A Coherence Enhancing Penalty for Diffusion MRI: Regularizing Property and Discrete Approximation

Processing of diffusion MRI data obtained from high angular resolution measurements consists of a series of steps, starting with the estimation of an orientation distribution function (ODF), which is then used as input for, e.g., tractography algorithms. It is important that ODF reconstruction methods yield accurate, coherent ODFs, particularly for low signal-to-noise ratio or coarsely sampled data sets. As the diffusion process is modeled independently in each voxel, reconstructions are often carried out for each voxel separately, disregarding the observation that neighboring voxels can be quite similar if they belong to the same fiber structure. There are surprisingly few approaches that make use of this kind of spatial regularity to improve coherence and stability of the reconstruction. In this work, we focus on a variation of a method proposed by Reisert and Kiselev based on the concept of fiber continuity. The method has already been shown to yield good numerical results but has not yet been analyzed theoretically. Under suitable smoothness assumptions, we apply results on constrained Tikhonov-type regularization with an approximate operator to show convergence of reconstructions from discrete noisy data for linear forward models. Further, we numerically illustrate the performance of the method on phantom and in vivo data.