Direct Estimation of Fiber Orientations Using Deep Learning in Diffusion Imaging

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. We evaluate 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.