DBP2:Harvard:Brain Segmentation Roadmap

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Stochastic Tractography for VCFS

Roadmap

The main goal of this application is to characterize anatomical connectivity abnormalities in the 
brain of patients with velocardiofacial syndrome (VCFS), and to link this information with deficits in schizophrenia. 

This page describes the technology roadmap for stochastic tractography, using newly acquired 3T data, NAMIC tools and slicer 3.

A - Optimization of stochastic tractography algorythm 

• We have been using stochastic tractography for our 1.5 Tesla data, where we traced and analyzed anterior limb of the internal capsule. This algorythm needs to be optimized for 3T data, adjusting for higher data resolution, higher number of diffusion directions and geometric distortions. (Tri, Doug)
• Since the algorythm has been tested on the group data only with respect to large bundle- internal capsule, it needs to be evaluated when applied to multiple anatomical structures that have smaller sizes, and larger curvature. (Tri, Marek, Doug)
  • These tests should be completed in relatively short period of time. We will try to optimize the algorythm so that it is ready to run on test dataset, and have results by the Santa Fe tractography meeting (October 2007).
  • We need good way of segmenting white matter on DTI scans, since stochastic tractography performance depends heavily on good white matter mask. We will look into automatic segmentation provided by slicer2 DTI module, as well as EM segmentation of T2 baseline in cliser 2 (Tri, Brad, Polina, Sylvain).

B – Slicer 3 Stochastic Tractography module and testing plus documentation 

• Since this technology already exists in Slicer 2, building the slicer 3 module should be relatively low risk project. We plan to have at least a basic version prepared for the January AHM. In order to ensure proper function of the module, we will test it on the phantom dataset (Tri, Brad). During the January programming week, we plan to finalize slicer 3 module, and work on the softwware documentation.

C - Analysis of small anatomical structures 

• After the protocol for stochastic tractography is optimized for 3T data, small anatomical structures, such as Uncinate Fasciculus, Arcuate Fasciculus, Fornix will be traced in both schizophrenia (first) and VCFS (later) (Doug, Marek).
  • Arcuate Fasciuclus is especially interesting for VCFS population, and this is the first tract that will be evaluated using this module and new 3T data.

D - Subject comparison 

• Since the DTI scanning protocol was established and tested on schizophrenia subjects, and thus data collection is much more advanced there, and since the ultimate goal is to compare anatomical connectivity abnormalities in VCFS with these in schizophrenia, this project has two benefits- it gives schizophrenia comparison data, as well as leads to establishing the tractography protocol that will be easily applicable to VCFS, once more data is collected.
• We are looking into ways to compare stochastic tractography results between groups. Tract based and volume based measures are considered (both included as parts of the NA-MIC Kit)
  • In addition, Ipek is developing local analysis tools and may have a tool available in Fall 2008.

Updates/Progress

A - Optimization of stochastic tractography algorithm 

• Stochastic Tractography algorythm has been used to analyze anterior limb of the internal capsule using 1.5T data, data was presented at the 2007 ACNP symposium, and we are now working on the manuscript.
• We have optimized our algorithm to work with high resolution diffusion data acquired on 3T magnet, and tested it on multiple white matter fiber tracts (cingulum bundle, arcuate fasciculus, fornix). We plan to submit methods paper to AJNR in April 2008.

B – Slicer 3 Stochastic Tractography module and testing plus documentation 

• Stochastic Tractography module has been finished, and presented at the AHM in SLC. Its now part of the slicer3.
• Module documentation can be found here:
  • Webcast (theora format, www.theora.org)

C - Analysis of small anatomical structures 

• We have started the project of investigating Arcuate Fasciculus using Stochastic Tractography. The pipeline for the analysis includes
  • Whole brain segmentation, and automatic extraction of regions interconnected by Arcuate Fasciculus (Inferior frontal and Supperior Temporal Gyri), as well as another ROI that would guide the tract ("waypoint" ROI). This step has been accomplished for the entire dataset of 20 schizophrenia subjects and 20 controls.
  • White matter segmentation, in order to prevent algorithm from traveling through the ventricles, where diffusivity is high. This has been done for a subset of subjects (19 in total).
  • Applying stochastic tractography algorithm, to find the path connecting two ROIs. This has been also done for the subset of subjects (19 in total).
  • Extracting path of interest, and calculating FA along the path for group comparison. Presentation with group results for 7 schizophrenics and 12 control subjects, can be found here: Progress Report Presentation

Staffing Plan

• Katharina, Sylvain, Tri and Doug are the DBP resources charged with adapting the tools in the NA-MIC Kit to the DBP needs
• Polina is the algorithm core contact
• Brad is the engineering core contact

Schedule

• 10/2007 - Optimization of Stochastic Tractography algorythm for 3T data.
• 10/2007 - Algorythm testing on Santa Fe data set and diffusion phantom.
• 01/2008-AHM - Prototype Stochastic Tractography module in Slicer 3.
• 01/2008-AHM - Working on the ways of extracting and measuring diffusion properties within the Arcuate Fasciculus using Slicer 3 module.
• 03/2008 - Start of the module application to group data.
• 07/2008 - BatchMake workflow.
• 10/2008 - Data analysis and paper write up.
• 01/2009-AHM - Groupwise tract based and volume based analysis of the multiple white matter tracts as a NA-MIC Workflow.

Team and Institute

• PI: Marek Kubicki (kubicki at bwh.harvard.edu)
• DBP2 Investigators: Sylvain Bouix, Tri Ngo
• NA-MIC Engineering Contact: Brad Davis, Kitware
• NA-MIC Algorithms Contact: Polina Gollard, MIT

Publications

In print

• NA-MIC Publications Database