pm: Breakout Session:''' [[2013 Project Week Breakout Session: IGT|Image-Guided Therapy]] (Tina)
[[MIT_Project_Week_Rooms#32-D407|32-D407]]
|'''10am-12pm:''' [[#Projects|Project Progress Updates]]
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
|-
|bgcolor="#ffffdd"|'''12pm-1pm'''
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch
|bgcolor="#ffffaa"|Lunch boxes; Adjourn by 1:30pm
|-
|bgcolor="#ffffdd"|'''1pm-5:30pm'''
|'''1-1:05pm: Ron Kikinis: Welcome'''
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
 ---------------------------------------- 
'''1:05-3:30pm:''' [[#Projects|Project Introductions]] (all Project Leads)
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
 ---------------------------------------- 
'''3:30-4:30pm''' [[2013 Summer Project Week Breakout Session:SlicerExtensions|Slicer4 Extensions]] (Jean-Christophe Fillion-Robin) 
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Room (Left)]]
|'''1-3pm:''' [[Renewal-06-2013|NA-MIC Renewal]] PIs Closed Door Session with Ron
[[MIT_Project_Week_Rooms#32-D407|32-D407]]
 ---------------------------------------- 
'''3-4pm:''' [[2013_Tutorial_Contest|Tutorial Contest Presentations]] 
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
|'''12:45-1pm:''' [[Events:TutorialContestJune2013|Tutorial Contest Winner Announcement]]
[[MIT_Project_Week_Rooms#Grier_34-401_AB|Grier Rooms]]
| ---------------------------------------- '''3-5:30pm: Breakout Session:''' [[2013 Summer Project Week Breakout Session:RT|Radiation Therapy]] (Greg, Csaba)
[[MIT_Project_Week_Rooms#32-D407|32-D407]]
|
|-
|bgcolor="#ffffdd"|'''5:30pm'''
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|bgcolor="#f0e68b"|Adjourn for the day
|
|}

== '''Projects''' ==

Please use [http://wiki.na-mic.org/Wiki/index.php/Project_Week/Template this template] to create wiki pages for your project. Then link the page here with a list of key personnel.

===Huntington's Disease===
* [[Dynamically Configurable Quality Assurance Module for Large Huntington's Disease Database Frontend]] (Dave)
* [[DWIConvert]] (Kent Williams)
* [[Learn and Apply FiberBundleLabelSelect for Huntington's Disease Data]] (Hans, Demian)
* [[Investigate Potential Tensor Computation Improvement via Positive Semi-Definite (PSD) Tensor Estimation]] (Hans)
* [[Enhance and update SPL atlas]] (Dave, Hans)
* [[Linear Mixed-effects shape model to explore Huntington's Disease Data]] (David, Hans, Manasi, Ross)

===Traumatic Brain Injury===
* [[Validation and testing of 3D Slicer modules implementing the Utah segmentation algorithm for traumatic brain injury]] (Bo Wang, Marcel Prastawa, Andrei Irimia, Micah Chambers, Guido Gerig, Jack van Horn)
* [[Visualization and quantification of peri-contusional white matter bundles in traumatic brain injury using diffusion tensor imaging]] (Andrei Irimia, Micah Chambers, Ron Kikinis, Jack van Horn)
* [[Clinically oriented assessment of local changes in the properties of white matter affected by intra-cranial hemorrhage]] (Andrei Irimia, Micah Chambers, Ron Kikinis, Jack van Horn)
* [[2013_Summer_Project_Week: A Portable Ultrasound Device for Intracranial Hemorrhage Detection|A Portable Ultrasound Device for Intracranial Hemorrhage Detection]] (Jason White, Vicki Noble, Kirby Vosburgh)

===Atrial Fibrillation===
* [[2013_Summer_Project_Week:CARMA_workflow_wizard|Cardiac MRI Toolkit LA segmentation and enhancement quantification workflow wizard]] (Salma Bengali, Alan Morris, Brian Zenger, Josh Cates, Rob MacLeod)
* [[2013_Summer_Project_Week:CARMA_Documentataion|Cardiac MRI Toolkit Documentation Project]] (Salma Bengali, Alan Morris, Brian Zenger, Josh Cates, Rob MacLeod)
* [[2013_Summer_Project_Week:CARMA_Visualization|LA model visualization]] (Salma Bengali, Alan Morris, Josh Cates, Rob MacLeod)
* [[2013_Summer_Project_Week:CARMA_AutoLASeg|Cardiac MRI Toolkit: Automatic LA Segmentation with Graph Cuts Module]] (Salma Bengali, Alan Morris, Josh Cates, Gopal, Ross Whitaker, Rob MacLeod)
* [[2013_Summer_Project_Week:Sobolev_Segmenter|Medical Volume Segmentation Using Sobolev Active Contours]] (Arie Nakhmani, Yi Gao, LiangJia Zhu, Rob MacLeod, Josh Cates, Ron Kikinis, Allen Tannenbaum)
* [[2013_Summer_Project_Week:Fibrosis_analysis|Fibrosis distribution analysis]] (Yi Gao, LiangJia Zhu, Rob MacLeod, Josh Cates, Ron Kikinis, Allen Tannenbaum)

===Radiation Therapy===
* [[2013_Summer_Project_Week:Landmark_Registration| Landmark Registration]] (Steve, Nadya, Greg, Paolo, Erol)
* [[2013_Summer_Project_Week:Slicer_RT:_DICOM-RT_Export | SlicerRT: Dicom-RT Export]] (Greg Sharp, Kevin Wang, Csaba Pinter)
* [[2013_Summer_Project_Week:Proton_dose_calculation | Proton dose calculation]] (Greg Sharp, Kevin Wang, Maxime Desplanques)
* [[2013_Summer_Project_Week:Deformable_registration_validation_toolkit | Deformable registration validation toolkit]] (Greg Sharp, Kevin Wang, Andrey Fedorov, anyone else?)
* [[Analysis of different atlas-based segmentation techniques for parotid glands]] (Christian Wachinger, Karl Fritscher, Greg Sharp, Matthew Brennan)
* [[2013_Summer_Project_Week:Deformable_transforms | Deformable transform handling in Transforms module]] (Csaba Pinter, Alex Yarmarkovich, Andras Lasso, ?)
* [[2013_Summer_Project_Week:CMFReg | Cranio-Maxillofacial Registration]] (Vinicius Boen)

===Device Integration with Slicer===
* [[2013_Summer_Project_Week:Open_source_electromagnetic_trackers_using_OpenIGTLink| Open-source electromagnetic trackers using OpenIGTLink]] (Peter Traneus Anderson, Tina Kapur, Sonia Pujol)
* [[2013_Summer_Project_Week:kukarobot| Interface for the integration of a KUKA robot using OpnIGTLink]] (Sebastian Tauscher, Nobuhiko Hata)

===IGT===
* [[2013_Summer_Project_Week:SlicerIGT_Extension| SlicerIGT extension]] (Tamas, Junichi, Laurent)
* [[2013_Summer_Project_Week:Ultrasound_Calibration| Ultrasound Calibration]] (Matthew Toews, Daniel Kostro, William Wells, Steven Aylward, Tamas Ungi)
* [[2013_Summer_Project_Week:Application of Statistical Shape Modeling to Robot Assisted Spine Surgery | Application of Statistical Shape Modeling to Robot Assisted Spine Surgery]] (Marine Clogenson)
* [[2013_Summer_Project_Week:Epilepsy_Surgery|Identification of MRI Blurring in Temporal Lobe Epilepsy Surgery]] (Luiz Murta)
* [[2013_Summer_Project_Week:_Is_Neurosurgical_Rigid_Registration_Really_Rigid%3F| Is Neurosurgical Rigid Registration Really Rigid?]] (Athena)
* [[2013_Summer_Project_Week:Liver_Trajectory_Management| Liver Trajectory Management]] (Laurent, Junichi)
* [[2013_Summer_Project_Week:4DUltrasound| 4D Ultrasound]] (Laurent, Junichi)
* [[2013_Summer_Project_Week: Individualized Neuroimaging Content Analysis using 3D Slicer in Alzheimer's Disease| Individualized Neuroimaging Content Analysis using 3D Slicer]] (Sidong Liu, Weidong Cai, Sonia Pujol, Ron Kikinis)
* [[2013_Summer_Project_Week: Computer Assisted Surgery| Computer Assisted Reconstruction of Complex Bone Fractures]] (Karl Fritscher, Peter Karasev, Ron Kikinis)
* [[2013_Summer_Project_Week:PerkTutorExtension| Perk Tutor Extension]] (Matthew Holden, Tamas Ungi)
* [[2013_Summer_Project_Week:TractAtlasCluster| Tract Atlas and Clustering]] (Lauren O'Donnell)

=== '''Informatics'''===
* [[2013_Summer_Project_Week:Biomedical_Image_Computing_Teaching_Modules|3D Slicer based Biomedical image computing teaching modules]] (A.Vilchis, J-C. Avila-Vilchis, S.Pujol)
* [[2013_Summer_Project_Week:Robot_Control| Robot Control]] (A.Vilchis, J-C. Avila-Vilchis, S.Pujol)

==='''Infrastructure'''===
* [[2013_Summer_Project_Week:MarkupsModuleSummer2013| Markups/Annotations rewrite]] (Nicole Aucoin)
* Provenance
* [[2013_Summer_Project_Week:Patient_hierarchy | Patient hierarchy]] (Csaba Pinter, Andras Lasso, Steve Pieper)
* [[2013_Summer_Project_Week:CLI_Improvements | CLI Improvements (hierarchy nodes, related nodes, roles)]] (Andras Lasso, Csaba Pinter, Jc, Steve, Jim, ?)
* [[2013_Summer_Project_Week:Sample_Data | Sample Data]] (Steve Pieper, Jim Miller, Bill Lorensen, Jc)
* [[Plastimatch in NiPype]] (Paolo, Dave, Hans)
** look for commonalities/reuse of CompareVolumes
* iPython in Slicer (Hans, Jc, Dave)
* [[2013_Summer_Project_Week:Optimizing start time of slicer| Optimizing start time of slicer]] (Jc, Steve)
* [[Common resampling and conversion utility functions in Slicer]] (Csaba Pinter, Steve Pieper, Hans, Kevin Wang)
* [[2013_Summer_Project_Week:CLI_modules_in_MeVisLab| Integrating CTK CLI modules into MeVisLab]] (Hans Meine, Steve, Jc)
*[[2013_Project_Week:WebbasedAnatomicalTeachingFrameworkSummer2013|Web-based anatomical teaching framework]] (Daniel Haehn, Steve Pieper, Rudolph Pienaar, Lilla Zollei, Nathaniel Reynolds)

