A Bayesian Random Effects Model for Enhancing Resolution in Diffusion MRI

Martin David King¹, Daniel C. Alexander², David G. Gadian¹, Chris A. Clark¹

Poor spatial resolution is a limitation in various diffusion MRI applications, including tractography. A Bayesian latent variables random effects model has been developed for increasing effective spatial resolution, based on a Markov random field treatment in which intrinsic Gaussian autoregressive priors are assigned to the fibre spherical coordinates. The model is used to separate crossing-fibres at the junction between the cingulum and corpus callosum, using diffusion MRI data acquired with a moderate b-value and 20 directions. The analyses were performed using Markov chain Monte Carlo simulation. Results demonstrate that a satisfactory separation of the crossing components can be obtained.