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.

翻译：扩散磁共振成像在无创研究活体人脑组织微结构特性和结构连接中发挥着关键作用。然而，为有效捕捉不同方向和尺度下水扩散的复杂特征，必须采用全面的q空间采样。遗憾的是，这一需求导致扫描时间过长，限制了dMRI的临床适用性。针对这一挑战，我们提出SSOR（同步q空间采样优化与重建框架）。我们利用球谐函数的连续表示与重建网络联合优化q空间采样子集，并通过施加l1范数和总变分正则化，将扩散磁共振成像在q空间和图像域中的独特特性相结合。在HCP数据集上的实验表明，SSOR在定量和定性方面均展现出显著优势，且对噪声具有鲁棒性。