3D Reconstruction for Partial Data Electrical Impedance Tomography Using a Sparsity Prior

TL;DR

This paper extends 2D electrical impedance tomography methods to 3D, using sparsity and spatial priors to improve reconstructions from partial boundary measurements, even in noisy conditions.

Contribution

It introduces a novel 3D reconstruction approach incorporating sparsity and spatial priors with a generalized conditional gradient method.

Findings

Effective reconstruction with partial data

Robustness to measurement noise

Improved accuracy over traditional methods

Abstract

In electrical impedance tomography the electrical conductivity inside a physical body is computed from electro-static boundary measurements. The focus of this paper is to extend recent result for the 2D problem to 3D. Prior information about the sparsity and spatial distribution of the conductivity is used to improve reconstructions for the partial data problem with Cauchy data measured only on a subset of the boundary. A sparsity prior is enforced using the $\ell_1$ norm in the penalty term of a Tikhonov functional, and spatial prior information is incorporated by applying a spatially distributed regularization parameter. The optimization problem is solved numerically using a generalized conditional gradient method with soft thresholding. Numerical examples show the effectiveness of the suggested method even for the partial data problem with measurements affected by noise.