Solving Electrical Impedance Tomography with Deep Learning
Computational Physics
2020-01-29 v2 Machine Learning
Numerical Analysis
Numerical Analysis
Abstract
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.
Cite
@article{arxiv.1906.03944,
title = {Solving Electrical Impedance Tomography with Deep Learning},
author = {Yuwei Fan and Lexing Ying},
journal= {arXiv preprint arXiv:1906.03944},
year = {2020}
}
Comments
22 pages, 17 figures