发表机构
École Polytechnique Fédérale de Lausanne (EPFL); Centre Hospitalier Universitaire Vaudois; University of Lausanne; CIBM Center for Biomedical Imaging（洛桑联邦理工学院; 洛桑大学附属医院; 洛桑大学; 生物医学成像中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一个基于蒙特卡洛字典的可靠性框架，通过三个评分识别扩散MRI微结构估计中的不可靠来源，并在合成及体内数据上验证其与实际误差的相关性，支持模型解释与发展。
AI 中文摘要
扩散加权MRI能够非侵入性地探测组织微结构，然而，由于逆问题固有的模糊性，解释微结构参数估计仍然具有挑战性。虽然基于仿真的方法可以纳入越来越逼真的组织模型，但它们仍然缺乏对推断参数可靠性和简并性的体素级表征。在此，我们引入了一个用于基于仿真的微结构估计的可靠性框架，该框架基于三个互补的评分，识别估计过程中不可靠性的不同来源：分布外信号、局部信号不匹配和参数简并性。该框架使用由几何逼真基底生成的1,050个合成体素的蒙特卡洛字典实现，参数范围基于大鼠胼胝体的电子显微镜测量。该字典涵盖生物学上可信的轴突半径（0.25-0.85 μm）、微观角扩散（0-10°）、堆积密度（60-92%）和内在扩散率（1.75-3.0 μm²/ms）。在合成数据上，所得可靠性指数与实际估计误差相关（Spearman ρ = -0.742），并区分了外推、局部插值不良和参数简并性。应用于体内大鼠胼胝体DW-MRI（256个体素，四只动物）和人类（MGH-USC HCP，18,765个体素，九名受试者），分别有91%和73%的体素超过R > 0.5。这些结果表明，可靠性感知分析如何支持基于仿真的扩散MRI微结构模型的解释和未来发展。
英文摘要
Diffusion-weighted MRI can probe tissue microstructure non-invasively, however interpreting microstructural parameter estimates remains challenging due to the intrinsic ambiguity of the inverse problem. While simulation-based approaches can incorporate increasingly realistic tissue models, they still lack voxel-wise characterisation of the reliability and degeneracy of the inferred parameters. Here, we introduce a reliability framework for simulation-based microstructure estimation based on three complementary scores that identify distinct sources of unreliability in the estimation process: out-of-distribution signals, local signal mismatch, and parameter degeneracy. The framework was implemented using a Monte Carlo dictionary of 1,050 synthetic voxels generated from geometrically realistic substrates, with parameter ranges grounded in electron microscopy measurements of rat corpus callosum. The dictionary spans biologically plausible axon radii (0.25-0.85 $μ$m), microscopic angular spread (0-10$^\circ$), packing densities (60-92%), and intrinsic diffusivities (1.75-3.0 $μ$m$^2$/ms). On synthetic data, the resulting Reliability Index correlated with actual estimation error (Spearman $ρ= -0.742$) and distinguished between extrapolation, poor local interpolation, and parameter degeneracy. Applied to in vivo corpus callosum DW-MRI in rat (256 voxels, four animals) and human (MGH-USC HCP, 18,765 voxels, nine subjects), 91% and 73% of voxels respectively exceeded $R > 0.5$. These results demonstrate how reliability-aware analysis can support the interpretation and future development of simulation-based diffusion MRI microstructure models.
