ArticleJournal of the mechanical behavior of biomedical materials2022
Estimation of the mechanical properties of a transversely isotropic material from shear wave fields via artificial neural networks.
Article in Journal of the mechanical behavior of biomedical materials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- APRIL: Adaptive Regression-Based Two-Dimensional Quantitative Anisotropy Imaging Using Acoustic Radiation Force Impulse.bioRxiv : the preprint server for biology · 2026Article
- Estimating shear modulus of isotropic materials from scanning laser Doppler vibrometry via convolutional neural networks.Journal of the mechanical behavior of biomedical materials · 2025Article
- Post-mortem changes of anisotropic mechanical properties in the porcine brain assessed by MR elastography.Brain multiphysics · 2024Article
- Shear wave speeds in a nearly incompressible fibrous material with two unequal fiber families.The Journal of the Acoustical Society of America · 2024Article
- Mechanical stiffness and anisotropy measured by MRE during brain development in the minipig.NeuroImage · 2023Article
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Authors and funding
7 authors.
Funding
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.
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