Deep Learning Based Reconstruction Methods for Electrical Impedance Tomography

TL;DR

This paper reviews deep learning methods for electrical impedance tomography, highlighting their advantages over traditional techniques in image reconstruction accuracy, especially for in-distribution data, while noting challenges in generalization.

Contribution

It provides a comprehensive comparison of learned and model-based reconstruction methods for EIT, emphasizing the effectiveness of hybrid approaches in real-world applications.

Findings

Learned methods outperform traditional techniques on in-distribution data.

Hybrid methods balance accuracy and generalization in EIT reconstruction.

Deep learning approaches face challenges in generalizing to out-of-distribution data.

Abstract

Electrical Impedance Tomography (EIT) is a powerful imaging modality widely used in medical diagnostics, industrial monitoring, and environmental studies. The EIT inverse problem is about inferring the internal conductivity distribution of the concerned object from the voltage measurements taken on its boundary. This problem is severely ill-posed, and requires advanced computational approaches for accurate and reliable image reconstruction. Recent innovations in both model-based reconstruction and deep learning have driven significant progress in the field. In this review, we explore learned reconstruction methods that employ deep neural networks for solving the EIT inverse problem. The discussion focuses on the complete electrode model, one popular mathematical model for real-world applications of EIT. We compare a wide variety of learned approaches, including fully-learned, post-processing and learned iterative methods, with several conventional model-based reconstruction techniques, e.g., sparsity regularization, regularized Gauss-Newton iteration and level set method. The evaluation is based on three datasets: a simulated dataset of ellipses, an out-of-distribution simulated dataset, and the KIT4 dataset, including real-world measurements. Our results demonstrate that learned methods outperform model-based methods for in-distribution data but face challenges in generalization, where hybrid methods exhibit a good balance of accuracy and adaptability.