Bayesian Tractography Using Geometric Shape Priors

The problem of estimating neuronal fiber tracts connecting different brain regions is important for various types of brain studies, including understanding brain functionality and diagnosing cognitive impairments. The popular techniques for tractography are mostly sequential-tracts are grown sequentially following principal directions of local water diffusion profiles. Despite several advancements on this basic idea, the solutions easily get stuck in local solutions, and can't incorporate global shape information. We present a global approach where fiber tracts between regions of interest are initialized and updated via deformations based on gradients of a posterior energy. This energy has contributions from diffusion data, global shape models, and roughness penalty. The resulting tracts are relatively immune to issues such as tensor noise and fiber crossings, and achieve more interpretable tractography results. We demonstrate this framework using both simulated and real dMRI and HARDI data.