A Spherical Convolutional Neural Network for White Matter Structure Imaging via dMRI

Diffusion Magnetic Resonance Imaging (dMRI) is a powerful non-invasive and in-vivo imaging modality for probing brain white matter structure. Convolutional neural networks (CNNs) have been shown to be tool many computer vision problems where the signals are acquired on regular grid translational invariance important. However, as we considering dMRI that sphere, rotational invariance, rather than translational, desired. In this work, propose spherical CNN model with fully spectral domain convolutional non-linear layers. It provides adapted real nature of uniform random distribution sampling points. The proposed positively evaluated problem estimation neurite orientation dispersion density (NODDI) parameters data from Human Connectome Project (HCP).