  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 and in vivo estimation of
microstructural organization in skeletal muscle from diffusion-weighted MRI
(dMRI). 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 to provide 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 within their associated confidence intervals,
demonstrating the capability of the proposed framework as a promising
non-invasive tool for assessing skeletal muscle health and function.
