Spatial-Angular Representation Learning for High-Fidelity Super-Resolution in Diffusion MRI

Motivation: Due to scanning limitations and system noise, the spatial and angular resolution of diffusion magnetic resonance imaging (dMRI) data is typically low, which limits the accuracy of quantitative parameter estimation in regions with fine anatomical details. Goal(s): Our goal is to enhance both the spatial and angular resolution of dMRI data simultaneously to improve the accuracy of quantitative parameter estimation. Approach: Using implicit neural representations and spherical harmonics to simultaneously enhance both spatial and angular resolution, delivering precise diffusion direction information at a high spatial resolution. Results: Experimental results on the publicly available HCP dataset validate the effectiveness of our method. Impact: Simultaneously enhancing the spatial and angular resolution of dMRI data can effectively reduce acquisition time, improve the accuracy of quantitative parameter estimation, and enhance clinical diagnostic efficiency.