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Tan Z, Lu S, Yang L, Xu Y, Qin S, Dai M, Li Z, Zhao Z. Research Trends and Hotspots of Medical Electrical Impedance Tomography Algorithms: A Bibliometric Analysis From 1987 to 2021. Cureus 2023; 15:e49700. [PMID: 38161896 PMCID: PMC10757460 DOI: 10.7759/cureus.49700] [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] [Accepted: 11/30/2023] [Indexed: 01/03/2024] Open
Abstract
Electrical impedance tomography (EIT) is a gradually maturing medical imaging technique that relies on computational algorithms for reconstructing and visualizing internal conductivity distributions within the human body. To provide a comprehensive and objective understanding of the current state and trends in the EIT algorithm research, we conducted bibliometric analysis on a 25-year EIT algorithm research dataset sourced from Web of Science Core Collections. We visualized publication characteristics, collaboration patterns, keywords, and co-cited references. The results indicate a steady increase in annual publications over recent decades. The United States, United Kingdom, China, and South Korea contributed 60% of the articles collaboratively. Keyword analysis unveiled three distinct stages in the evolution of EIT algorithm research: the establishment of fundamental algorithm frameworks, optimization for improved imaging performance, and the development of algorithms for clinical applications. Additionally, there has been a shift in research focus from traditional theories to the incorporation of new methods, such as artificial intelligence. Co-cited references suggest that integrating EIT with other established imaging techniques may emerge as a new trend in EIT algorithm research. In summary, EIT algorithms have been a consistent research focus, with current efforts centered on optimizing algorithms to enhance imaging performance. The emerging research trend involves utilizing more diverse and intersecting algorithms.
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Affiliation(s)
- Zhangjun Tan
- Department of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, CHN
| | - Shiyue Lu
- Department of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, CHN
| | - Lin Yang
- Department of Aerospace Medicine, Fourth Military Medical University, Xi'an, CHN
| | - Yuqing Xu
- Department of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, CHN
| | - Shaojie Qin
- Department of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, CHN
| | - Meng Dai
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, CHN
| | - Zhe Li
- Department of Critical Care Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, CHN
| | - Zhanqi Zhao
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, CHN
- Department of Critical Care Medicine, Peking Union Medical College Hospital, Beijing, CHN
- Department of Technical Medicine, Furtwangen University, Villingen-Schwenningen, DEU
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da Mata AMM, de Moura BF, Martins MF, Palma FHS, Ramos R. Signal-to-noise ratio variance impact on the image reconstruction of electrical resistance tomography in solutions with high background conductivity. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2022; 93:074705. [PMID: 35922304 DOI: 10.1063/5.0088296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 06/28/2022] [Indexed: 06/15/2023]
Abstract
Electrical Resistance Tomography (ERT) has the potentialities of non-intrusive techniques and high temporal resolution which are essential characteristics for multiphase flow measurements. However, high background conductivities, such as saline water in oil extraction, impose a limitation in ERT image reconstruction. Focusing on the operational limits of an ERT tomography system operating in different conductivity backgrounds from 0.010 to 4.584 S/m, the impact on the image reconstruction was assessed via signal-to-noise variance. The signal-to-noise ratio (SNR) variance had a strong correlation (p-value = 5.40 × 10-15) with the image reconstruction quality at the threshold of 30 dB, reaching a correlation value of r = -0.92 in the range of 0.010-0.246 S/m. Regarding the position error of the phantom, p-value = 1.30 × 10-5 and r = -0.66 were attained. The global results revealed that the correlation of the mean of the SNR (p-value = 5 × 10-4 and r = 0.55) was kept unaltered through the whole conductivity range, showing that such a statistical index can induce bias in establishing the operational limits of the hardware.
