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Zhang T, Tian X, Liu X, Ye J, Fu F, Shi X, Liu R, Xu C. Advances of deep learning in electrical impedance tomography image reconstruction. Front Bioeng Biotechnol 2022; 10:1019531. [PMID: 36588934 PMCID: PMC9794741 DOI: 10.3389/fbioe.2022.1019531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 12/02/2022] [Indexed: 12/15/2022] Open
Abstract
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.
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Affiliation(s)
- Tao Zhang
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China,Drug and Instrument Supervision and Inspection Station, Xining Joint Logistics Support Center, Lanzhou, China
| | - Xiang Tian
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - XueChao Liu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - JianAn Ye
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - Feng Fu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - XueTao Shi
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - RuiGang Liu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China
| | - CanHua Xu
- Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China,Shaanxi Key Laboratory for Bioelectromagnetic Detection and Intelligent Perception, Xi’an, China,*Correspondence: CanHua Xu,
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Bayford R, Sadleir R, Frerichs I. Advances in electrical impedance tomography and bioimpedance including applications in COVID-19 diagnosis and treatment. Physiol Meas 2022; 43. [DOI: 10.1088/1361-6579/ac4e6c] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 01/24/2022] [Indexed: 11/12/2022]
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