Estimation of the mechanical properties of a transversely isotropic material from shear wave fields via artificial neural networks. (February 2022)

• Record Type: Journal Article
• Authors: Hou, Zuoxian; Guertler, Charlotte A.; Okamoto, Ruth J.; Chen, Hong; Garbow, Joel R.; Kamilov, Ulugbek S.; Bayly, Philip V.
• Is Part Of: Journal of the mechanical behavior of biomedical materials. Volume 126(2022)
• Publication Date: 2022-02
• Subjects: Anisotropy -- MR elastography -- Focused ultrasound -- Artificial neural network -- Machine learning; Biomedical materials -- Periodicals; Biomedical materials -- Mechanical properties -- Periodicals; Biomedical materials; Biomedical materials -- Mechanical properties; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.jmbbm.2021.105046
• Languages: English
• ISSNs: 1751-6161

Abstract

Abstract: 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 μ, shear anisotropy φ, and tensile anisotropy ζ ). 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. Highlights: Shear wave speeds and strains depend on fiber directions in anisotropic tissue. Artificial neural networks were used to estimate anisotropic material parameters. Neural nets were trained on simulations to estimate parameters from experiments. Anisotropic parameter estimates are robust to choices in modeling and fitting.