  Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the
noninvasive investigation of tissue microstructural properties and structural
connectivity in the \textit{in vivo} human brain. However, to effectively
capture the intricate characteristics of water diffusion at various directions
and scales, it is important to employ comprehensive q-space sampling.
Unfortunately, this requirement leads to long scan times, limiting the clinical
applicability of dMRI. To address this challenge, we propose SSOR, a
Simultaneous q-Space sampling Optimization and Reconstruction framework. We
jointly optimize a subset of q-space samples using a continuous representation
of spherical harmonic functions and a reconstruction network. Additionally, we
integrate the unique properties of diffusion magnetic resonance imaging (dMRI)
in both the q-space and image domains by applying $l1$-norm and total-variation
regularization. The experiments conducted on HCP data demonstrate that SSOR has
promising strengths both quantitatively and qualitatively and exhibits
robustness to noise.