==='''Brain & Spine Segmentation'''===
* [[2013_Summer_Project_Week:Multi_Atlas_Based_Multi_Image_Segmentation | Multi-Atlas-Based Multi-Image Segmentation for Brain MR Images]] (Minjeong Kim, Xiaofeng Liu, Jim Miller, Dinggang Shen)
* [[2013_Summer_Project_Week:Radnostics |Spine Segmentation & Osteoporosis Detection In CT Imaging Studies]] (Anthony Blumfield, Ron Kikinis)

===""Chronic Obstructive Pulmonary Disease""===
* [[2013_Summer_Project_Week:Airway_Inspector_Porting | Porting Airway Inspector to Slicer 4]] (Raul San Jose, Demian Wassermann, Rola Harmouche)
* [[2013_Summer_Project_Week:MRML_Infrastructure_Airway_Inspector | Airway Inspector: Slicer Extension and MRML Infrastructure]] (Demian Wassermann, Raul San Jose, Rola Harmouche)
* [[2013_Summer_Project_Week:Nipype_CLI_Integration | Integration of Nipype with CLI modules in the Chest Imaging Platform Library ]] (Rola Harmouche,Demian Wassermann, Raul San Jose)

== '''Background''' ==

We are pleased to announce the 17th PROJECT WEEK of hands-on research and development activity for applications in Neuroscience, Image-Guided Therapy and several additional areas of biomedical research that enable personalized medicine. Participants will engage in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithm design, medical imaging sequence development, tracking experiments, and clinical application. The main goal of this event is to move forward the translational research deliverables of the sponsoring centers and their collaborators. Active and potential collaborators are encouraged and welcome to attend this event. This event will be set up to maximize informal interaction between participants. If you would like to learn more about this event, please [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week click here to join our mailing list].

Active preparation begins on Thursday, April 25th at 3pm ET, with a kick-off teleconference. Invitations to this call will be sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties who have expressed an interest in working with these centers. The main goal of the kick-off call is to get an idea of which groups/projects will be active at the upcoming event, and to ensure that there is sufficient coverage for all. Subsequent teleconferences will allow for more focused discussions on individual projects and allow the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in breakout sessions. In the final days leading upto the meeting, all project teams will be asked to fill in a template page on this wiki that describes the objectives and plan of their projects.

The event itself will start off with a short presentation by each project team, driven using their previously created description, and will help all participants get acquainted with others who are doing similar work. In the rest of the week, about half the time will be spent in breakout discussions on topics of common interest of subsets of the attendees, and the other half will be spent in project teams, doing hands-on project work. The hands-on activities will be done in 40-50 small teams of size 2-4, each with a mix of multi-disciplinary expertise. To facilitate this work, a large room at MIT will be setup with several tables, with internet and power access, and each computer software development based team will gather on a table with their individual laptops, connect to the internet to download their software and data, and be able to work on their projects. Teams working on projects that require the use of medical devices will proceed to Brigham and Women's Hospital and carry out their experiments there. On the last day of the event, a closing presentation session will be held in which each project team will present a summary of what they accomplished during the week.

This event is part of the translational research efforts of [http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu/ NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], [http://www.cimit.org CIMIT], and OCAIRO. It is an expansion of the NA-MIC Summer Project Week that has been held annually since 2005. It will be held every summer at MIT and Brigham and Womens Hospital in Boston, typically during the last full week of June, and in Salt Lake City in the winter, typically during the second week of January.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

== '''Logistics''' ==

*'''Dates:''' June 17-21, 2013.
*'''Location:''' MIT.
*'''REGISTRATION:''' http://www.regonline.com/namic2013summerprojweek. Please note that as you proceed to the checkout portion of the registration process, RegOnline will offer you a chance to opt into a free trial of ACTIVEAdvantage -- click on "No thanks" in order to finish your Project Week registration.
*'''Registration Fee:''' $300.
*'''Hotel:''' Similar to previous years, no rooms have been blocked in a particular hotel.
*'''Room sharing''': If interested, add your name to the list before May 27th. See [[2013_Summer_Project_Week/RoomSharing|here]]

== '''Preparation''' ==

# Please make sure that you are on the http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week mailing list
# The NA-MIC engineering team will be discussing projects in a their [http://wiki.na-mic.org/Wiki/index.php/Engineering:TCON_2013 weekly teleconferences]. Participants from the above mailing list will be invited to join to discuss their projects, so please make sure you are on it!
# By 3pm ET on Thursday May 8, all participants to add a one line title of their project to #Projects
# By 3pm ET on Thursday June 6, all project leads to complete [[Project_Week/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.
# By 3pm on June 13: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Matt)
## 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. XNAT/MIDAS). 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.)
## Where possible, 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. (Matt)
# 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...
# People doing Slicer related projects should come to project week with slicer built on your laptop.
## See the [http://www.slicer.org/slicerWiki/index.php/Documentation/4.0/Developers Developer Section of slicer.org] for information.
## Projects to develop extension modules should be built against the latest Slicer4 trunk.

== '''Registrants''' ==

Do not add your name to this list - it is maintained by the organizers based on your paid registration. ([http://www.regonline.com/Register/Checkin.aspx?EventID=1233699 Please click here to register.])

#Charles Anderson, BWH, canderson26@partners.org
#Peter Anderson, retired, traneus@verizon.net
#Nicole Aucoin, BWH, nicole@bwh.harvard.edu
#Juan Carlos Avila Vilchis, Univ del Estado de Mexico, jc.avila.vilchis@hotmail.com
#Salma Bengali, Univ UT, salma.bengali@carma.utah.edu
#Anthony Blumfield, Radnostics, Anthony.Blumfield@Radnostics.com
#Vinicius Boen, Univ Michigan, vboen@umich.edu
#Matthew Brennan, MIT, brennanm@mit.edu
#Francois Budin, NIRAL-UNC, fbudin@unc.edu
#Ivan Buzurovic, BWH/HMS, ibuzurovic@lroc.harvard.edu
#Josh Cates, Univ UT, cates@sci.utah.edu
#Micah Chambers, UCLA, micahcc@ucla.edu
#Marine Clogenson, Ecole Polytechnique Federale de Lausanne (Switzerland), marine.clogenson@epfl.ch
#Adrian Dalca, MIT, adalca@MIT.EDU
#Manasi Datar, Univ UT-SCI Institute, datar@sci.utah.edu
#Sneha Durgapal, BWH, durgapalsneha@gmail.com
#Luping Fang, Zhejiang Univ of Technology (China), flp@zjut.edu.cn
#Andriy Fedorov, BWH, fedorov@bwh.harvard.edu
#Jean-Christophe Fillion-Robin, Kitware, jchris.fillionr@kitware.com
#Gregory Fischer, WPI, gfischer@wpi.edu
#Karl Fritscher, MGH, kfritscher@gmail.com
#Yi Gao, Univ AL Birmingham, gaoyi.cn@gmail.com
#Daniel Haehn, Boston Childrens Hospital, daniel.haehn@childrens.harvard.edu
#Michael Halle, BWH-SPL, mhalle@bwh.harvard.edu
#Rola Harmouche, BWH, rharmo@bwh.harvard.edu
#Nobuhiko Hata, BWH, hata@bwh.harvard.edu
#Nicholas Herlambang, AZE Technology Inc, nicholas.herlambang@azetech.com
#Matthew Holden, Queen's Univ (Canada), mholden8@cs.queensu.ca
#Andrei Irimia, UCLA, andrei.irimia@loni.ucla.edu
#Jayender Jagadeesan, BWH-SPL, jayender@bwh.harvard.edu
#Hans Johnson, Univ Iowa, hans-johnson@uiowa.edu
#Tina Kapur, BWH/HMS, tkapur@bwh.harvard.edu
#Ron Kikinis, HMS, kikinis@bwh.harvard.edu
#Daniel Kostro, BWH, dkostro@bwh.harvard.edu
#Andras Lasso, Queen's Univ (Canada), lasso@cs.queensu.ca
#Rui Li, GE Global Research, li.rui@ge.com
#Xu Li, BWH, lixu0103@gmail.com
#Sidong Liu, Univ Sydney (Australia), sliu7418@uni.sydney.edu.au
#William Lorensen, Bill's Basement, bill.lorensen@gmail.com
#Bradley Lowekamp, Medical Science & Computing Inc, bradley.lowekamp@nih.gov
#Athena Lyons, Univ Western Australia, 20359511@student.uwa.edu.au
#Katie Mastrogiacomo, BWH - SPL, kmast@bwh.harvard.edu
#Alireza Mehrtash, BWH - SPL, mehrtash@bwh.harvard.edu
#Hans Meine, Fraunhofer MEVIS (Germany), hans.meine@mevis.fraunhofer.de
#Jim Miller, GE Global Research, millerjv@ge.com
#Luis Murta, Univ Sao Paulo (Brazil), lomurta@gmail.com
#Arie Nakhmani, Univ AL Birmingham, anry@uab.edu
#Isaiah Norton, BWH, inorton@bwh.harvard.edu
#Lauren O'Donnell, BWH, odonnell@bwh.harvard.edu
#Dirk Padfield, GE Global Research, padfield@research.ge.com
#Jian Pan, Zhejiang Univ of Technology (China), pj@zjut.edu.cn
#Steve Pieper, Isomics Inc, pieper@isomics.com
#Csaba Pinter, Queen's Univ (Canada), pinter@cs.queensu.ca
#Sonia Pujol, HMS, spujol@bwh.harvard.edu
#Adam Rankin, Queen's Univ (Canada), rankin@cs.queensu.ca
#Nathaniel Reynolds, MGH, reynolds@nmr.mgh.harvard.edu
#Raul San Jose, BWH, rjosest@bwh.harvard.edu
#Anuja Sharma, Univ UT-SCI Institute, anuja@cs.utah.edu
#Greg Sharp, MGH, gcsharp@partners.org
#Nadya Shusharina, MGH, nshusharina@partners.org
#Sebastian Tauscher, Leibniz Univ Hannover (Germany), sebastian.tauscher@imes.uni-hannover.de
#Matthew Toews, BWH/HMS, mt@bwh.harvard.edu
#Junichi Tokuda, BWH, tokuda@bwh.harvard.edu
#Tamas Ungi, Queen's Univ (Canada), ungi@cs.queensu.ca
#Adriana Vilchis González, Univ del Estado de Mexico, hvigady@hotmail.com
#Christian Wachinger, MIT, wachinge@mit.edu
#Bo Wang, Univ UT-SCI Institute, bowang@sci.utah.edu
#Demian Wassermann, BWH, demian@bwh.harvard.edu
#David Welch, Univ Iowa, david-welch@uiowa.edu
#William Wells, BWH/HMS, sw@bwh.harvard.edu
#Phillip White, BWH/HMS, white@bwh.harvard.edu
#Alex Yarmarkovich, Isomics Inc, alexy@bwh.harvard.edu
#Yang Yu, Rutgers Univ, yyu@cs.rutgers.edu
#Paolo Zaffino, Univ Magna Graecia of Catanzaro (Italy), p.zaffino@unicz.it
#Lilla Zollei, MGH, lzollei@nmr.mgh.harvard.edu

Algorithm:Utah

2012-11-26T17:53:14Z

Manasi: /* Mixed-Effects Shape Models for Longitudinal Analysis */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

|-
{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
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== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.

|-
| style="width:15%" | [[Image:EPI.png|200px]]
| style="width:85%" |

== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

 R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.

|-

| | [[Image:pipeline.png|150px]]
| |

== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==

This framework addresses the simultaneous alignment and ﬁltering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

 Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.

