Better fiber ODFs from suboptimal data with autoencoder based regularization

Abstract

We propose a novel way of estimating fiber orientation distribution functions (fODFs) from diffusion MRI. Our method combines convex optimization with unsupervised learning in a way that preserves the relative benefits of both. In particular, we regularize constrained spherical deconvolution (CSD) with a prior that is derived from an fODF autoencoder, effectively encouraging solutions that are similar to fODFs observed in high-quality training data. Our method improves results on independent test data, especially when only few measurements or relatively weak diffusion weighting (low b values) are available.