Zhang Tao, Tian Xiang, Liu XueChao, Ye JianAn, Fu Feng, Shi XueTao, Liu RuiGang, Xu CanHua
Department of Biomedical Engineering, The Fourth Military Medical University, Xi'an, China.
Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi'an, China.
Front Bioeng Biotechnol. 2022 Dec 14;10:1019531. doi: 10.3389/fbioe.2022.1019531. eCollection 2022.
Electrical impedance tomography (EIT) has been widely used in biomedical research because of its advantages of real-time imaging and nature of being non-invasive and radiation-free. Additionally, it can reconstruct the distribution or changes in electrical properties in the sensing area. Recently, with the significant advancements in the use of deep learning in intelligent medical imaging, EIT image reconstruction based on deep learning has received considerable attention. This study introduces the basic principles of EIT and summarizes the application progress of deep learning in EIT image reconstruction with regards to three aspects: a single network reconstruction, deep learning combined with traditional algorithm reconstruction, and multiple network hybrid reconstruction. In future, optimizing the datasets may be the main challenge in applying deep learning for EIT image reconstruction. Adopting a better network structure, focusing on the joint reconstruction of EIT and traditional algorithms, and using multimodal deep learning-based EIT may be the solution to existing problems. In general, deep learning offers a fresh approach for improving the performance of EIT image reconstruction and could be the foundation for building an intelligent integrated EIT diagnostic system in the future.
电阻抗断层成像（EIT）因其具有实时成像的优势以及无创、无辐射的特性，已在生物医学研究中得到广泛应用。此外，它能够重建传感区域内电特性的分布或变化情况。近年来，随着深度学习在智能医学成像领域的重大进展，基于深度学习的EIT图像重建受到了广泛关注。本研究介绍了EIT的基本原理，并从单网络重建、深度学习与传统算法相结合的重建以及多网络混合重建三个方面总结了深度学习在EIT图像重建中的应用进展。未来，优化数据集可能是将深度学习应用于EIT图像重建的主要挑战。采用更好的网络结构、关注EIT与传统算法的联合重建以及使用基于多模态深度学习的EIT可能是解决现有问题的方法。总体而言，深度学习为提高EIT图像重建性能提供了一种新方法，并且可能成为未来构建智能集成EIT诊断系统的基础。
