A coherence enhancing penalty for Diffusion MRI: regularizing property and discrete approximation
This work provides a theoretical foundation for a known heuristic method, benefiting the Diffusion MRI community by ensuring reliability of spatial regularization in ODF reconstruction.
The authors analyze a coherence-enhancing penalty for Diffusion MRI that uses spatial regularity to improve ODF reconstruction, proving convergence for linear forward models and demonstrating improved coherence on phantom and in-vivo data.
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, in particular for low SNR or coarsely sampled data sets. As the diffusion process is modelled independently in each voxel, reconstructions are often carried out for each voxel separately, disregarding the observation that neighboring voxels are often 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 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.