Ashish Raj1, Christopher Hess2, Pratik Mukherjee2
1Radiology, Weill Medical College of Cornell University, New York, NY, USA; 2Radiology, UCSF, San Francisco, CA, USA
MR Diffusion Imaging is an important noninvasive method for probing the white matter connectivity of the human brain. Current methods such as diffusion tensor imaging (DTI), high angular resolution diffusion imaging (HARDI), and diffusion spectrum imaging (DSI) are limited by low spatial resolution, long scan times, and low signal-to-noise ratio (SNR). These methods perform reconstruction on a voxel-by-voxel level, effectively discarding the natural coherence of the data at different points in space. We propose a Bayesian reconstruction to exploit a priori constraints about the smoothly varying orientation structure of white matter tracts over 3D space, and thereby improve their spatial resolution and noise tolerance.