Solving electrical impedance tomography with deep learning

JOURNAL OF COMPUTATIONAL PHYSICS

卷:404

Article

Fan, Yuwei¹  Ying, Lexing^1,2 

[1] Stanford Univ, Dept Math, Stanford, CA 94305 USA

[2] Stanford Univ, ICME, Stanford, CA 94305 USA

关键词: Dirichlet-to-Neumann map;    Electrical impedance tomography;    Inverse problem;    Neural networks;    BCR-Net;    Convolutional neural network;   

DOI  :  10.1016/j.jcp.2019.109119

来源: Elsevier

【 摘 要 】

This paper introduces a new approach for solving electrical impedance tomography (EIT) problems using deep neural networks. The mathematical problem of EIT is to invert the electrical conductivity from the Dirichlet-to-Neumann (DtN) map. Both the forward map from the electrical conductivity to the DtN map and the inverse map are high-dimensional and nonlinear. Motivated by the linear perturbative analysis of the forward map and based on a numerically low-rank property, we propose compact neural network architectures for the forward and inverse maps for both 2D and 3D problems. Numerical results demonstrate the efficiency of the proposed neural networks. (C) 2019 Elsevier Inc. All rights reserved.

【 授权许可】

Free