MPI可精准捕捉神经组织微结构变化，不受宏观排列干扰。
Microscopic Propagator Imaging (MPI) with Diffusion MRI
- 基于球谐函数建模与机器学习回归，解构水分子微观位移分布。
- 在合成数据和人脑扩散MRI上验证，指标对微结构变化更敏感。
- 适合研究轴突、细胞等微结构损伤，如神经退行性疾病早期检测。
我们提出一种新方法——微观传播子成像（Microscopic Propagator Imaging, MPI），用于获取微观传播子的指标，即水分子在神经组织微结构内因扩散产生的位移概率密度函数。与集合平均传播子指标或扩散张量成像（DTI）指标不同，MPI指标不受组织宏观排列（如多束轴突方向及取向分散）影响。因此，这些指标更能特异性反映组织中体积、尺寸和类型的微结构（如轴突和细胞）信息。当微结构发生改变时，MPI指标可更直接关联其存在状态与完整性变化。该方法基于球谐函数的分区域建模、信号仿真与机器学习回归，并在合成数据和人类扩散磁共振成像（Human Diffusion MRI）数据上得到验证。
原文摘要 · Abstract (English)
We propose Microscopic Propagator Imaging (MPI) as a novel method to retrieve the indices of the microscopic propagator which is the probability density function of water displacements due to diffusion within the nervous tissue microstructures. Unlike the Ensemble Average Propagator indices or the Diffusion Tensor Imaging metrics, MPI indices are independent from the mesoscopic organization of the tissue such as the presence of multiple axonal bundle directions and orientation dispersion. As a consequence, MPI indices are more specific to the volumes, sizes, and types of microstructures, like axons and cells, that are present in the tissue. Thus, changes in MPI indices can be more directly linked to alterations in the presence and integrity of microstructures themselves. The methodology behind MPI is rooted on zonal modeling of spherical harmonics, signal simulation, and machine learning regression, and is demonstrated on both synthetic and Human Diffusion MRI data.
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