2010 Winter Project Week WM ATLAS
Automatically segmented fiber tracts.
Example whole-brain tract cluster mean fibers.
Key Investigators
- Lauren O'Donnell (Instructor, Harvard Medical School (BWH))
- Carl-Fredrik Westin (Professor, Harvard Medical School (BWH) )
- William Wells (Professor, Harvard Medical School (BWH) )
- Alexandra J Golby (Professor of Neurosurgery, Harvard Medical School (BWH) )
Objective
Implement tract-based morphometry pipeline in 3D Slicer, including atlas-based WM (white matter) segmentation and whole-brain WM coordinate system generation. Investigate integration of FMRI+DTI into atlas pipeline, for neurosurgical planning applications.
This work is based on the papers Defining Spatial Relationships Between fMRI and DTI Fiber Tracts Lauren J. O'Donnell, Laura E. Rigolo, Isaiah Norton, Carl-Fredrik Westin, and Alexandra J. Golby MICCAI Workshop on Diffusion Modeling and the Fibre Cup, 2009 Tract-Based Morphometry for White Matter Group Analysis. Lauren J. O'Donnell, Carl-Fredrik Westin, and Alexandra J. Golby NeuroImage 45(3):832-844, 2009 and Automatic Tractography Segmentation Using a High-Dimensional White Matter Atlas Lauren O'Donnell and Carl-Fredrik Westin IEEE Transactions in Medical Imaging 26(11):1562-1575, 2007
Approach, Plan
The original pipeline consists of (over a group):
- Preprocessing
- DTI and FA image calculation (slicer)
- congealing registration of FA images (Lilla's code)
- whole-brain tract seeding (slicer2)
- application of registration to tractography (matlab)
- measurement of fiber-fMRI model (slicer2/matlab)
- EITHER atlas-based segmentation of tractography
- OR atlas generation
- clustering of (sample from) whole-brain tractography in group
- expert labeling of clusters to give anatomical structures
- the above produces an atlas that can be used to segment
- analysis of segmented WM
- optimal generation of tract-based coordinate systems per anatomical structure
- export of cluster/structure based measurements (FA, etc)
- some statistical analysis
- Clustering
- investigating options in python...
The eventual goal is re-implementation of much of the above in the Slicer3 framework. This will enable use of the pipeline for neuroscience research and our future investigation of DTI+fMRI atlases for neurosurgery.
Progress
- Preprocessing
- initial implementation of BatchMake modules for tensor calc, but found ipython is preferable for overall pipeline
- tested congealing implementation in ITK from NAMIC SandBox to replace our current use of original code by Lilla Zollei
- implementation of initial seeding pipeline script in ipython
- current pipeline uses slicer command line modules that call teem/vtk
- Gordon has implemented some probing capability in teem, may allow more direct teem/itk/vtk dependencies
- discussions with Sandy, Lilla indicate ITK congealing broke with new ITK version, unclear when this was
- Clustering
- looking into available packages in python
