Estimation of the mechanical properties of a transversely isotropic material from shear wave fields via artificial neural networks

Artificial neural networks (ANN), established tools in machine learning, are applied to the problem of estimating parameters of a transversely isotropic (TI) material model using data from magnetic resonance elastography (MRE) and diffusion tensor imaging (DTI). We use neural networks to estimate parameters from experimental measurements of ultrasound-induced shear waves after training on analogous data from simulations of a computer model with similar loading, geometry, and boundary conditions. Strain ratios and shear-wave speeds (from MRE) and fiber direction (the direction of maximum diffusivity from diffusion tensor imaging (DTI)) are used as inputs to neural networks trained to estimate the parameters of a TI material (baseline shear modulus mu, shear anisotropy phi, and tensile anisotropy zeta). Ensembles of neural networks are applied to obtain distributions of parameter estimates. The robustness of this approach is assessed by quantifying the sensitivity of property estimates to assumptions in modeling (such as assumed loss factor) and choices in fitting (such as the size of the neural network). This study demonstrates the successful application of simulation-trained neural networks to estimate anisotropic material parameters from complementary MRE and DTI imaging data.

Automated summary

This study utilizes artificial neural networks to estimate parameters of a transversely isotropic material model using data from MRE and DTI. The inputs include strain ratios, shear-wave speeds, and fiber direction, and ensembles of neural networks are used to obtain parameter estimates. The robustness of this approach is evaluated.