Monotonicity-based regularization for phantom experiment data in Electrical Impedance Tomography

TL;DR

This paper introduces a monotonicity-based regularization method for electrical impedance tomography that enhances image quality and reduces artifacts by incorporating a linear constraint, validated through phantom experiments.

Contribution

It proposes a novel regularization approach using monotonicity constraints within the linearized-data-fit framework for EIT reconstruction.

Findings

Improved image quality in phantom experiments.

Significant reduction of ringing artifacts.

Enhanced robustness of the reconstruction algorithm.

Abstract

In electrical impedance tomography, algorithms based on minimizing the linearized-data-fit residuum have been widely used due to their real-time implementation and satisfactory reconstructed images. However, the resulting images usually tend to contain ringing artifacts. In this work, we shall minimize the linearized-data-fit functional with respect to a linear constraint defined by the monotonicity relation in the framework of real electrode setting. Numerical results of standard phantom experiment data confirm that this new algorithm improves the quality of the reconstructed images as well as reduce the ringing artifacts.