DBP2:UNC:Cortical Thickness Roadmap

Objective

We would like to create an end-to-end application within Slicer3 allowing individual and group analysis of regional and local cortical thickness. Such a workflow applied to the young brain (2-4 years old) is one goal of the UNC DBP. This page describes the technology roadmap for cortical thickness analysis in the NA-MIC Kit.

Roadmap

Starting with several MRI images (weighted-T1, weighted-T2...) we want to obtain cortical thickness maps for each subject and compute cortical correspondences within subjects. Ultimately, the NA-MIC Kit will provide a workflow for individual and group analysis of cortical thickness. It will be implemented as a set of Slicer3 modules that can be used interactively within the Slicer3 application as well as in batch on a computing cluster using BatchMake. The main modules will include:

A - White matter/gray matter segmentation 

• UNC has a segmentation technique implemented in an ITK framework for segmenting white matter and gray matter in the young brain. This technique will be converted into a Slicer3 command line module
  • Since this segmentation technique exists in an ITK framework, the integration into Slicer3 is low risk and should be completed over the next couple of months (mid fall)
• UNC will also investigate adapting the Slicer3 EM Segment module to their young brain studies. Here, UNC will adapt the UNC atlas of the 2 year old brain to provide priors for the EM Segment module
  • This will be a good test case for applying the Slicer3 EM Segment module to a slightly different application. UNC should work through the training material on the Slicer3 EM Segment module and then refer to Brad Davis and Kilian Pohl as needed.
  • This also should be completed before the AHM.

B - Local cortical thickness measurement 

• UNC has an algorithm to measure local cortical thickness given a labeling of white matter and gray matter. This technique will be converted into a Slicer3 command line module
  • This technique is non-symmetric and sparse (only computing distances where they can be computed reliably).
  • It is expected that this module should be available before the AHM.
• Marc Niethammer was developing a technique at a previous project week that would be symmetric. This could be an alternative used as a comparison.

C - Local correspondence 

• Regional as well as local subject comparisons are needed
• Regional analysis will require precise deformable registration to a young brain atlas
  • NA-MIC Kit tools can be applied here
• Local analysis requires techniques which are not currently in the NA-MIC Kit
  • Freesurfer could be used for the local analysis (but it is not in the NA-MIC Kit)
  • Ipek is developing local analysis tools and may have a tool available in Fall 2008.

D - Hypothesis testing

E - Performance characterization and validation 

• Characterize response based on signal noise, patient motion, etc.
• Comparison to other tools (FreeSurfer)

To do

• Assign owners to tasks
• Define schedule

Staffing Plan

• Clement is the DBP resource charged with adapting the tools in the NA-MIC Kit to the DBP needs
• Martin is the algorithm core contact
• Jim is the engineering core contact

Schedule

• xx/xx/2007 - White matter/gray matter segmentation of the young brain using UNC technique as a Slicer3 module
• xx/xx/2007 - White matter/gray matter segmentation of the young brain using the Slicer3 EM Segment module
• xx/xx/2007 - Cortical thickness measurement using UNC technique as a Slicer3 module
• xx/xx/2007 - Cortical thickness measurement using Marc Niethammer's approach as a Slicer3 module
• xx/xx/2007 - Deformable registration of young brain regional atlas
• xx/xx/2007 - Regional analysis of cortical thickness as a Slicer3 module
• xx/xx/2007 - BatchMake workflow
• xx/xx/2007 - Groupwise regional analysis of cortical thickness as a NA-MIC Workflow
• xx/xx/200x - Groupwise local analysis of cortical thickness as a NA-MIC Workflow

Team and Institute

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