# Monotonicity-based Electrical Impedance Tomography for Lung Imaging

## TL;DR

This paper introduces a novel monotonicity-based EIT method for continuous lung monitoring, leveraging periodic data decomposition and conductivity change constraints to enhance imaging quality, validated through simulations and experiments.

## Contribution

The paper proposes a new monotonicity-based approach for lung EIT imaging that improves image quality by exploiting conductivity change properties.

## Key findings

- Enhanced lung imaging quality demonstrated in simulations.
- Successful application in phantom and human experiments.
- Effective separation of pulmonary, cardiac, and other signals.

## Abstract

This paper presents a monotonicity-based spatiotemporal conductivity imaging method for continuous regional lung monitoring using electrical impedance tomography (EIT). The EIT data (i.e., the boundary current-voltage data) can be decomposed into pulmonary, cardiac and other parts using their different periodic natures. The time-differential current-voltage operator corresponding to the lung ventilation can be viewed as either semi-positive or semi-negative definite owing to monotonic conductivity changes within the lung regions. We used this monotonicity constraints to improve the quality of lung EIT imaging. We tested the proposed methods in numerical simulations, phantom experiments and human experiments.