Accurate white-matter tractography maps can be a useful clinical tool for assessing neurological disorders, however, incorrect assumptions within tractography algorithms can yield non-physiological results. We have developed a tractography methodology based on probability theory that uses both local and global information to improve accuracy, and standard partial differential equation solvers for fast whole-brain mapping. In this abstract we demonstrate: 1) the accuracy of the method by comparing a topographical map of the corpus callosum (CC) generated from a symmetrized human data phantom to published maps; 2) how differences in CC topography may be associated with stroke location and functional disability.
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