Abstract #0859

Fully Automated Probabilistic White-Matter Tractography with Anatomical Priors: Application to Huntington's Disease

Anastasia Yendiki¹, Allison Stevens¹, Jean Augustinack¹, David Salat¹, Lilla Zollei¹, Ruopeng Wang¹, Diana Rosas², Bruce Fischl^1,3

¹HMS/MGH/MIT Martinos Center for Biomedical Imaging, Charlestown, MA, USA; ²Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; ³Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA

We illustrate the application of a robust and fully automated method for probabilistic white-matter tractography to analyze diffusion-weighted MR images from a large cohort of Huntington's disease patients and matched healthy controls. The method uses manually labeled paths from a set of training subjects to construct priors on these paths. The priors are then incorporated into a probabilistic tractography framework to trace the paths automatically in the test subjects (Huntington's disease patients and controls). Preliminary results show significant decreases of the fractional anisotropy in several parts of the corticospinal tract and superior longitudinal fasciculus of the patient population.