Keywords: Image Reconstruction, Diffusion/other diffusion imaging techniques, q-space, denoisingDISCUS addresses two challenges currently limiting the analysis potential of diffusion MRI: sparsity of measurements and variability in q-space sampling schemes. Our method combines the advantages of model-fit approaches with continuous sampling (spherical harmonics, SHORE) and rigid, discrete learning-based methods. DISCUS can be initialized from any acquisition scheme and permits signal prediction for an arbitrary q-vector. Despite the added flexibility, DISCUS performs on par with other, far less flexible learning methods, while outperforming model-fit methods. DISCUS-derived signals translate to higher-quality FA estimates promising accurate analyses even from very short acquisitions.
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