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

IF 1.1 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Yen-Fen Ko, Yue-Der Lin, Po-Lan Su
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引用次数: 0

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.

利用半siamese U-Net重建肺电阻抗断层成像的心脏相关图像。
导语:电阻抗断层扫描(EIT)广泛用于床边通气监测,但由于肺阻抗变化占主导地位,在重建心脏相关信号方面受到限制。本研究旨在利用一种新颖的半暹罗式U-Net结构重建肺电阻抗成像。方法:采用共享编码器和两个解码器开发深度学习模型,设计用于独立分割肺和心脏区域。该模型在基于有限元的EIT仿真中进行了训练和验证,并在真实人体EIT数据上进行了测试。采用加权二元交叉熵损失来强调心脏相关学习。结果:模型达到了Dice系数>0.99和MAE。讨论:这些发现表明半暹罗U-Net可以克服信号优势,改善心脏相关的EIT重建。然而,目前有希望的结果仅限于对真实数据的定性评估和基于模拟的训练。结论:该方法为ICU环境下肺-心同步监测提供了一条潜在的途径。未来的工作将集中在临床验证和实时实施上。
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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
246
审稿时长
1 months
期刊介绍: Current Medical Imaging Reviews publishes frontier review articles, original research articles, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques. The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.
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