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| | [[Image:Sulcaldepth.png|200px]]
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== [[Projects:CorticalCorrespondenceWithParticleSystem|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. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

 Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
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== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

 Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

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| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

 Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

|-

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

| | [[Image:NonRegularSurfCorres.png|200px]]
| |

== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

M Datar, Y Gur, B Paniagua, M Styner, R Whitaker,Geometric Correspondence for Ensembles of Nonregular Shapes, MICCAI 2011.

|-

| | [[Image:MixedEffectsShape.png|200px]]
| |

== [[Projects:MixedEffectsShape|Mixed-Effects Shape Models for Longitudinal Analysis]] ==

Longitudinal shape changes in anatomy are characterized using a new method that combines point correspondences across shapes with the statistical modeling of individual and population trends via the linear mixed-effects model. This method helps us examine and contrast population trends with individual growth trajectories.

'''New: ''' M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher,Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy, STIA 2012

|-

| | [[Image:CamFAIAnalysis.png|200px]]
| |

== Statistical Shape Analysis of Cam-FAI ==

Cam femoroacetabular impingement (FAI) is characterized by a malformed femoral head that may lead to early hip osteoarthritis. Radiographic measurements are used to diagnose cam FAI and often assume the femur shape to be spherical. Statistical shape modeling (SSM) can be used to compare complex 3D morphology without the need to assume ideal geometry and quantify morphologic differences between control and FAI femurs.

'''New: ''' MD Harris, M Datar, E Jurrus, CL Peters, RT Whitaker, AE Anderson, [http://www.cs.utah.edu/~manasi/pubs/CM165P.pdf|Statistical Shape Modeling of CAM-type Femoroacetabular Impingement], CMBBE 2012

|-

| | [[Image:MiceMOAnalysis.png|200px]]
| |

== Understanding Short Bone Phenotype in Multiple Osteochondromas ==

Novel statistical methods were developed to study the 'steal phenomenon' caused by multiple osteochondromas in mouse models. Bone lengths and volumes were compared. Metaphyseal volume deviations from normal, as a measure of osteochondroma volumetric growth, were correlated with length deviations.

'''New: ''' KB Jones, M Datar, S Ravichandran, H Jin, E Jurrus, RT Whitaker, MR Capecchi, Toward an Understanding of the Short Bone Phenotype Associated with Multiple Osteochondromas, JOR 2012 (to appear)

|-

| | [[Image:FiberTracts-angle.jpg|200px]]
| |

== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

|-
| style="width:15%" | [[Image:DTIFiltering.jpg|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-

| | [[Image:Brain-seg-utah.png|200px]]
| |

== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

|-

| | [[Image:combined_50_seg_labeled.png|200px]]
| |

== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find the kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of the labels of all images in the dataset, which can give an accurate atlas for the target.

 P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

|}

Algorithm:Utah

2012-11-26T17:52:56Z

Manasi: /* Geometric Correspondence for Nonregular Surfaces */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

|-
{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.

|-
| style="width:15%" | [[Image:EPI.png|200px]]
| style="width:85%" |

== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

 R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.

|-

| | [[Image:pipeline.png|150px]]
| |

== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==

This framework addresses the simultaneous alignment and ﬁltering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

 Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.

|-

| | [[Image:Sulcaldepth.png|200px]]
| |

== [[Projects:CorticalCorrespondenceWithParticleSystem|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. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

 Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
| |

== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

 Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

 Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

|-

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

| | [[Image:NonRegularSurfCorres.png|200px]]
| |

== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

M Datar, Y Gur, B Paniagua, M Styner, R Whitaker,Geometric Correspondence for Ensembles of Nonregular Shapes, MICCAI 2011.

|-

| | [[Image:MixedEffectsShape.png|200px]]
| |

== [[Projects:MixedEffectsShape|Mixed-Effects Shape Models for Longitudinal Analysis]] ==

Longitudinal shape changes in anatomy are characterized using a new method that combines point correspondences across shapes with the statistical modeling of individual and population trends via the linear mixed-effects model. This method helps us examine and contrast population trends with individual growth trajectories.

'''New: ''' M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher, [http://www.cs.utah.edu/~manasi/pubs/MixedEffectsSTIA2012.pdf|Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy], STIA 2012

|-

| | [[Image:CamFAIAnalysis.png|200px]]
| |

== Statistical Shape Analysis of Cam-FAI ==

Cam femoroacetabular impingement (FAI) is characterized by a malformed femoral head that may lead to early hip osteoarthritis. Radiographic measurements are used to diagnose cam FAI and often assume the femur shape to be spherical. Statistical shape modeling (SSM) can be used to compare complex 3D morphology without the need to assume ideal geometry and quantify morphologic differences between control and FAI femurs.

'''New: ''' MD Harris, M Datar, E Jurrus, CL Peters, RT Whitaker, AE Anderson, [http://www.cs.utah.edu/~manasi/pubs/CM165P.pdf|Statistical Shape Modeling of CAM-type Femoroacetabular Impingement], CMBBE 2012

|-

| | [[Image:MiceMOAnalysis.png|200px]]
| |

== Understanding Short Bone Phenotype in Multiple Osteochondromas ==

Novel statistical methods were developed to study the 'steal phenomenon' caused by multiple osteochondromas in mouse models. Bone lengths and volumes were compared. Metaphyseal volume deviations from normal, as a measure of osteochondroma volumetric growth, were correlated with length deviations.

'''New: ''' KB Jones, M Datar, S Ravichandran, H Jin, E Jurrus, RT Whitaker, MR Capecchi, Toward an Understanding of the Short Bone Phenotype Associated with Multiple Osteochondromas, JOR 2012 (to appear)

|-

| | [[Image:FiberTracts-angle.jpg|200px]]
| |

== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

|-
| style="width:15%" | [[Image:DTIFiltering.jpg|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-

| | [[Image:Brain-seg-utah.png|200px]]
| |

== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

|-

| | [[Image:combined_50_seg_labeled.png|200px]]
| |

== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find the kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of the labels of all images in the dataset, which can give an accurate atlas for the target.

 P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

|}

Projects:MixedEffectsShape

2012-11-26T17:52:31Z

Manasi: /* Publications */

Back to [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Mixed-Effects Shape Models for Longitudinal Analysis =

{|
|[[Image:MixedEffectsShape.png|thumb|450px|Visualization of fixed- and random-effects for brain surfaces.]]
|}

= Description =
We propose a new method for longitudinal shape analysis that fits a linear mixed-effects model, while simultaneously optimizing correspondences on a set of anatomical shapes. Shape changes are modeled in a hierarchical fashion, with the global population trend as a fixed effect and individual trends as random effects. The statistical significance of the estimated trends are evaluated using specifically designed permutation tests. We also develop a permutation test based on the Hotelling T2 statistic to compare the average shapes trends between two populations. We demonstrate the benefits of our method on a synthetic example of longitudinal tori and data from a developmental neuroimaging study.

= Key Investigators =

* Utah: Manasi Datar, Prasanna Muralidharan, Sylvain Gouttard, Guido Gerig, Ross Whitaker and P. Thomas Fletcher

= Publications =

* M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher, [http://www.cs.utah.edu/~manasi/pubs/MixedEffectsSTIA2012.pdf|Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy], STIA 2012

[[Category:Shape Analysis]] [[Category:Statistics]]

Algorithm:Utah

2012-11-26T17:51:43Z

Manasi: /* Geometric Correspondence for Nonregular Surfaces */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

|-
{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.

|-
| style="width:15%" | [[Image:EPI.png|200px]]
| style="width:85%" |

== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

 R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.

|-

| | [[Image:pipeline.png|150px]]
| |

== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==

This framework addresses the simultaneous alignment and ﬁltering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

 Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.

|-

| | [[Image:Sulcaldepth.png|200px]]
| |

== [[Projects:CorticalCorrespondenceWithParticleSystem|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. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

 Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
| |

== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

 Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

 Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

|-

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

| | [[Image:NonRegularSurfCorres.png|200px]]
| |

== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, [http://www.cs.utah.edu/~manasi/pubs/ShapeWorksMICCAI2011.pdf|Geometric Correspondence for Ensembles of Nonregular Shapes], MICCAI 2011.

|-

| | [[Image:MixedEffectsShape.png|200px]]
| |

== [[Projects:MixedEffectsShape|Mixed-Effects Shape Models for Longitudinal Analysis]] ==

Longitudinal shape changes in anatomy are characterized using a new method that combines point correspondences across shapes with the statistical modeling of individual and population trends via the linear mixed-effects model. This method helps us examine and contrast population trends with individual growth trajectories.

