IEEE Trans Med Imaging. 2024 Nov;43(11):3698-3709. doi: 10.1109/TMI.2024.3397790. Epub 2024 Nov 4.
Muscle health is a critical component of overall health and quality of life. However, current measures of skeletal muscle health take limited account of microstructural variations within muscle, which play a crucial role in mediating muscle function. To address this, we present a physics-inspired, machine learning-based framework for the non-invasive estimation of microstructural organization in skeletal muscle from diffusion-weighted MRI (dMRI) in an uncertainty-aware manner. To reduce the computational expense associated with direct numerical simulations of dMRI physics, a polynomial meta-model is developed that accurately represents the input/output relationships of a high-fidelity numerical model. This meta-model is used to develop a Gaussian process (GP) model that provides voxel-wise estimates and confidence intervals of microstructure organization in skeletal muscle. Given noise-free data, the GP model accurately estimates microstructural parameters. In the presence of noise, the diameter, intracellular diffusion coefficient, and membrane permeability are accurately estimated with narrow confidence intervals, while volume fraction and extracellular diffusion coefficient are poorly estimated and exhibit wide confidence intervals. A reduced-acquisition GP model, consisting of one-third the diffusion-encoding measurements, is shown to predict parameters with similar accuracy to the original model. The fiber diameter and volume fraction estimated by the reduced GP model is validated via histology, with both parameters accurately estimated, demonstrating the capability of the proposed framework as a promising non-invasive tool for assessing skeletal muscle health and function.
肌肉健康是整体健康和生活质量的关键组成部分。然而，目前评估骨骼肌健康的方法仅有限地考虑了肌肉内的微观结构变化，而这些变化在调节肌肉功能方面起着至关重要的作用。为了解决这个问题，我们提出了一种基于物理启发和机器学习的框架，用于以不确定感知的方式从扩散加权 MRI（dMRI）中非侵入性地估计骨骼肌的微观结构组织。为了降低直接数值模拟 dMRI 物理的计算费用，开发了一个多项式元模型，该模型准确地表示了高保真数值模型的输入/输出关系。该元模型用于开发高斯过程（GP）模型，该模型提供了骨骼肌微观结构组织的体素级估计和置信区间。在无噪声数据的情况下，GP 模型可以准确地估计微观结构参数。在存在噪声的情况下，直径、细胞内扩散系数和膜通透性可以以较窄的置信区间准确估计，而体积分数和细胞外扩散系数估计较差，置信区间较宽。减少采集的 GP 模型，仅包含三分之一的扩散编码测量值，被证明可以以与原始模型相似的精度预测参数。通过组织学验证了由简化 GP 模型估计的纤维直径和体积分数，这两个参数都得到了准确的估计，证明了所提出的框架作为一种有前途的非侵入性工具来评估骨骼肌健康和功能的能力。
