Anatomical priors for global probabilistic diffusion tractography

We investigate the use of anatomical priors in a Bayesian framework for diffusion tractography. We compare priors that utilize different types of information on the white-matter pathways to be reconstructed. This information includes manually labeled paths from a set of training subjects and anatomical segmentation labels obtained from T1-weighted MR images of the same subjects. Our results indicate that the use of prior information increases robustness to end-point ROI size and yields solutions that agree with expert-drawn manual labels, obviating the need for manual intervention on any new test subjects.National Institute of Biomedical Imaging and Bioengineering (U.S.) (K99/R00 Pathway to Independence Award EB008129)National Institute of Biomedical Imaging and Bioengineering (U.S.) (Grant R01-EB001550)National Institute of Biomedical Imaging and Bioengineering (U.S.) (Grant R01-EB006758)National Center for Research Resources (U.S.) (Grant P41-RR14075)National Center for Research Resources (U.S.) (Grant R01-RR16594)National Center for Research Resources (U.S.) (NCRR BIRN Morphometric Project BIRN002 Grant U24-RR0213820)National Institute of Neurological Disorders and Stroke (U.S.) (Grant R01-NS052585)Mind Research InstituteNational Alliance for Medical Image Computing (U.S.) the MIND Institute, and the National Alliance for Medical Image Computing (NIH Roadmap for Medical Research Grant U54-EB005149