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Affiliation(s)
- Adriana Machado Malafaia da Mata
- Laboratory for Computational Transport Phenomena (LFTC), Department of Postgraduate Studies in Mechanical Engineering, Universidade Federal do Espírito Santo (UFES), Vitória-ES 29075-910, Brazil
| | - Bruno Furtado de Moura
- Faculty of Engineering, Universidade Federal de Catalão (UFCAT), Catalão-State of Goiás 75705-220, Brazil
| | - Marcio Ferreira Martins
- Laboratory for Computational Transport Phenomena (LFTC), Department of Postgraduate Studies in Mechanical Engineering, Universidade Federal do Espírito Santo (UFES), Vitória-ES 29075-910, Brazil
| | - Francisco Hernán Sepúlveda Palma
- Laboratorio de Metrología Térmica, Department of Mechanical Engineering, Universidad de Santiago de Chile (Usach), 9170022 Región Metropolitana, Chile
| | - Rogério Ramos
- Nucleus for Oil and Gas Flow Measurement (NEMOG), Department of Mechanical Engineering, Universidade Federal Do Espírito Santo (UFES), Vitória-ES 29075-910, Brazil
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Shi Y, Yang Z, Xie F, Ren S, Xu S. The Research Progress of Electrical Impedance Tomography for Lung Monitoring. Front Bioeng Biotechnol 2021; 9:726652. [PMID: 34660553 PMCID: PMC8517404 DOI: 10.3389/fbioe.2021.726652] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 08/09/2021] [Indexed: 01/16/2023] Open
Abstract
Medical imaging can intuitively show people the internal structure, morphological information, and organ functions of the organism, which is one of the most important inspection methods in clinical medical diagnosis. Currently used medical imaging methods can only be applied to some diagnostic occasions after qualitative lesions have been generated, and the general imaging technology is usually accompanied by radiation and other conditions. However, electrical impedance tomography has the advantages of being noninvasive and non-radiative. EIT (Electrical Impedance Tomography) is also widely used in the early diagnosis and treatment of some diseases because of these advantages. At present, EIT is relatively mature and more and more image reconstruction algorithms are used to improve imaging resolution. Hardware technology is also developing rapidly, and the accuracy of data collection and processing is continuously improving. In terms of clinical application, EIT has also been used for pathological treatment of lungs, the brain, and the bladder. In the future, EIT has a good application prospect in the medical field, which can meet the needs of real-time, long-term monitoring and early diagnosis. Aiming at the application of EIT in the treatment of lung pathology, this article reviews the research progress of EIT, image reconstruction algorithms, hardware system design, and clinical applications used in the treatment of lung diseases. Through the research and introduction of several core components of EIT technology, it clarifies the characteristics of EIT system complexity and its solutions, provides research ideas for subsequent research, and once again verifies the broad development prospects of EIT technology in the future.
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Affiliation(s)
- Yan Shi
- The School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
| | - ZhiGuo Yang
- The School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
| | - Fei Xie
- Department of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing, China
| | - Shuai Ren
- The School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, China
| | - ShaoFeng Xu
- The School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
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Koolman PM, Bukshtynov V. A multiscale optimization framework for reconstructing binary images using multilevel PCA-based control space reduction. Biomed Phys Eng Express 2021; 7:025005. [PMID: 33522496 DOI: 10.1088/2057-1976/abd4be] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
An efficient computational approach for optimal reconstructing parameters of binary-type physical properties for models in biomedical applications is developed and validated. The methodology includes gradient-based multiscale optimization with multilevel control space reduction by using principal component analysis (PCA) coupled with dynamical control space upscaling. The reduced dimensional controls are used interchangeably at fine and coarse scales to accumulate the optimization progress and mitigate side effects at both scales. Flexibility is achieved through the proposed procedure for calibrating certain parameters to enhance the performance of the optimization algorithm. Reduced size of control spaces supplied with adjoint-based gradients obtained at both scales facilitate the application of this algorithm to models of higher complexity and also to a broad range of problems in biomedical sciences. This technique is shown to outperform regular gradient-based methods applied to fine scale only in terms of both qualities of binary images and computing time. Performance of the complete computational framework is tested in applications to 2D inverse problems of cancer detection by the electrical impedance tomography (EIT). The results demonstrate the efficient performance of the new method and its high potential for minimizing possibilities for false positive screening and improving the overall quality of the EIT-based procedures.
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Affiliation(s)
- Priscilla M Koolman
- College of Engineering & Science, Florida Institute of Technology, Melbourne, FL 32901, United States of America
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