This work proposes $\mu$GUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or MRI signal representation, with exemplar demonstration in diffusion-weighted MRI. Harnessing a new deep learning architecture for automatic signal feature selection combined with simulation-based inference and efficient sampling of the posterior distributions, $\mu$GUIDE bypasses the high computational and time cost of conventional Bayesian approaches and does not rely on acquisition constraints to define model-specific summary statistics. The obtained posterior distributions allow to highlight degeneracies present in the model definition and quantify the uncertainty and ambiguity of the estimated parameters.

翻译：本研究提出μGUIDE：一种通用贝叶斯框架，用于从任意生物物理模型或MRI信号表示中估计组织微结构参数的后验分布，并以扩散加权MRI为例进行演示。通过采用新型深度学习架构实现自动信号特征选择，结合基于模拟的推断及后验分布的高效采样，μGUIDE规避了传统贝叶斯方法的高计算与时间成本，且无需依赖采集约束来定义模型特定的摘要统计量。所得后验分布能够揭示模型定义中存在的简并性，并对估计参数的不确定性与模糊性进行量化。