# Solving Electrical Impedance Tomography with Deep Learning

## TL;DR

This paper presents a novel deep learning approach to efficiently solve the high-dimensional, nonlinear inverse problem of electrical impedance tomography, improving reconstruction accuracy and computational speed.

## Contribution

It introduces compact neural network architectures for both forward and inverse EIT maps, leveraging low-rank properties for 2D and 3D problems.

## Key findings

- Neural networks accurately reconstruct conductivity from DtN maps.
- Proposed methods are computationally efficient.
- Effective for both 2D and 3D EIT problems.

## 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.