ArticleProceedings of the National Academy of Sciences of the United States of America2021
Learning hidden elasticity with deep neural networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Stress-Constrained Physics-Informed UNet for Voxel-Wise Multiparameter Hyperelastic Inversion in Volumetric Medical Imaging.Annals of biomedical engineering · 2026Article
- Robust Physics-Informed Neural Network Approach for Estimating Heterogeneous Elastic Properties from Noisy Displacement Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Optimal experimental design for repeatable hyperelastic material characterization.Journal of the mechanical behavior of biomedical materials · 2025Article
- Ultrasound elastic modulus reconstruction using a deep learning model trained with simulated data.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- A machine learning-based framework for mapping hydrogen at the atomic scale.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- [Reconstruction of elasticity modulus distribution base on semi-supervised neural network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Physics-informed UNets for discovering hidden elasticity in heterogeneous materials.Journal of the mechanical behavior of biomedical materials · 2024Article
- Segmenting mechanically heterogeneous domains via unsupervised learning.Biomechanics and modeling in mechanobiology · 2024Article
- Benchmarking physics-informed frameworks for data-driven hyperelasticity.Computational mechanics · 2024Article
- Can machine learning accelerate soft material parameter identification from complex mechanical test data?Biomechanics and modeling in mechanobiology · 2023Article
- Elasticity imaging using physics-informed neural networks: Spatial discovery of elastic modulus and Poisson's ratio.Acta biomaterialia · 2023Article
- Design of a flexing organ-chip to modelBiomicrofluidics · 2022Article
- Scalable Graphene Defect Prediction Using Transferable Learning.Nanomaterials (Basel, Switzerland) · 2021Article
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2 authors.
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Abstract
Elastography is an imaging technique to reconstruct elasticity distributions of heterogeneous objects. Since cancerous tissues are stiffer than healthy ones, for decades, elastography has been applied to medical imaging for noninvasive cancer diagnosis. Although the conventional strain-based elastography has been deployed on ultrasound diagnostic-imaging devices, the results are prone to inaccuracies. Model-based elastography, which reconstructs elasticity distributions by solving an inverse problem in elasticity, may provide more accurate results but is often unreliable in practice due to the ill-posed nature of the inverse problem. We introduce ElastNet, a de novo elastography method combining the theory of elasticity with a deep-learning approach. With prior knowledge from the laws of physics, ElastNet can escape the performance ceiling imposed by labeled data. ElastNet uses backpropagation to learn the hidden elasticity of objects, resulting in rapid and accurate predictions. We show that ElastNet is robust when dealing with noisy or missing measurements. Moreover, it can learn probable elasticity distributions for areas even without measurements and generate elasticity images of arbitrary resolution. When both strain and elasticity distributions are given, the hidden physics in elasticity-the conditions for equilibrium-can be learned by ElastNet.
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