Keywords: AI/ML Image Reconstruction, Diffusion/other diffusion imaging techniques, implicit neural representation, q-space undersampling, spherical harmonics
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.
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