Reconstruction of Heart-related Imaging from Lung Electrical Impedance Tomography Using Semi-Siamese U-Net
Yen-Fen Ko, Yue-Der Lin, Po-lan Su
TL;DR
This paper introduces a new deep learning model to separately reconstruct heart and lung images from EIT data, improving cardiac monitoring in ICU settings.
Contribution
A novel semi-Siamese U-Net architecture is proposed to overcome signal dominance and enable heart-related EIT reconstruction.
Findings
The model achieved a Dice coefficient >0.99 and MAE <0.1% on simulation data.
It successfully separated lung and heart regions in real human EIT data without fine-tuning.
Abstract
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. 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. 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. These findings reveal that the…
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Taxonomy
TopicsHemodynamic Monitoring and Therapy · Electrical and Bioimpedance Tomography · Intravenous Infusion Technology and Safety