Reconstruction of Heart-related Imaging from Lung Electrical Impedance Tomography Using Semi-Siamese U-Net

Yen-Fen Ko; Yue-Der Lin; Po-Lan Su

doi:10.2174/0115734056408077250610070821

Reconstruction of Heart-related Imaging from Lung Electrical Impedance Tomography Using Semi-Siamese U-Net

Curr Med Imaging. 2025 Jul 2. doi: 10.2174/0115734056408077250610070821. Online ahead of print.

Authors

Yen-Fen Ko¹, Yue-Der Lin², Po-Lan Su³

Affiliations

¹ Department of Biomedical Engineering, College of Biomedical Engineering, China Medical University, Taichung, Taiwan.
² Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan.
³ Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.

PMID: 40611416
DOI: 10.2174/0115734056408077250610070821

Abstract

Introduction: Electrical Impedance Tomography (EIT) is widely used for bedside ventilation monitoring but is limited in reconstructing cardiac-related signals due to the dominance of lung impedance changes. This study aims to reconstruct heart-related impedance imaging from lung EIT using a novel semi-Siamese U-Net architecture.

Methods: A deep learning model was developed with a shared encoder and two decoders designed to segment lung and heart regions independently. The model was trained and validated on FEM-based EIT simulations and tested on real human EIT data. A weighted binary cross-entropy loss was applied to emphasize cardiac-related learning.

Results: The model achieved a Dice coefficient >0.99 and MAE <0.1% on simulation data. It successfully separated lung and heart regions on human EIT frames without additional fine-tuning, demonstrating strong generalization capacity.

Discussion: These findings reveal that the semi-Siamese U-Net can overcome signal dominance and improve cardiac-related EIT reconstruction. However, promising results are currently limited to qualitative evaluation of real data and simulation-based training.

Conclusion: The proposed method offers a potential pathway for simultaneous lung-heart monitoring in ICU settings. Future work will focus on clinical validation and real-time implementation.

Keywords: ARDS.; Electrical impedance tomography; Heart imaging; ICU settings; Lung imaging; PEEP; Semi-Siamese U-Net.