Robust imaging using electrical impedance tomography: review of current tools.

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Computational Electromagnetics

Background:

• Electrical impedance tomography (EIT) is a developing medical imaging modality with significant potential.
• Current EIT applications are limited by the ill-posed nature of the inverse problem, affecting tool genericity.
• Robustness in EIT data acquisition and processing is crucial for widespread clinical adoption.

Purpose of the Study:

• To present and analyze robust data acquisition and processing tools for EIT.
• To assess the potential of these tools in improving EIT's robustness across various applications.
• To determine the feasibility of a universal, robust EIT tool for resistivity mapping.

Main Methods:

• Review of scientific literature on robust EIT data acquisition and processing tools.
• Analysis of techniques for faulty electrode detection and specialized electrode designs.
• Description of algorithms designed to overcome inverse problem challenges like boundary errors and noise.

Main Results:

• Faulty electrode detection algorithms and specific electrode designs enhance data acquisition quality.
• Processing algorithms effectively address issues like boundary position errors and ill-conditioned inverse problems.
• Identified tools demonstrate potential for high-quality image reconstruction despite disturbances.

Conclusions:

• Robust data acquisition and processing are key to overcoming EIT limitations.
• The reviewed tools show significant potential for creating a generic, robust EIT system.
• Feasible development of a robust EIT tool for diverse resistivity mapping applications is supported.