'''New: ''' M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher, [http://www.cs.utah.edu/~manasi/pubs/MixedEffectsSTIA2012.pdf|Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy], STIA 2012

|-

| | [[Image:CamFAIAnalysis.png|200px]]
| |

== Statistical Shape Analysis of Cam-FAI ==

Cam femoroacetabular impingement (FAI) is characterized by a malformed femoral head that may lead to early hip osteoarthritis. Radiographic measurements are used to diagnose cam FAI and often assume the femur shape to be spherical. Statistical shape modeling (SSM) can be used to compare complex 3D morphology without the need to assume ideal geometry and quantify morphologic differences between control and FAI femurs.

'''New: ''' MD Harris, M Datar, E Jurrus, CL Peters, RT Whitaker, AE Anderson, [http://www.cs.utah.edu/~manasi/pubs/CM165P.pdf|Statistical Shape Modeling of CAM-type Femoroacetabular Impingement], CMBBE 2012

|-

| | [[Image:MiceMOAnalysis.png|200px]]
| |

== Understanding Short Bone Phenotype in Multiple Osteochondromas ==

Novel statistical methods were developed to study the 'steal phenomenon' caused by multiple osteochondromas in mouse models. Bone lengths and volumes were compared. Metaphyseal volume deviations from normal, as a measure of osteochondroma volumetric growth, were correlated with length deviations.

'''New: ''' KB Jones, M Datar, S Ravichandran, H Jin, E Jurrus, RT Whitaker, MR Capecchi, Toward an Understanding of the Short Bone Phenotype Associated with Multiple Osteochondromas, JOR 2012 (to appear)

|-

| | [[Image:FiberTracts-angle.jpg|200px]]
| |

== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

|-
| style="width:15%" | [[Image:DTIFiltering.jpg|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-

| | [[Image:Brain-seg-utah.png|200px]]
| |

== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

|-

| | [[Image:combined_50_seg_labeled.png|200px]]
| |

== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find the kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of the labels of all images in the dataset, which can give an accurate atlas for the target.

 P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

|}

Algorithm:Utah

2012-11-26T17:50:57Z

Manasi: /* Mixed-Effects Shape Models for Longitudinal Analysis */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

|-
{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.

|-
| style="width:15%" | [[Image:EPI.png|200px]]
| style="width:85%" |

== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

 R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.

|-

| | [[Image:pipeline.png|150px]]
| |

== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==

This framework addresses the simultaneous alignment and ﬁltering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

 Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.

|-

| | [[Image:Sulcaldepth.png|200px]]
| |

== [[Projects:CorticalCorrespondenceWithParticleSystem|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. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

 Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
| |

== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

 Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

 Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

|-

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

| | [[Image:NonRegularSurfCorres.png|200px]]
| |

== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, Geometric Correspondence for Ensembles of Nonregular Shapes, MICCAI 2011.

|-

| | [[Image:MixedEffectsShape.png|200px]]
| |

== [[Projects:MixedEffectsShape|Mixed-Effects Shape Models for Longitudinal Analysis]] ==

Longitudinal shape changes in anatomy are characterized using a new method that combines point correspondences across shapes with the statistical modeling of individual and population trends via the linear mixed-effects model. This method helps us examine and contrast population trends with individual growth trajectories.

'''New: ''' M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher, [http://www.cs.utah.edu/~manasi/pubs/MixedEffectsSTIA2012.pdf|Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy], STIA 2012

|-

| | [[Image:CamFAIAnalysis.png|200px]]
| |

== Statistical Shape Analysis of Cam-FAI ==

Cam femoroacetabular impingement (FAI) is characterized by a malformed femoral head that may lead to early hip osteoarthritis. Radiographic measurements are used to diagnose cam FAI and often assume the femur shape to be spherical. Statistical shape modeling (SSM) can be used to compare complex 3D morphology without the need to assume ideal geometry and quantify morphologic differences between control and FAI femurs.

'''New: ''' MD Harris, M Datar, E Jurrus, CL Peters, RT Whitaker, AE Anderson, [http://www.cs.utah.edu/~manasi/pubs/CM165P.pdf|Statistical Shape Modeling of CAM-type Femoroacetabular Impingement], CMBBE 2012

|-

| | [[Image:MiceMOAnalysis.png|200px]]
| |

== Understanding Short Bone Phenotype in Multiple Osteochondromas ==

Novel statistical methods were developed to study the 'steal phenomenon' caused by multiple osteochondromas in mouse models. Bone lengths and volumes were compared. Metaphyseal volume deviations from normal, as a measure of osteochondroma volumetric growth, were correlated with length deviations.

'''New: ''' KB Jones, M Datar, S Ravichandran, H Jin, E Jurrus, RT Whitaker, MR Capecchi, Toward an Understanding of the Short Bone Phenotype Associated with Multiple Osteochondromas, JOR 2012 (to appear)

|-

| | [[Image:FiberTracts-angle.jpg|200px]]
| |

== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

|-
| style="width:15%" | [[Image:DTIFiltering.jpg|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-

| | [[Image:Brain-seg-utah.png|200px]]
| |

== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

|-

| | [[Image:combined_50_seg_labeled.png|200px]]
| |

== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find the kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of the labels of all images in the dataset, which can give an accurate atlas for the target.

 P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

|}

Algorithm:Utah

2012-11-16T20:51:42Z

Manasi: /* Understanding Short Bone Phenotype in Multiple Osteochondromas */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

|-
{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.

|-
| style="width:15%" | [[Image:EPI.png|200px]]
| style="width:85%" |

== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

 R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.

|-

| | [[Image:pipeline.png|150px]]
| |

== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==

This framework addresses the simultaneous alignment and ﬁltering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

 Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.

|-

| | [[Image:Sulcaldepth.png|200px]]
| |

== [[Projects:CorticalCorrespondenceWithParticleSystem|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. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

 Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
| |

== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

 Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

 Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

|-

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

| | [[Image:NonRegularSurfCorres.png|200px]]
| |

== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, Geometric Correspondence for Ensembles of Nonregular Shapes, MICCAI 2011.

|-

| | [[Image:MixedEffectsShape.png|200px]]
| |

== [[Projects:MixedEffectsShape|Mixed-Effects Shape Models for Longitudinal Analysis]] ==

Longitudinal shape changes in anatomy are characterized using a new method that combines point correspondences across shapes with the statistical modeling of individual and population trends via the linear mixed-effects model. This method helps us examine and contrast population trends with individual growth trajectories.

'''New: ''' M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher, Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy, STIA 2012

|-

| | [[Image:CamFAIAnalysis.png|200px]]
| |

== Statistical Shape Analysis of Cam-FAI ==

Cam femoroacetabular impingement (FAI) is characterized by a malformed femoral head that may lead to early hip osteoarthritis. Radiographic measurements are used to diagnose cam FAI and often assume the femur shape to be spherical. Statistical shape modeling (SSM) can be used to compare complex 3D morphology without the need to assume ideal geometry and quantify morphologic differences between control and FAI femurs.

'''New: ''' MD Harris, M Datar, E Jurrus, CL Peters, RT Whitaker, AE Anderson, [http://www.cs.utah.edu/~manasi/pubs/CM165P.pdf|Statistical Shape Modeling of CAM-type Femoroacetabular Impingement], CMBBE 2012

|-

| | [[Image:MiceMOAnalysis.png|200px]]
| |

== Understanding Short Bone Phenotype in Multiple Osteochondromas ==

Novel statistical methods were developed to study the 'steal phenomenon' caused by multiple osteochondromas in mouse models. Bone lengths and volumes were compared. Metaphyseal volume deviations from normal, as a measure of osteochondroma volumetric growth, were correlated with length deviations.

'''New: ''' KB Jones, M Datar, S Ravichandran, H Jin, E Jurrus, RT Whitaker, MR Capecchi, Toward an Understanding of the Short Bone Phenotype Associated with Multiple Osteochondromas, JOR 2012 (to appear)

|-

| | [[Image:FiberTracts-angle.jpg|200px]]
| |

== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

|-
| style="width:15%" | [[Image:DTIFiltering.jpg|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-

| | [[Image:Brain-seg-utah.png|200px]]
| |

== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

|-

| | [[Image:combined_50_seg_labeled.png|200px]]
| |

== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find the kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of the labels of all images in the dataset, which can give an accurate atlas for the target.

 P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

|}

Algorithm:Utah

2012-11-16T20:49:15Z

Manasi: /* Understanding Short Bone Phenotype in Multiple Osteochondromas */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

|-
{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.

|-
| style="width:15%" | [[Image:EPI.png|200px]]
| style="width:85%" |

== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

 R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.

|-

| | [[Image:pipeline.png|150px]]
| |

== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==

This framework addresses the simultaneous alignment and ﬁltering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

 Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.

|-

| | [[Image:Sulcaldepth.png|200px]]
| |

== [[Projects:CorticalCorrespondenceWithParticleSystem|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. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

 Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
| |

== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

 Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

 Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

|-

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

| | [[Image:NonRegularSurfCorres.png|200px]]
| |

== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, Geometric Correspondence for Ensembles of Nonregular Shapes, MICCAI 2011.

|-

| | [[Image:MixedEffectsShape.png|200px]]
| |

== [[Projects:MixedEffectsShape|Mixed-Effects Shape Models for Longitudinal Analysis]] ==

Longitudinal shape changes in anatomy are characterized using a new method that combines point correspondences across shapes with the statistical modeling of individual and population trends via the linear mixed-effects model. This method helps us examine and contrast population trends with individual growth trajectories.

'''New: ''' M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher, Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy, STIA 2012

|-

| | [[Image:CamFAIAnalysis.png|200px]]
| |

== Statistical Shape Analysis of Cam-FAI ==

Cam femoroacetabular impingement (FAI) is characterized by a malformed femoral head that may lead to early hip osteoarthritis. Radiographic measurements are used to diagnose cam FAI and often assume the femur shape to be spherical. Statistical shape modeling (SSM) can be used to compare complex 3D morphology without the need to assume ideal geometry and quantify morphologic differences between control and FAI femurs.

'''New: ''' MD Harris, M Datar, E Jurrus, CL Peters, RT Whitaker, AE Anderson, [http://www.cs.utah.edu/~manasi/pubs/CM165P.pdf|Statistical Shape Modeling of CAM-type Femoroacetabular Impingement], CMBBE 2012

|-

| | [[Image:MiceMOAnalysis.png|200px]]
| |

== Understanding Short Bone Phenotype in Multiple Osteochondromas ==

Novel statistical methods were developed to study the 'steal phenomenon' caused by multiple osteochondromas in mouse models. Bone lengths and volumes were compared. Metaphyseal volume deviations from normal, as a measure of osteochondroma volumetric growth, were correlated with length deviations.

