Fast 3D Partial Boundary Data EIT Reconstructions using Direct Inversion CGO-based Methods

TL;DR

This paper introduces fast, non-iterative 3D electrical impedance tomography algorithms based on complex geometrical optics for partial boundary data, enabling rapid and accurate imaging in medical applications.

Contribution

The paper develops the first partial boundary data CGO-based methods for 3D EIT that are fast, require no iteration, and perform well with noisy data and model inaccuracies.

Findings

Reconstruction times under 2 seconds, much faster than traditional methods.

Good localization of targets with partial boundary data.

Robust performance under high noise and modeling errors.

Abstract

The first partial boundary data complex geometrical optics based methods for electrical impedance tomography in three dimensions are developed, and tested, on simulated and experimental data. The methods provide good localization of targets for both absolute and time-difference imaging, when large portions of the domain are inaccessible for measurement. As most medical applications of electrical impedance tomography are limited to partial boundary data, the development of partial boundary algorithms is highly desirable. While iterative schemes have been used traditionally, their high computational cost makes them cost-prohibitive for applications that need fast imaging. The proposed algorithms require no iteration and provide informative absolute or time-difference images exceptionally quickly in under 2 seconds. Reconstructions are compared to reference reconstructions from standard linear difference imaging (30 seconds) and total variation regularized absolute imaging (several minutes) The algorithms perform well under high levels of noise and incorrect domain modeling.