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.
['Anastasia Yendiki', 'Allison Stevens', 'Jean C. Augustinack', 'David H. Salat', 'Lilla Zöllei', 'Bruce Fischl']
Anatomical priors for global probabilistic diffusion tractography