New: KB Jones, M Datar, S Ravichandran, H Jin, E Jurrus, RT Whitaker, MR Capecchi, Toward an Understanding of the Short Bone Phenotype Associated with Multiple Osteochondromas, JOR 2012 (to appear)

|-

| | [[Image:FiberTracts-angle.jpg|200px]]
| |

== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

|-
| style="width:15%" | [[Image:DTIFiltering.jpg|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-

| | [[Image:Brain-seg-utah.png|200px]]
| |

== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

|-

| | [[Image:combined_50_seg_labeled.png|200px]]
| |

== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find the kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of the labels of all images in the dataset, which can give an accurate atlas for the target.

 P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

|}

Algorithm:Utah

2012-11-16T20:48:47Z

Manasi: /* Statistical Shape Analysis of Cam-FAI */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

|-
{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.

|-
| style="width:15%" | [[Image:EPI.png|200px]]
| style="width:85%" |

== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

 R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.

|-

| | [[Image:pipeline.png|150px]]
| |

== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==

This framework addresses the simultaneous alignment and ﬁltering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

 Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.

|-

| | [[Image:Sulcaldepth.png|200px]]
| |

== [[Projects:CorticalCorrespondenceWithParticleSystem|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. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

 Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
| |

== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

 Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

 Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

|-

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

| | [[Image:NonRegularSurfCorres.png|200px]]
| |

== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, Geometric Correspondence for Ensembles of Nonregular Shapes, MICCAI 2011.

|-

| | [[Image:MixedEffectsShape.png|200px]]
| |

== [[Projects:MixedEffectsShape|Mixed-Effects Shape Models for Longitudinal Analysis]] ==

Longitudinal shape changes in anatomy are characterized using a new method that combines point correspondences across shapes with the statistical modeling of individual and population trends via the linear mixed-effects model. This method helps us examine and contrast population trends with individual growth trajectories.

'''New: ''' M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher, Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy, STIA 2012

|-

| | [[Image:CamFAIAnalysis.png|200px]]
| |

== Statistical Shape Analysis of Cam-FAI ==

Cam femoroacetabular impingement (FAI) is characterized by a malformed femoral head that may lead to early hip osteoarthritis. Radiographic measurements are used to diagnose cam FAI and often assume the femur shape to be spherical. Statistical shape modeling (SSM) can be used to compare complex 3D morphology without the need to assume ideal geometry and quantify morphologic differences between control and FAI femurs.

'''New: ''' MD Harris, M Datar, E Jurrus, CL Peters, RT Whitaker, AE Anderson, [http://www.cs.utah.edu/~manasi/pubs/CM165P.pdf|Statistical Shape Modeling of CAM-type Femoroacetabular Impingement], CMBBE 2012

|-

| | [[Image:MiceMOAnalysis.png|200px]]
| |

== Understanding Short Bone Phenotype in Multiple Osteochondromas ==

Novel statistical methods were developed to study the 'steal phenomenon' caused by multiple osteochondromas in mouse models. Bone lengths and volumes were compared. Metaphyseal volume deviations from normal, as a measure of osteochondroma volumetric growth, were correlated with length deviations.

 KB Jones, M Datar, S Ravichandran, H Jin, E Jurrus, RT Whitaker, MR Capecchi, Toward an Understanding of the Short Bone Phenotype Associated with Multiple Osteochondromas, JOR 2012 (to appear)

|-

| | [[Image:FiberTracts-angle.jpg|200px]]
| |

== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

|-
| style="width:15%" | [[Image:DTIFiltering.jpg|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-

| | [[Image:Brain-seg-utah.png|200px]]
| |

== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

|-

| | [[Image:combined_50_seg_labeled.png|200px]]
| |

== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find the kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of the labels of all images in the dataset, which can give an accurate atlas for the target.

 P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

|}

Algorithm:Utah

2012-11-16T20:48:30Z

Manasi: /* Statistical Shape Analysis of Cam-FAI */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

|-
{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.

|-
| style="width:15%" | [[Image:EPI.png|200px]]
| style="width:85%" |

== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

 R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.

|-

| | [[Image:pipeline.png|150px]]
| |

== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==

This framework addresses the simultaneous alignment and ﬁltering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

 Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.

|-

| | [[Image:Sulcaldepth.png|200px]]
| |

== [[Projects:CorticalCorrespondenceWithParticleSystem|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. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

 Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
| |

== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

 Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

 Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

|-

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

| | [[Image:NonRegularSurfCorres.png|200px]]
| |

== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, Geometric Correspondence for Ensembles of Nonregular Shapes, MICCAI 2011.

|-

| | [[Image:MixedEffectsShape.png|200px]]
| |

== [[Projects:MixedEffectsShape|Mixed-Effects Shape Models for Longitudinal Analysis]] ==

Longitudinal shape changes in anatomy are characterized using a new method that combines point correspondences across shapes with the statistical modeling of individual and population trends via the linear mixed-effects model. This method helps us examine and contrast population trends with individual growth trajectories.

'''New: ''' M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher, Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy, STIA 2012

|-

| | [[Image:CamFAIAnalysis.png|200px]]
| |

== Statistical Shape Analysis of Cam-FAI ==

Cam femoroacetabular impingement (FAI) is characterized by a malformed femoral head that may lead to early hip osteoarthritis. Radiographic measurements are used to diagnose cam FAI and often assume the femur shape to be spherical. Statistical shape modeling (SSM) can be used to compare complex 3D morphology without the need to assume ideal geometry and quantify morphologic differences between control and FAI femurs.

'''New: ''' MD Harris, M Datar, E Jurrus, CL Peters, RT Whitaker, AE Anderson, [http://www.cs.utah.edu/~manasi/pubs/CM165P.pdf|Statistical Shape Modeling of CAM-type Femoroacetabular Impingement, CMBBE 2012

|-

| | [[Image:MiceMOAnalysis.png|200px]]
| |

== Understanding Short Bone Phenotype in Multiple Osteochondromas ==

Novel statistical methods were developed to study the 'steal phenomenon' caused by multiple osteochondromas in mouse models. Bone lengths and volumes were compared. Metaphyseal volume deviations from normal, as a measure of osteochondroma volumetric growth, were correlated with length deviations.

 KB Jones, M Datar, S Ravichandran, H Jin, E Jurrus, RT Whitaker, MR Capecchi, Toward an Understanding of the Short Bone Phenotype Associated with Multiple Osteochondromas, JOR 2012 (to appear)

|-

| | [[Image:FiberTracts-angle.jpg|200px]]
| |

== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

|-
| style="width:15%" | [[Image:DTIFiltering.jpg|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-

| | [[Image:Brain-seg-utah.png|200px]]
| |

== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

|-

| | [[Image:combined_50_seg_labeled.png|200px]]
| |

== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find the kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of the labels of all images in the dataset, which can give an accurate atlas for the target.

 P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

|}

Algorithm:Utah

2012-11-16T20:47:45Z

Manasi: /* Geometric Correspondence for Nonregular Surfaces */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

|-
{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.

|-
| style="width:15%" | [[Image:EPI.png|200px]]
| style="width:85%" |

== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

 R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.

|-

| | [[Image:pipeline.png|150px]]
| |

== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==

This framework addresses the simultaneous alignment and ﬁltering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

 Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.

|-

| | [[Image:Sulcaldepth.png|200px]]
| |

== [[Projects:CorticalCorrespondenceWithParticleSystem|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. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

 Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
| |

== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

 Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

 Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

|-

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

| | [[Image:NonRegularSurfCorres.png|200px]]
| |

== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, Geometric Correspondence for Ensembles of Nonregular Shapes, MICCAI 2011.

|-

| | [[Image:MixedEffectsShape.png|200px]]
| |

== [[Projects:MixedEffectsShape|Mixed-Effects Shape Models for Longitudinal Analysis]] ==

Longitudinal shape changes in anatomy are characterized using a new method that combines point correspondences across shapes with the statistical modeling of individual and population trends via the linear mixed-effects model. This method helps us examine and contrast population trends with individual growth trajectories.

'''New: ''' M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher, Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy, STIA 2012

|-

| | [[Image:CamFAIAnalysis.png|200px]]
| |

== Statistical Shape Analysis of Cam-FAI ==

Cam femoroacetabular impingement (FAI) is characterized by a malformed femoral head that may lead to early hip osteoarthritis. Radiographic measurements are used to diagnose cam FAI and often assume the femur shape to be spherical. Statistical shape modeling (SSM) can be used to compare complex 3D morphology without the need to assume ideal geometry and quantify morphologic differences between control and FAI femurs.

 MD Harris, M Datar, E Jurrus, CL Peters, RT Whitaker, AE Anderson, Statistical Shape Modeling of CAM-type Femoroacetabular Impingement, CMBBE 2012

|-

| | [[Image:MiceMOAnalysis.png|200px]]
| |

== Understanding Short Bone Phenotype in Multiple Osteochondromas ==

Novel statistical methods were developed to study the 'steal phenomenon' caused by multiple osteochondromas in mouse models. Bone lengths and volumes were compared. Metaphyseal volume deviations from normal, as a measure of osteochondroma volumetric growth, were correlated with length deviations.

 KB Jones, M Datar, S Ravichandran, H Jin, E Jurrus, RT Whitaker, MR Capecchi, Toward an Understanding of the Short Bone Phenotype Associated with Multiple Osteochondromas, JOR 2012 (to appear)

|-

| | [[Image:FiberTracts-angle.jpg|200px]]
| |

== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

|-
| style="width:15%" | [[Image:DTIFiltering.jpg|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-

| | [[Image:Brain-seg-utah.png|200px]]
| |

== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

|-

| | [[Image:combined_50_seg_labeled.png|200px]]
| |

== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find the kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of the labels of all images in the dataset, which can give an accurate atlas for the target.

 P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

|}

Algorithm:Utah

2012-11-16T20:47:35Z

Manasi: /* Particle Based Shape Regression */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

|-
{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.

|-
| style="width:15%" | [[Image:EPI.png|200px]]
| style="width:85%" |

== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

 R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.

|-

| | [[Image:pipeline.png|150px]]
| |

== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==

This framework addresses the simultaneous alignment and ﬁltering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

 Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.

|-

| | [[Image:Sulcaldepth.png|200px]]
| |

== [[Projects:CorticalCorrespondenceWithParticleSystem|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. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

 Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
| |

== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

 Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

 Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

|-

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

| | [[Image:NonRegularSurfCorres.png|200px]]
| |

== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

 M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, Geometric Correspondence for Ensembles of Nonregular Shapes, MICCAI 2011.

|-

| | [[Image:MixedEffectsShape.png|200px]]
| |

== [[Projects:MixedEffectsShape|Mixed-Effects Shape Models for Longitudinal Analysis]] ==

Longitudinal shape changes in anatomy are characterized using a new method that combines point correspondences across shapes with the statistical modeling of individual and population trends via the linear mixed-effects model. This method helps us examine and contrast population trends with individual growth trajectories.

