AUTHOR=Dong Xiaoming , Zhang Zhengwu , Srivastava Anuj 

TITLE=Bayesian Tractography Using Geometric Shape Priors

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 11 - 2017

YEAR=2017

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2017.00483

DOI=10.3389/fnins.2017.00483

ISSN=1662-453X

ABSTRACT=The problem of estimating neuronal fiber tracts connecting different brain regions is important for 
various types of brain studies, such as understanding brain functionality and diagnosing cognitive impairments. The popular techniques for tractography using dMRI data are sequential approaches -- tracts are grown sequentially 
following principal directions of local water diffusion profiles. Despite several advancements of this basic idea, the solutions easily lead to 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 data.