Progress Report:Main
Contents
Annual Progress Report
Ongoing Progress Report
- October 2005: Spatial Regularization for fMRI Detection
- October 2005: DTI White Matter Clustering in Slicer/ITK
- October 2005: Shape Based Segmentation and Registration of Brain MR Images
- September 2005: DTI Path of Interest Analysis Port and Application to Core 3 Data
- September 2005: GT DLPFC Slicer Module Development and Application to Core 3 Data
- September 2005: GT Bayesian Classification Development and Application to Core 3 Data
- September 2005: UNC Shape Analysis Development and Application to Core 3 Data
- July 2005: Diffusion Tensor Statistics
- April 2005: How Core 1 is Using Core 3 Data
- Last Updated March 2005: Ongoing Statistics for Data Upload and Access on BIRN Servers
- Feb 2005:Completed visualization of QBALL images in Slicer (MGH, BWH)
Milestone Slides submitted to NIH
- October 7, 2005 Milestones. This is in response to NIH request dated October 5, 2005 for two milestones.
- March 2005 Accomplishments
February 2006 Report to NIH
Progress in the Clinical Application of Diffusion Tensor Imaging
MIT computer scientists in collaboration with Harvard neuroscientists, have produced a robust method for identifying anatomically distinct fiber tracts in the human brian using clustering techniques applied to magnetic resonance diffusion tensor imaging (DTI). DTI is a relatively new technique that makes it possible to visualize and to quantify the organization and integrity of white matter fiber tracts in the brain, in vivo. As part of the U54 NCBC collaboration, the focus is on identifying fiber tracts that may be abnormal in schizophrenia, although such work can be applied to study both global and specific diffusion changes in white matter in various neurological and psychiatric disorders including Alzheimer's disease and multiple sclerosis. Of note here, MIT computer scientists have worked closely with schizophrenia researchers to develop and apply sophisticated computer vision algorithms in order to extract fiber bundles likely important in the pathophysiology of schizophrenia, including the fornix, uncinate fasciculus (the largest fiber tract connecting the frontal and temporal lobe), and the corpus callosum (largest white matter fiber tract in the brain and likely important in communication between the two hemispheres). The method developed can also be applied to surgical planning, to clinical psychiatry, as well as to neurological disorders and for identifying fiber bundles in the brain.
The paper, "A Method for Clustering White Matter Fiber Tracts", will appear in the May issue of the American Journal of Neuroradiology. This work was funded by the National Alliance for Medical Image Computing (NA-MIC), one of the NIH Roadmap Grants for Medical Research. Illustrating the cross-disciplinary nature of NA-MIC, an MIT computer scientist is the first author on this paper with collaborators from Harvard and the Veterans Administration. NA-MIC brings together computer scientists and software engineers to create industry quality software tools to solve driving biological problems.
Lauren O'Donnell(1,2), Marck Kubicki(3,4), Martha Shenton(3,4), Mark Druesicke(3), W. Eric Grimson(1), Carl_Fredrik Westin(5), "A Method for Clustering White Matter Fiber Tracts," American Journal of Neuroradiology,vol 25, issue 5, May 2006.
1. MIT Computer Science and AI Lab (CSAIL) 2. Harvard-MIT Division of Heath Science and Technology 3. Psychiatry Neuroimaing Lab, Dept of Psychiatry, Brigham and Womoen's Hospital 4. Clinical Neuroscince Division, Laboratory of Neuroscience, VA Boston 5. Laboratory of Mathematics in Imaging, Dept of Radiology, Brigham and Women's Hospital.
A Method for Clustering White Matter Fiber Tracts (in press)