'''New: ''' M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher, Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy, STIA 2012

|-

| | [[Image:CamFAIAnalysis.png|200px]]
| |

== Statistical Shape Analysis of Cam-FAI ==

Cam femoroacetabular impingement (FAI) is characterized by a malformed femoral head that may lead to early hip osteoarthritis. Radiographic measurements are used to diagnose cam FAI and often assume the femur shape to be spherical. Statistical shape modeling (SSM) can be used to compare complex 3D morphology without the need to assume ideal geometry and quantify morphologic differences between control and FAI femurs.

 MD Harris, M Datar, E Jurrus, CL Peters, RT Whitaker, AE Anderson, Statistical Shape Modeling of CAM-type Femoroacetabular Impingement, CMBBE 2012

|-

| | [[Image:MiceMOAnalysis.png|200px]]
| |

== Understanding Short Bone Phenotype in Multiple Osteochondromas ==

Novel statistical methods were developed to study the 'steal phenomenon' caused by multiple osteochondromas in mouse models. Bone lengths and volumes were compared. Metaphyseal volume deviations from normal, as a measure of osteochondroma volumetric growth, were correlated with length deviations.

 KB Jones, M Datar, S Ravichandran, H Jin, E Jurrus, RT Whitaker, MR Capecchi, Toward an Understanding of the Short Bone Phenotype Associated with Multiple Osteochondromas, JOR 2012 (to appear)

|-

| | [[Image:FiberTracts-angle.jpg|200px]]
| |

== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

|-
| style="width:15%" | [[Image:DTIFiltering.jpg|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-

| | [[Image:Brain-seg-utah.png|200px]]
| |

== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

|-

| | [[Image:combined_50_seg_labeled.png|200px]]
| |

== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find the kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of the labels of all images in the dataset, which can give an accurate atlas for the target.

 P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

|}

Algorithm:Utah

2012-11-16T20:47:19Z

Manasi: /* Mixed-Effects Shape Models for Longitudinal Analysis */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

|-
{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.

|-
| style="width:15%" | [[Image:EPI.png|200px]]
| style="width:85%" |

== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

 R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.

|-

| | [[Image:pipeline.png|150px]]
| |

== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==

This framework addresses the simultaneous alignment and ﬁltering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

 Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.

|-

| | [[Image:Sulcaldepth.png|200px]]
| |

== [[Projects:CorticalCorrespondenceWithParticleSystem|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. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

 Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
| |

== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

 Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

 Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

|-

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

 M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

| | [[Image:NonRegularSurfCorres.png|200px]]
| |

== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

 M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, Geometric Correspondence for Ensembles of Nonregular Shapes, MICCAI 2011.

|-

| | [[Image:MixedEffectsShape.png|200px]]
| |

== [[Projects:MixedEffectsShape|Mixed-Effects Shape Models for Longitudinal Analysis]] ==

Longitudinal shape changes in anatomy are characterized using a new method that combines point correspondences across shapes with the statistical modeling of individual and population trends via the linear mixed-effects model. This method helps us examine and contrast population trends with individual growth trajectories.

'''New: ''' M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher, Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy, STIA 2012

|-

| | [[Image:CamFAIAnalysis.png|200px]]
| |

== Statistical Shape Analysis of Cam-FAI ==

Cam femoroacetabular impingement (FAI) is characterized by a malformed femoral head that may lead to early hip osteoarthritis. Radiographic measurements are used to diagnose cam FAI and often assume the femur shape to be spherical. Statistical shape modeling (SSM) can be used to compare complex 3D morphology without the need to assume ideal geometry and quantify morphologic differences between control and FAI femurs.

 MD Harris, M Datar, E Jurrus, CL Peters, RT Whitaker, AE Anderson, Statistical Shape Modeling of CAM-type Femoroacetabular Impingement, CMBBE 2012

|-

| | [[Image:MiceMOAnalysis.png|200px]]
| |

== Understanding Short Bone Phenotype in Multiple Osteochondromas ==

Novel statistical methods were developed to study the 'steal phenomenon' caused by multiple osteochondromas in mouse models. Bone lengths and volumes were compared. Metaphyseal volume deviations from normal, as a measure of osteochondroma volumetric growth, were correlated with length deviations.

 KB Jones, M Datar, S Ravichandran, H Jin, E Jurrus, RT Whitaker, MR Capecchi, Toward an Understanding of the Short Bone Phenotype Associated with Multiple Osteochondromas, JOR 2012 (to appear)

|-

| | [[Image:FiberTracts-angle.jpg|200px]]
| |

== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

|-
| style="width:15%" | [[Image:DTIFiltering.jpg|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-

| | [[Image:Brain-seg-utah.png|200px]]
| |

== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

|-

| | [[Image:combined_50_seg_labeled.png|200px]]
| |

== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find the kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of the labels of all images in the dataset, which can give an accurate atlas for the target.

 P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

|}

Algorithm:Utah

2012-11-16T20:43:36Z

Manasi: /* Understanding Short Bone Phenotype in Multiple Osteochondromas */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

|-
{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.

|-
| style="width:15%" | [[Image:EPI.png|200px]]
| style="width:85%" |

== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

 R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.

|-

| | [[Image:pipeline.png|150px]]
| |

== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==

This framework addresses the simultaneous alignment and ﬁltering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

 Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.

|-

| | [[Image:Sulcaldepth.png|200px]]
| |

== [[Projects:CorticalCorrespondenceWithParticleSystem|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. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

 Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
| |

== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

 Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

 Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

|-

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

 M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

| | [[Image:NonRegularSurfCorres.png|200px]]
| |

== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

 M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, Geometric Correspondence for Ensembles of Nonregular Shapes, MICCAI 2011.

|-

| | [[Image:MixedEffectsShape.png|200px]]
| |

== [[Projects:MixedEffectsShape|Mixed-Effects Shape Models for Longitudinal Analysis]] ==

Longitudinal shape changes in anatomy are characterized using a new method that combines point correspondences across shapes with the statistical modeling of individual and population trends via the linear mixed-effects model. This method helps us examine and contrast population trends with individual growth trajectories.

 M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher, Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy, STIA 2012

|-

| | [[Image:CamFAIAnalysis.png|200px]]
| |

== Statistical Shape Analysis of Cam-FAI ==

Cam femoroacetabular impingement (FAI) is characterized by a malformed femoral head that may lead to early hip osteoarthritis. Radiographic measurements are used to diagnose cam FAI and often assume the femur shape to be spherical. Statistical shape modeling (SSM) can be used to compare complex 3D morphology without the need to assume ideal geometry and quantify morphologic differences between control and FAI femurs.

 MD Harris, M Datar, E Jurrus, CL Peters, RT Whitaker, AE Anderson, Statistical Shape Modeling of CAM-type Femoroacetabular Impingement, CMBBE 2012

|-

| | [[Image:MiceMOAnalysis.png|200px]]
| |

== Understanding Short Bone Phenotype in Multiple Osteochondromas ==

Novel statistical methods were developed to study the 'steal phenomenon' caused by multiple osteochondromas in mouse models. Bone lengths and volumes were compared. Metaphyseal volume deviations from normal, as a measure of osteochondroma volumetric growth, were correlated with length deviations.

 KB Jones, M Datar, S Ravichandran, H Jin, E Jurrus, RT Whitaker, MR Capecchi, Toward an Understanding of the Short Bone Phenotype Associated with Multiple Osteochondromas, JOR 2012 (to appear)

|-

| | [[Image:FiberTracts-angle.jpg|200px]]
| |

== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

|-
| style="width:15%" | [[Image:DTIFiltering.jpg|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-

| | [[Image:Brain-seg-utah.png|200px]]
| |

== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

|-

| | [[Image:combined_50_seg_labeled.png|200px]]
| |

== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find the kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of the labels of all images in the dataset, which can give an accurate atlas for the target.

 P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

|}

Projects:ShapeRegression

2012-11-16T20:34:56Z

Manasi: /* Publications */

Back to [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Particle Based Shape Regression =

{|
|[[Image:HeadRegressionResult.png|thumb|512px|Changes in early head shape with log(age).]]
|}

= Description =
Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. In this paper we propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The statistical significance of the dependence is evaluated using permutation tests designed to estimate the likelihood of achieving the observed statistics under numerous rearrangements of the shape parameters with respect to the explanatory variable. We demonstrate the method on synthetic
data and provide a new results on clinical MRI data related to early development of the human head.

= Key Investigators =

* Utah: Manasi Datar, Joshua Cates, P. Thomas Fletcher, Sylvain Gouttard, Guido Gerig, Ross Whitaker

= Publications =

* M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, [http://www.cs.utah.edu/~manasi/pubs/ShapeWorksMICCAI2009.pdf|Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging], MICCAI 2009

[[Category:Shape Analysis]] [[Category:Statistics]]

Projects:NonRegularSurfCorres

2012-11-16T20:34:35Z

Manasi: /* Publications */

Back to [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Geometric Correspondence for Nonregular Surfaces =

{|
|[[Image:NonRegularSurfCorres.png|thumb|450px|Examples of challenges posed by nonregular surfaces.]]
|}

= Description =
An ensemble of biological shapes can be represented and analyzed with a dense set of point correspondences. In previous work, optimal point placement was determined by optimizing an information theoretic criterion that depends on relative spatial locations on different shapes combined with pairwise Euclidean distances between nearby points on the same shape. These choices have prevented such methods from effectively characterizing shapes with complex geometry such as thin or highly curved features. This paper extends previous methods for automatic shape correspondence by taking into account the underlying geometry of individual shapes. This is done by replacing the Euclidean distance for intrashape pairwise particle interactions by the geodesic distance. A novel set of numerical techniques for fast distance computations on curved surfaces is used to extract these distances. In addition, we introduce an entropy term that incorporates surface normal information to achieve better particle correspondences near sharp features. Finally, we demonstrate this new method on synthetic and biological datasets.

