Direct estimation of fiber orientations using deep learning in diffusion imaging

Abstract

An effective technique for investigating human brain connectivities, is the reconstruction of fiber orientation distribution functions based on diffusion-weighted MRI. To reconstruct fiber orientations, most current approaches fit a simplified diffusion model, resulting in an approximation error. We present a novel approach for estimating the fiber orientation directly from raw data, by converting the model fitting process into a classification problem based on a convolutional Deep Neural Network, which is able to identify correlated diffusion information within a single voxel. Wevaluate our approach quantitatively on realistic synthetic data as well as on real data and achieve reliable results compared to a state-of-the-art method. This approach is even capable to relieable reconstruct three fiber crossing utilizing only 10 gradient acquisitions.