Electrical Impedance Tomography in Medical Imaging: Fundamental Principles, Clinical Applications, and Future Innovation Trajectories

DOI

10.2991/978-94-6463-823-3_57How to use a DOI?

Keywords

Electrical Impedance Tomography; Medical Imaging; Deep Learning; Clinical Applications

Abstract

Electrical Impedance Tomography (EIT) is an emerging non-invasive imaging modality that leverages tissue-specific electrical conductivity variations to generate functional images, offering unique advantages over conventional techniques like X-ray, CT, MRI, and ultrasonography. Unlike radiation-based methods, EIT eliminates ionising exposure risks while providing portability, real-time monitoring, and lower costs. EIT applies a low-amplitude AC through the surface electrodes and measures the boundary voltage to reconstruct the internal conductivity distribution. Addressing inverse problems to reconstruct conductivity from voltage data, which face challenges like nonlinearity, underdetermination, and sensitivity variations. Algorithmic advancements are driving EIT innovation. Traditional model-based approaches, such as FEM and Bayesian probabilistic models, improve accuracy by addressing anatomical variability. Meanwhile, deep learning techniques enhance image reconstruction by correcting noise and artefacts, enabling high-resolution organ monitoring. Future developments focus on miniaturization, intelligent algorithms, and multimodal integration. With the innovations of AI and hardware, EIT is used in specialized fields, from real-time pneumothorax detection to sleep monitoring, ultimately bridging gaps in radiation-free medical imaging.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yimeng Sun
PY  - 2025
DA  - 2025/08/31
TI  - Electrical Impedance Tomography in Medical Imaging: Fundamental Principles, Clinical Applications, and Future Innovation Trajectories
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 570
EP  - 579
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_57
DO  - 10.2991/978-94-6463-823-3_57
ID  - Sun2025
ER  -