= Key Investigators =

* Utah: Manasi Datar, Yaniv Gur, Ross Whitaker
* UNC: Beatriz Paniagua, Martin Styner

= Publications =

* M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, [http://www.cs.utah.edu/~manasi/pubs/ShapeWorksMICCAI2011.pdf|Geometric Correspondence for Ensembles of Nonregular Shapes], MICCAI 2011

[[Category:Shape Analysis]] [[Category:Statistics]]

Projects:NonRegularSurfCorres

2012-11-16T20:33:46Z

Manasi: /* Publications */

Back to [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Geometric Correspondence for Nonregular Surfaces =

{|
|[[Image:NonRegularSurfCorres.png|thumb|450px|Examples of challenges posed by nonregular surfaces.]]
|}

= Description =
An ensemble of biological shapes can be represented and analyzed with a dense set of point correspondences. In previous work, optimal point placement was determined by optimizing an information theoretic criterion that depends on relative spatial locations on different shapes combined with pairwise Euclidean distances between nearby points on the same shape. These choices have prevented such methods from effectively characterizing shapes with complex geometry such as thin or highly curved features. This paper extends previous methods for automatic shape correspondence by taking into account the underlying geometry of individual shapes. This is done by replacing the Euclidean distance for intrashape pairwise particle interactions by the geodesic distance. A novel set of numerical techniques for fast distance computations on curved surfaces is used to extract these distances. In addition, we introduce an entropy term that incorporates surface normal information to achieve better particle correspondences near sharp features. Finally, we demonstrate this new method on synthetic and biological datasets.

= Key Investigators =

* Utah: Manasi Datar, Yaniv Gur, Ross Whitaker
* UNC: Beatriz Paniagua, Martin Styner

= Publications =

* M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, [http://www.cs.utah.edu/~manasi/pubs/ShapeWorksMICCAI2009.pdf | Geometric Correspondence for Ensembles of Nonregular Shapes], MICCAI 2011

[[Category:Shape Analysis]] [[Category:Statistics]]

Projects:ShapeRegression

2012-11-16T20:33:00Z

Manasi: /* Publications */

Back to [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Particle Based Shape Regression =

{|
|[[Image:HeadRegressionResult.png|thumb|512px|Changes in early head shape with log(age).]]
|}

= Description =
Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. In this paper we propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The statistical significance of the dependence is evaluated using permutation tests designed to estimate the likelihood of achieving the observed statistics under numerous rearrangements of the shape parameters with respect to the explanatory variable. We demonstrate the method on synthetic
data and provide a new results on clinical MRI data related to early development of the human head.

= Key Investigators =

* Utah: Manasi Datar, Joshua Cates, P. Thomas Fletcher, Sylvain Gouttard, Guido Gerig, Ross Whitaker

= Publications =

* M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, [http://www.cs.utah.edu/~manasi/pubs/ShapeWorksMICCAI2009.pdf | Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging], MICCAI 2009

[[Category:Shape Analysis]] [[Category:Statistics]]

File:MiceMOAnalysis.png

2012-11-16T20:29:13Z

Manasi:

Algorithm:Utah

2012-11-16T20:28:57Z

Manasi: /* Utah Projects */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

|-
{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.

|-
| style="width:15%" | [[Image:EPI.png|200px]]
| style="width:85%" |

== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

 R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.

|-

| | [[Image:pipeline.png|150px]]
| |

== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==

This framework addresses the simultaneous alignment and ﬁltering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

 Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.

|-

| | [[Image:Sulcaldepth.png|200px]]
| |

== [[Projects:CorticalCorrespondenceWithParticleSystem|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. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

 Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
| |

== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

 Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

 Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

|-

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

 M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

| | [[Image:NonRegularSurfCorres.png|200px]]
| |

== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

 M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, Geometric Correspondence for Ensembles of Nonregular Shapes, MICCAI 2011.

|-

| | [[Image:MixedEffectsShape.png|200px]]
| |

== [[Projects:MixedEffectsShape|Mixed-Effects Shape Models for Longitudinal Analysis]] ==

Longitudinal shape changes in anatomy are characterized using a new method that combines point correspondences across shapes with the statistical modeling of individual and population trends via the linear mixed-effects model. This method helps us examine and contrast population trends with individual growth trajectories.

 M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher, Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy, STIA 2012

|-

| | [[Image:CamFAIAnalysis.png|200px]]
| |

== Statistical Shape Analysis of Cam-FAI ==

Cam femoroacetabular impingement (FAI) is characterized by a malformed femoral head that may lead to early hip osteoarthritis. Radiographic measurements are used to diagnose cam FAI and often assume the femur shape to be spherical. Statistical shape modeling (SSM) can be used to compare complex 3D morphology without the need to assume ideal geometry and quantify morphologic differences between control and FAI femurs.

 MD Harris, M Datar, E Jurrus, CL Peters, RT Whitaker, AE Anderson, Statistical Shape Modeling of CAM-type Femoroacetabular Impingement, CMBBE 2012

|-

| | [[Image:MiceMOAnalysis.png|200px]]
| |

== Understanding Short Bone Phenotype in Multiple Osteochondromas ==

Novel statistical methods were developed to study the 'steal phenomenon' caused by multiple osteochondromas in mouse models. Bone lengths and volumes were compared. Metaphyseal volume deviations from normal, as a measure of osteochondroma volumetric growth, were correlated with length deviations.

 KB Jones, M Datar, S Ravichandran, H Jin, E Jurrus, RT Whitaker, MR Capecchi, Toward an Understanding of the Short Bone Phenotype Associated with Multiple Osteochondromas, JOR 2012 (accepted)

|-

| | [[Image:FiberTracts-angle.jpg|200px]]
| |

== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

|-
| style="width:15%" | [[Image:DTIFiltering.jpg|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-

| | [[Image:Brain-seg-utah.png|200px]]
| |

== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

|-

| | [[Image:combined_50_seg_labeled.png|200px]]
| |

== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find the kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of the labels of all images in the dataset, which can give an accurate atlas for the target.

 P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

|}

File:CamFAIAnalysis.png

2012-11-16T17:51:04Z

Manasi:

Algorithm:Utah

2012-11-16T17:50:50Z

Manasi: /* Utah Projects */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

|-
{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.

|-
| style="width:15%" | [[Image:EPI.png|200px]]
| style="width:85%" |

== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

 R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.

|-

| | [[Image:pipeline.png|150px]]
| |

== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==

This framework addresses the simultaneous alignment and ﬁltering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

 Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.

|-

| | [[Image:Sulcaldepth.png|200px]]
| |

== [[Projects:CorticalCorrespondenceWithParticleSystem|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. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

 Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

|-

| | [[Image:CatesNamicFigure3.png|200px]]
| |

== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

 Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

|-

| | [[Image:UNCShape_OverviewAnalysis_MICCAI06.gif|200px]]
| |

== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

 Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

|-

| | [[Image:HeadRegressionResult.png|200px]]
| |

== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

 M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

|-

| | [[Image:NonRegularSurfCorres.png|200px]]
| |

== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

 M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, Geometric Correspondence for Ensembles of Nonregular Shapes, MICCAI 2011.

|-

| | [[Image:MixedEffectsShape.png|200px]]
| |

== [[Projects:MixedEffectsShape|Mixed-Effects Shape Models for Longitudinal Analysis]] ==

Longitudinal shape changes in anatomy are characterized using a new method that combines point correspondences across shapes with the statistical modeling of individual and population trends via the linear mixed-effects model. This method helps us examine and contrast population trends with individual growth trajectories.

 M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher, Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy, STIA 2012

|-

| | [[Image:CamFAIAnalysis.png|200px]]
| |

== Statistical Shape Analysis of Cam-FAI ==

Cam femoroacetabular impingement (FAI) is characterized by a malformed femoral head that may lead to early hip osteoarthritis. Radiographic measurements are used to diagnose cam FAI and often assume the femur shape to be spherical. Statistical shape modeling (SSM) can be used to compare complex 3D morphology without the need to assume ideal geometry and quantify morphologic differences between control and FAI femurs.

 MD Harris, M Datar, E Jurrus, CL Peters, RT Whitaker, AE Anderson, Statistical Shape Modeling of CAM-type Femoroacetabular Impingement, CMBBE 2012

|-

| | [[Image:FiberTracts-angle.jpg|200px]]
| |

== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

|-
| style="width:15%" | [[Image:DTIFiltering.jpg|200px]]
| style="width:85%" |

== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

|-

| | [[Image:Brain-seg-utah.png|200px]]
| |

== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

|-

| | [[Image:combined_50_seg_labeled.png|200px]]
| |

== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find the kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of the labels of all images in the dataset, which can give an accurate atlas for the target.

 P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

|}

Projects:MixedEffectsShape

2012-11-16T17:38:46Z

Manasi: Created page with ' Back to Utah Algorithms __NOTOC__ = Mixed-Effects Shape Models for Longitudinal Analysis = {| |[[Image:MixedEffectsShape.png|thumb|450px|Visualization of…'

Back to [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Mixed-Effects Shape Models for Longitudinal Analysis =

{|
|[[Image:MixedEffectsShape.png|thumb|450px|Visualization of fixed- and random-effects for brain surfaces.]]
|}

= Description =
We propose a new method for longitudinal shape analysis that fits a linear mixed-effects model, while simultaneously optimizing correspondences on a set of anatomical shapes. Shape changes are modeled in a hierarchical fashion, with the global population trend as a fixed effect and individual trends as random effects. The statistical significance of the estimated trends are evaluated using specifically designed permutation tests. We also develop a permutation test based on the Hotelling T2 statistic to compare the average shapes trends between two populations. We demonstrate the benefits of our method on a synthetic example of longitudinal tori and data from a developmental neuroimaging study.

