Edge-enhancing reconstruction algorithm for three-dimensional electrical impedance tomography

TL;DR

This paper presents an iterative edge-enhancing reconstruction algorithm for 3D electrical impedance tomography that effectively handles complex meshes and electrode configurations, improving image quality in practical applications.

Contribution

It introduces a novel edge-preferring prior-based iterative method using lagged diffusivity for 3D EIT reconstruction on dense meshes with many electrodes.

Findings

Successfully reconstructs conductivity with embedded inhomogeneities.

Capable of handling dense unstructured 3D meshes.

Validated with simulated and experimental data.

Abstract

Electrical impedance tomography is an imaging modality for extracting information on the conductivity distribution inside a physical body from boundary measurements of current and voltage. In many practical applications, it is a priori known that the conductivity consists of embedded inhomogeneities in an approximately constant background. This work introduces an iterative reconstruction algorithm that aims at finding the maximum a posteriori estimate for the conductivity assuming an edge-preferring prior. The method is based on applying (a single step of) priorconditioned lagged diffusivity iteration to sequential linearizations of the forward model. The algorithm is capable of producing reconstructions on dense unstructured three-dimensional finite element meshes and with a high number of measurement electrodes. The functionality of the proposed technique is demonstrated with both simulated and experimental data in the framework of the complete electrode model, which is the most accurate model for practical impedance tomography.