A generative model for resolution enhancement of diffusion MRI data

The advent of diffusion magnetic resonance imaging (DMRI) presents unique opportunities for the exploration of white matter connectivity in vivo and non-invasively. However, DMRI suffers from insufficient spatial resolution, often limiting its utility to the studying of only major white matter structures. Many image enhancement techniques rely on expensive scanner upgrades and complex time-consuming sequences. We will instead take a post-processing approach in this paper for resolution enhancement of DMRI data. This will allow the enhancement of existing data without re-acquisition. Our method uses a generative model that reflects the image generation process and, after the parameters of the model have been estimated, we can effectively sample high-resolution images from this model. More specifically, we assume that the diffusion-weighted signal at each voxel is an agglomeration of signals from an ensemble of fiber segments that can be oriented and located freely within the voxel. Our model for each voxel therefore consists of an arbitrary number of signal generating fiber segments, and the model parameters that need to be determined are the locations and orientations of these fiber segments. Solving for these parameters is an ill-posed problem. However, by borrowing information from neighboring voxels, we show that this can be solved by using Markov chain Monte Carlo (MCMC) methods such as the Metropolis-Hastings algorithm. Preliminary results indicate that out method substantially increases structural visibility in both subcortical and cortical regions.