在扩散磁共振成像中，传统多壳约束球面反卷积方法通常将白质微结构建模为各向异性，而灰质微结构建模为各向同性．然而，组织学和高分辨率扩散磁共振成像的研究表明，灰质中水分子的扩散过程具有明显的各向异性特征，因此传统约束球面反卷积方法必不能准确地描述灰质微结构．针对这一问题，本文提出了一种基于体细胞和神经突密度成像（Soma And Neurite Density Imaging，SANDI）模型的灰质多壳约束球面反卷积方法，旨在更准确地揭示灰质微结构特性．利用神经元数据对胞体部分信号进行仿真，模拟灰质特有的信号模式，使用SANDI模型生成了灰质的信号以及相应的响应函数，并将其应用于真实大脑数据的灰质进行微结构重建．结果表明，该方法在提取灰质区域各向异性特征方面表现出色，提高了纤维方向估计的准确度．

In diffusion magnetic resonance imaging (dMRI), conventional multi-shell constrained spherical deconvolution (CSD) methods usually model white matter microstructure as anisotropic and gray matter microstructure as isotropic. Nonetheless, investigations using high-resolution dMRI and histology have demonstrated that the diffusion process in gray matter exhibits clear anisotropic features. To better disclose the microstructural aspects of gray matter, this research suggests a gray matter multi-shell restricted spherical deconvolution method based on the SANDI (Soma And Neurite Density Imaging) model. We first simulate the signal of grey matter using the SpinDoctor with real neuronal data and then reconstruct the fibers in the grey matter portion of real brain data. Experimental results demonstrate that our algorithm effectively captures the anisotropic features of grey matter regions and estimates grey matter fiber orientations with greater accuracy, closely reflecting real-world scenarios.