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Yang L, Gao Z, Wang C, Wang H, Dai J, Liu Y, Qin Y, Dai M, Cao X, Zhao Z. Evaluation of adjacent and opposite current injection patterns for a wearable chest electrical impedance tomography system. Physiol Meas 2024; 45:025004. [PMID: 38266301 DOI: 10.1088/1361-6579/ad2215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 01/24/2024] [Indexed: 01/26/2024]
Abstract
Objective.Wearable electrical impedance tomography (EIT) can be used to monitor regional lung ventilation and perfusion at the bedside. Due to its special system architecture, the amplitude of the injected current is usually limited compared to stationary EIT system. This study aims to evaluate the performance of current injection patterns with various low-amplitude currents in healthy volunteers.Approach.A total of 96 test sets of EIT measurement was recorded in 12 healthy subjects by employing adjacent and opposite current injection patterns with four amplitudes of small current (i.e. 1 mA, 500 uA, 250 uA and 125 uA). The performance of the two injection patterns with various currents was evaluated in terms of signal-to-noise ratio (SNR) of thorax impedance, EIT image metrics and EIT-based clinical parameters.Main results.Compared with adjacent injection, opposite injection had higher SNR (p< 0.01), less inverse artifacts (p< 0.01), and less boundary artifacts (p< 0.01) with the same current amplitude. In addition, opposite injection exhibited more stable EIT-based clinical parameters (p< 0.01) across the current range. For adjacent injection, significant differences were found for three EIT image metrics (p< 0.05) and four EIT-based clinical parameters (p< 0.01) between the group of 125 uA and the other groups.Significance.For better performance of wearable pulmonary EIT, currents greater than 250 uA should be used in opposite injection, 500 uA in adjacent one, to ensure a high level of SNR, a high quality of reconstructed image as well as a high reliability of clinical parameters.
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Affiliation(s)
- Lin Yang
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, People's Republic of China
| | - Zhijun Gao
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, People's Republic of China
| | - Chunchen Wang
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, People's Republic of China
| | - Hang Wang
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, People's Republic of China
| | - Jing Dai
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, People's Republic of China
| | - Yang Liu
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, People's Republic of China
| | - Yilong Qin
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, People's Republic of China
| | - Meng Dai
- Department of Biomedical Engineering, Air Force Medical University, Xi'an, People's Republic of China
| | - Xinsheng Cao
- Department of Aerospace Medicine, Air Force Medical University, Xi'an, People's Republic of China
| | - Zhanqi Zhao
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, People's Republic of China
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Beijing, People's Republic of China
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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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Yang L, Qu S, Zhang Y, Zhang G, Wang H, Yang B, Xu C, Dai M, Cao X. Removing Clinical Motion Artifacts During Ventilation Monitoring With Electrical Impedance Tomography: Introduction of Methodology and Validation With Simulation and Patient Data. Front Med (Lausanne) 2022; 9:817590. [PMID: 35174192 PMCID: PMC8841770 DOI: 10.3389/fmed.2022.817590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
Objective Electrical impedance tomography (EIT) is a bedside tool for lung ventilation and perfusion assessment. However, the ability for long-term monitoring diminished due to interferences from clinical interventions and motion artifacts. The purpose of this study is to investigate the feasibility of the discrete wavelet transform (DWT) to detect and remove the common types of motion artifacts in thoracic EIT. Methods Baseline drifting, step-like and spike-like interferences were simulated to mimic three common types of motion artifacts. The discrete wavelet decomposition was employed to characterize those motion artifacts in different frequency levels with different wavelet coefficients, and those motion artifacts were then attenuated by suppressing the relevant wavelet coefficients. Further validation was conducted in two patients when motion artifacts were introduced through pulsating mattress and deliberate body movements. The db8 wavelet was used to decompose the contaminated signals into several sublevels. Results In the simulation study, it was shown that, after being processed by DWT, the signal consistency improved by 92.98% for baseline drifting, 97.83% for the step-like artifact, and 62.83% for the spike-like artifact; the signal similarity improved by 77.49% for baseline drifting, 73.47% for the step-like artifact, and 2.35% for the spike-like artifact. Results from patient data demonstrated the EIT image errors decreased by 89.24% (baseline drifting), 88.45% (step-like artifact), and 97.80% (spike-like artifact), respectively; the data correlations between EIT images without artifacts and the processed were all > 0.95. Conclusion This study found that DWT is a universal and effective tool to detect and remove these motion artifacts.
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Affiliation(s)
- Lin Yang
- Department of Aerospace Medicine, Fourth Military Medical University, Xi'an, China
| | - Shuoyao Qu
- Department of Pulmonary and Critical Care Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Yanwei Zhang
- Department of Medical Imaging, Bethune International Peace Hospital, Shijiazhuang, China
| | - Ge Zhang
- Department of Medical Imaging, Bethune International Peace Hospital, Shijiazhuang, China
- Department of Medical Imaging, Henan Provincial People's Hospital and the People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Hang Wang
- Department of Aerospace Medicine, Fourth Military Medical University, Xi'an, China
| | - Bin Yang
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Canhua Xu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Meng Dai
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
- *Correspondence: Meng Dai
| | - Xinsheng Cao
- Department of Aerospace Medicine, Fourth Military Medical University, Xi'an, China
- Xinsheng Cao
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Hauptmann A, Smyl D. Fusing electrical and elasticity imaging. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200194. [PMID: 33966458 DOI: 10.1098/rsta.2020.0194] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Electrical and elasticity imaging are promising modalities for a suite of different applications, including medical tomography, non-destructive testing and structural health monitoring. These emerging modalities are capable of providing remote, non-invasive and low-cost opportunities. Unfortunately, both modalities are severely ill-posed nonlinear inverse problems, susceptive to noise and modelling errors. Nevertheless, the ability to incorporate complimentary datasets obtained simultaneously offers mutually beneficial information. By fusing electrical and elastic modalities as a joint problem, we are afforded the possibility to stabilize the inversion process via the utilization of auxiliary information from both modalities as well as joint structural operators. In this study, we will discuss a possible approach to combine electrical and elasticity imaging in a joint reconstruction problem giving rise to novel multi-modality applications for use in both medical and structural engineering. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.
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Affiliation(s)
- Andreas Hauptmann
- Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
- Department of Computer Science, University College London, London, UK
| | - Danny Smyl
- Department of Civil and Structural Engineering, University of Sheffield, Sheffield, UK
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Liu D, Gu D, Smyl D, Khambampati AK, Deng J, Du J. Shape-Driven EIT Reconstruction Using Fourier Representations. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:481-490. [PMID: 33044928 DOI: 10.1109/tmi.2020.3030024] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Shape-driven approaches have been proposed as an effective strategy for the electrical impedance tomography (EIT) reconstruction problem in recent years. In order to augment the shape-driven approaches, we propose a new method that transforms the shape to be reconstructed as basic primitives directly modeled by using Fourier representations. To allow automatic topological changes between the basic primitives and surrounding objects simultaneously, Boolean operations are employed. The Boolean operations with direct representation of primitives can be utilized for dimensionality and ill-posedness reduction, enabling feasible shape and topology optimization with shape-driven approaches. As a proof of principle, we leverage the proposed method for two dimensional shape reconstruction in EIT with various conductivity distributions. We demonstrate that our method is able to improve EIT reconstructions by enabling accurate shape and topology optimization.
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