Learning hidden elasticity with deep neural networks

被引:59
作者
Chen, Chun-Teh [1 ]
Gu, Grace X. [2 ]
机构
[1] Univ Calif Berkeley, Dept Mat Sci & Engn, Berkeley, CA 94720 USA
[2] Univ Calif Berkeley, Dept Mech Engn, Berkeley, CA 94720 USA
关键词
deep learning  neural networks  elastography  elasticity theory  inverse problems; INVERSE PROBLEMS; ELASTOGRAPHY; FORMULATION; ALGORITHM;
D O I
10.1073/pnas.2102721118
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
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页数:8
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