= Key Investigators =

* Utah: Manasi Datar, Prasanna Muralidharan, Sylvain Gouttard, Guido Gerig, Ross Whitaker and P. Thomas Fletcher

= Publications =

* M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher, Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy, STIA 2012

[[Category:Shape Analysis]] [[Category:Statistics]]

Projects:NonRegularSurfCorres

2012-11-16T17:37:09Z

Manasi: /* Key Investigators */

Back to [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Geometric Correspondence for Nonregular Surfaces =

{|
|[[Image:NonRegularSurfCorres.png|thumb|450px|Examples of challenges posed by nonregular surfaces.]]
|}

= Description =
An ensemble of biological shapes can be represented and analyzed with a dense set of point correspondences. In previous work, optimal point placement was determined by optimizing an information theoretic criterion that depends on relative spatial locations on different shapes combined with pairwise Euclidean distances between nearby points on the same shape. These choices have prevented such methods from effectively characterizing shapes with complex geometry such as thin or highly curved features. This paper extends previous methods for automatic shape correspondence by taking into account the underlying geometry of individual shapes. This is done by replacing the Euclidean distance for intrashape pairwise particle interactions by the geodesic distance. A novel set of numerical techniques for fast distance computations on curved surfaces is used to extract these distances. In addition, we introduce an entropy term that incorporates surface normal information to achieve better particle correspondences near sharp features. Finally, we demonstrate this new method on synthetic and biological datasets.

= Key Investigators =

* Utah: Manasi Datar, Yaniv Gur, Ross Whitaker
* UNC: Beatriz Paniagua, Martin Styner

= Publications =

* M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, Geometric Correspondence for Ensembles of Nonregular Shapes, MICCAI 2011

[[Category:Shape Analysis]] [[Category:Statistics]]

File:MixedEffectsShape.png

2012-11-16T17:22:33Z

Manasi:

Algorithm:Utah

2012-11-16T17:21:29Z

Manasi: /* Utah Projects */

Back to [[Algorithm:Main|NA-MIC Algorithms]]
__NOTOC__
= Overview of Utah Algorithms (PI: Ross Whitaker) =

We are developing new methods in the areas of statistical shape analysis, MRI tissue segmentation, and diffusion tensor image processing and analysis. We are building shape analysis tools that can generate efficient statistical models appropriate for analyzing anatomical shape differences in the brain. We are developing a wide range of tools for diffusion tensor imaging, that span the entire pipeline from image processing to automatic white matter tract extraction to statistical testing of clinical hypotheses.

= Utah Projects =

{| cellpadding="10" style="text-align:left;"

|-
{| cellpadding="10" style="text-align:left;"

| | [[Image:sgerber_brainmanifold_oasis_manifold.png|200px]]
| |

== [[Projects:BrainManifold|Brain Manifold Learning]] ==

This work is concerned with modeling high dimensional spaces, such as the space of brain images. Common approach for representing populations are template or clustering based approaches. In this project we develop a data driven method to learn a manifold representation from a set of brain images. The presented approach is described and evaluated in the setting of brain MRI but generalizes to other application domains.

S Gerber, T Tasdizen, S Joshi, R Whitaker, On the Manifold Structure of the Space of Brain Images, MICCAI 2009.

S Gerber, T Tasdizen, R Whitaker, Dimensionality Reduction and Principal Surfaces via Kernel Map, ICCV 2009.

S. Gerber, T. Tasdizen, P.T. Fletcher, S. Joshi, R. Whitaker, Manifold Modeling for Brain Population Analysis, Medical Image Anal, 3, 2010.

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== [[Projects:EPIDistortionCorrection| Correction for Geometric Distortion in Echo Planar Images]] ==

We have developed a variational image-based approach to correct the susceptibility artifacts in the alignment of diffusion weighted and structural MRI.The correction is formulated as an optimization of a penalty that captures the intensity difference between the jacobian corrected EPI baseline images and a corresponding T2-weighted structural image.

 R Tao, P T Fletcher, S Gerber, R Whitaker, A Variational Image-Based Approach to the Correction of Susceptibility Artifacts
in the Alignment of Diffusion Weighted and Structural MRI, IPMI 2009.

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== [[Projects:StructuralAndDWIPipeline| A Framework for Joint Analysis of Structural and Diffusion MRI]] ==

This framework addresses the simultaneous alignment and ﬁltering of DWI images to correct eddy current artifacts and the subsequent alignment of those images to structural, T1 MRI to correct for susceptibility artifacts, and this paper demonstrates the importance of performing these corrections. It also shows how a T1-based, group speciﬁc atlas can be used to generate grey-matter regions of interest that can drive subsequent connectivity analyses. The result is a system that can be combined with a variety of tools for MRI analysis for tissue classiﬁcation, morphometry, and cortical parcellation.

 Ran Tao, P. Thomas Fletcher, Ross T. Whitaker, in MICCAI 2008 on Computational Diffusion MRI.

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== [[Projects:CorticalCorrespondenceWithParticleSystem|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. [[Projects:CorticalCorrespondenceWithParticleSystem|More...]]

 Oguz I, Niethammer M, Cates J, Whitaker R, Fletcher T, Vachet C, Styner M. “Cortical Correspondence with Probabilistic Fiber Connectivity”. Proc. Information Processing in Medical Imaging, 2009.

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== [[Projects:ParticlesForShapesAndComplexes|Adaptive, Particle-Based Sampling for Shapes and Complexes]] ==

This research is a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. [[Projects:ParticlesForShapesAndComplexes|More...]]

 Particle-Based Shape Analysis of Multi-object Complexes. Cates J., Fletcher P.T., Styner M., Hazlett H.C., Whitaker R. Int Conf Med Image Comput Comput Assist Interv. 2008;11(Pt 1):477-485.

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== [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|Shape Analysis Framework using SPHARM-PDM]] ==

The UNC shape analysis is based on an analysis framework of objects with spherical topology, described mainly by sampled spherical harmonics SPHARM-PDM. The input of the shape analysis framework is a set of binary segmentations of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a shape description (SPHARM) with correspondence and analyzed via Hotelling T^2 two sample metric. [[Projects:ShapeAnalysisFrameworkUsingSPHARMPDM|More...]]

 Zhao Z., Taylor W., Styner M., Steffens D., Krishnan R., Macfall J. , Hippocampus shape analysis and late-life depression. PLoS ONE. 2008 Mar 19;3(3):e1837.

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== [[Projects:ShapeRegression|Particle Based Shape Regression]] ==

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. We propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The method is applied to provide new results on clinical MRI data related to early development of the human head.

 M Datar, J Cates, P T Fletcher, S Gouttard, G Gerig, R Whitaker, Particle Based Shape Regression of Open Surfaces with Applications to Developmental Neuroimaging, MICCAI 2009.

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== [[Projects:NonRegularSurfCorres|Geometric Correspondence for Nonregular Surfaces]] ==

To resolve the challenges posed by highly nonregular surfaces, we have proposed an efficient method which incorporates Geodesic distances and an entropy based on surface normals to improve correspondences.

 M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, Geometric Correspondence for Ensembles of Nonregular Shapes, MICCAI 2011.

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== [[Projects:MixedEffectsShape|Mixed-Effects Shape Models for Longitudinal Analysis]] ==

Longitudinal shape changes in anatomy are characterized using a new method that combines point correspondences across shapes with the statistical modeling of individual and population trends via the linear mixed-effects model. This method helps us examine and contrast population trends with individual growth trajectories.

 M Datar, P Muralidharan, A Kumar, S Gouttard, J Piven, G Gerig, RT Whitaker, PT Fletcher, Mixed-Effects Shape Models for Estimating Longitudinal Changes in Anatomy, STIA 2012

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== [[Projects:DTIVolumetricWhiteMatterConnectivity|DTI Volumetric White Matter Connectivity]] ==

We have developed a PDE-based approach to white matter connectivity from DTI that is founded on the principal of minimal paths through the tensor volume. Our method computes a volumetric representation of a white matter tract given two endpoint regions. We have also developed statistical methods for quantifying the full tensor data along these pathways, which should be useful in clinical studies using DT-MRI. [[Projects:DTIVolumetricWhiteMatterConnectivity|More...]]

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== [[Projects:DTIProcessingTools|DTI Processing and Statistics Tools]] ==

We implement the diffusion weighted image (DWI) registration model from the paper of G.K.Rohde et al. Patient head motion and eddy currents distortion cause artifacts in maps of diffusion parameters computer from DWI. This model corrects these two distortions at the same time including brightness correction.

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== [[Projects:TissueClassificationWithNeighborhoodStatistics| Tissue Classification with Neighborhood Statistics]] ==

We have implemented an MRI tissue classification algorithm based on unsupervised non-parametric density estimation of tissue intensity classes.
[[Projects:TissueClassificationWithNeighborhoodStatistics|More...]]

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== [[Projects:AtlasBasedBrainSegmentation| Atlas-Based Brain Segmentation]] ==

We have implemented an Multi-Atlases based brain MRI tissue segmentation method. This method using a fast,shape-based hierarchical matching approach to find the kNNs for a target brain in a brain dataset with known segmentations/labels, and then fuse the labels of the kNNs instead of the labels of all images in the dataset, which can give an accurate atlas for the target.

 P. Zhu, S.P. Awate, S. Gerber, R. Whitaker. Fast Shape-Based Nearest-Neighbor Search for Brain MRIs Using Hierarchical Feature Matching, MICCAI 2011.

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Projects:NonRegularSurfCorres

2012-11-16T17:16:36Z

Manasi: /* Publications */

Back to [[Algorithm:Utah|Utah Algorithms]]
__NOTOC__
= Geometric Correspondence for Nonregular Surfaces =

{|
|[[Image:NonRegularSurfCorres.png|thumb|450px|Examples of challenges posed by nonregular surfaces.]]
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= Description =
An ensemble of biological shapes can be represented and analyzed with a dense set of point correspondences. In previous work, optimal point placement was determined by optimizing an information theoretic criterion that depends on relative spatial locations on different shapes combined with pairwise Euclidean distances between nearby points on the same shape. These choices have prevented such methods from effectively characterizing shapes with complex geometry such as thin or highly curved features. This paper extends previous methods for automatic shape correspondence by taking into account the underlying geometry of individual shapes. This is done by replacing the Euclidean distance for intrashape pairwise particle interactions by the geodesic distance. A novel set of numerical techniques for fast distance computations on curved surfaces is used to extract these distances. In addition, we introduce an entropy term that incorporates surface normal information to achieve better particle correspondences near sharp features. Finally, we demonstrate this new method on synthetic and biological datasets.

= Key Investigators =

* Utah: Manasi Datar, Yaniv Gur, Beatriz Paniagua, Martin Styner, Ross Whitaker

= Publications =

* M Datar, Y Gur, B Paniagua, M Styner, R Whitaker, Geometric Correspondence for Ensembles of Nonregular Shapes, MICCAI 2011

[[Category:Shape Analysis]] [[Category:Statistics]]