Robust Computation in 2D Absolute EIT (a-EIT) Using D-bar Methods with the `exp' Approximation

TL;DR

This paper demonstrates that 2D D-bar methods for absolute Electrical Impedance Tomography (EIT) are robust against modeling errors like shape and electrode placement inaccuracies, making them promising for clinical applications.

Contribution

The study shows that D-bar reconstruction methods for absolute EIT maintain robustness against modeling errors, unlike traditional approaches, and are validated on experimental tank data.

Findings

D-bar methods are robust to shape and electrode errors.

Artefacts in absolute EIT images resemble those in time-difference EIT.

Experimental validation confirms robustness on multiple EIT systems.

Abstract

Objective: Absolute images have important applications in medical Electrical Impedance Tomography (EIT) imaging, but the traditional minimization and statistical based computations are very sensitive to modeling errors and noise. In this paper, it is demonstrated that D-bar reconstruction methods for absolute EIT are robust to such errors. Approach: The effects of errors in domain shape and electrode placement on absolute images computed with 2D D-bar reconstruction algorithms are studied on experimental data. Main Results: It is demonstrated with tank data from several EIT systems that these methods are quite robust to such modeling errors, and furthermore the artefacts arising from such modeling errors are similar to those occurring in classic time-difference EIT imaging. Significance: This study is promising for clinical applications where absolute EIT images are desirable, but previously thought impossible.