Unsupervised q-Space Interpolation Using Physics-Constrained Coordinate-Based Implicit Network

Motivation: Most diffusion MRI techniques require extensive sampling of q-space to effectively resolve fiber structures at a fine detail. The scan times become impractically long, especially for clinical settings. Goal(s): Our goal is to arbitrarily interpolate the q-space data to enable downsampling of q-space, while maintaining high fidelity diffusion metrics. Approach: We propose QUCCI, a subject-specific unsupervised implicit network model that utilizes both implicit and physics-driven explicit regularization to encode diffusion MRI signals with angular continuity. Results: QUCCI achieves superior q-space interpolation, outperforming traditional and deep learning methods. Impact: QUCCI provides high-fidelity diffusion MRI metrics via improving the angular interpolation of diffusion MRI signals under highly undersampled q-space cases, which may especially be beneficial in the clinical settings where excessively long scan times are impractical.