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Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in thenoninvasive investigation of tissue microstructural properties and structuralconnectivity in the \textit{in vivo} human brain. However, to effectivelycapture the intricate characteristics of water diffusion at various directionsand scales, it is important to employ comprehensive q-space sampling.Unfortunately, this requirement leads to long scan times, limiting the clinicalapplicability of dMRI. To address this challenge, we propose SSOR, aSimultaneous q-Space sampling Optimization and Reconstruction framework. Wejointly optimize a subset of q-space samples using a continuous representationof spherical harmonic functions and a reconstruction network. Additionally, weintegrate the unique properties of diffusion magnetic resonance imaging (dMRI)in both the q-space and image domains by applying $l1$-norm and total-variationregularization. The experiments conducted on HCP data demonstrate that SSOR haspromising strengths both quantitatively and qualitatively and exhibitsrobustness to noise.

Simultaneous q-Space Sampling Optimization and Reconstruction for Fast and High-fidelity Diffusion Magnetic Resonance Imaging