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Related Experiment Video

Updated: Jun 26, 2026

Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit

Monitoring Lung Function with Electrical Impedance Tomography in the Intensive Care Unit

Published on: September 6, 2024

Three dimensional electrical impedance tomography in thorax complete model.

Huanli Wu¹, Guizhi Xu, Hongli Yu

¹Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, 300130, China. hlwu@hebut.edu.cn

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 24, 2009

Summary

This study presents a 3D electrical impedance tomography model for monitoring thorax activities like breathing. The inverse problem solution aids in understanding lung, muscle, and heart functions.

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Area of Science:

Biomedical Engineering
Medical Imaging
Computational Electrophysiology

Background:

Electrical Impedance Tomography (EIT) is a non-invasive imaging technique.
Monitoring thoracic activities like respiration is crucial for clinical assessment.
Accurate modeling of the thorax is essential for effective EIT.

Purpose of the Study:

To develop and validate a 3D Electrical Impedance Tomography (EIT) model of the thorax.
To research both the forward and inverse problems of EIT for thoracic monitoring.
To utilize EIT for observing physiological activities such as inhalation and exhalation.

Main Methods:

Finite element method (FEM) was employed to solve the forward problem, calculating potential distributions for inhaling and exhaling models.
The inverse problem was addressed using the Homotopy-Newton-Raphson algorithm.
Iterative impedance revisions were performed by comparing measured and computed potentials on electrodes.

Main Results:

A complete 3D thorax model was constructed, including lung, muscle, heart, and spinal column.
Potential distributions for both inhalation and exhalation phases were successfully computed.
The inverse problem solution demonstrated the feasibility of monitoring thoracic activities.

Conclusions:

The developed 3D EIT model provides a robust framework for analyzing thoracic activity.
The Homotopy-Newton-Raphson algorithm effectively solves the inverse problem in this context.
This research contributes to the advancement of non-invasive monitoring of cardiopulmonary functions.