Reliable Estimation of the Number of Compartments in Diffusion MRI

Abstract

A-priori knowledge of the number of fibers in a voxel is mandatory and crucial when reconstructing multi-fiber voxels in diffusion MRI. Especially for clinical purposes, this estimation needs to be stable, even when only few gradient directions are acquired. In this work, we propose a novel approach to address this problem based on a deep convolutional neural network (CNN), which is able to identify important gradient directions and can be directly trained on real data. To obtain a ground truth using real data, 100 uncorrelated Human Connectome Project datasets are utilized, with a state-of-the-art framework used for generating a relative ground truth. It is shown that this CNN approach outperforms other state-of-the-art machine learning approaches.