Mumford-Shah regularization in electrical impedance tomography with complete electrode model

TL;DR

This paper introduces a Mumford-Shah regularization approach for electrical impedance tomography with the complete electrode model, improving the reconstruction of targets with distinct regions.

Contribution

It demonstrates the theoretical applicability of a Mumford-Shah regularizer via $ ext{Γ}$-convergence and validates its effectiveness through numerical and experimental results.

Findings

Higher quality reconstructions for piecewise smooth conductivities

The Mumford-Shah regularizer outperforms traditional smoothness regularizations

The approach is feasible within the complete electrode model framework

Abstract

In electrical impedance tomography, we aim to solve the conductivity within a target body through electrical measurements made on the surface of the target. This inverse conductivity problem is severely ill-posed, especially in real applications with only partial boundary data available. Thus regularization has to be introduced. Conventionally regularization promoting smooth features is used, however, the Mumford--Shah regularizer familiar for image segmentation is more appropriate for targets consisting of several distinct objects or materials. It is, however, numerically challenging. We show theoretically through $\Gamma$-convergence that a modification of the Ambrosio--Tortorelli approximation of the Mumford--Shah regularizer is applicable to electrical impedance tomography, in particular the complete electrode model of boundary measurements. With numerical and experimental studies, we confirm that this functional works in practice and produces higher quality results than typical regularizations employed in electrical impedance tomography when the conductivity of the target consists of distinct smoothly-varying regions.