Super-resolution across RF-encoding and q-space dimensions via physics-driven neural fields for accelerated gSlider diffusion MRI

Motivation: gSlider, a state-of-the-art diffusion MRI technique, requires extensive sampling of thick-slice RF-encoding and q-space points to resolve fiber structures at the submillimeter level. This results in impractically long scan times. Goal(s): Our goal is to super-resolve gSlider data in RF-encoding and q-space dimensions to enhance scan efficiency while maintaining high-fidelity diffusion metrics. Approach: We introduce a novel self-supervised model, sq-QUCCI, that cascades two neural field modules with physics-driven regularization to super-resolve undersampled gSlider acquisitions. Results: Compared to state-of-the-art baselines, sq-QUCCI achieves superior fidelity in diffusion metrics at 500μm resolution while enabling 7-fold reduction in scan time for gSlider acquisitions. Impact: sq-QUCCI enables collection of whole-brain high-spatial/angular-resolution, high-SNR diffusion MRI data in a 15-min scan by super-resolving across RF-encoding and q-space dimensions of undersampled gSlider acquisitions, overcoming the efficiency barrier for adoption in clinical settings.