Emerging trends and hot spots on electrical impedance tomography extrapulmonary applications

Xu, Yuqing; Yang, Lin; Lu, Shiyue; Qin, Shaojie; Tang, Ri; Zhao, Zhanqi; Deng, Yuxiao; Li, Zhe

doi:10.1016/j.heliyon.2022.e12458

Cited by 2 publications

(3 citation statements)

References 49 publications

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“…Therefore, specific DL schemes should be utilized on particular applications, according to their strengths. For example, the fd-EIT based MMV-ADMM-Net [218] and MSFCF-Net [220] frameworks can be applied in ex-vivo cases (biopsies for malignant tissue detection [239]) or material sample investigation. The dense-net (VD-Net and V 2 A-Net) and Beltrami-Net frameworks can be transferred to thoracic impedance imaging applications since they demonstrate sufficient performance in boundary shape preservation.…”

Section: Discussionmentioning

confidence: 99%

Advances in Electrical Impedance Tomography Inverse Problem Solution Methods: From Traditional Regularization to Deep Learning

Dimas,

Alimisis,

Uzunoglu

et al. 2024

IEEE Access

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Electrical Impedance Tomography (EIT) has emerged as a valuable medical imaging modality, which visualizes the conductivity distribution of a subject by performing multi-electrode impedance measurements. EIT finds applications in monitoring lung and cardiac function, brain imaging and the detection of malignant tissues. Its mobility, outstanding temporal resolution and the absence of ionizing radiation make it particularly suitable for repetitive real-time monitoring and diagnostics, especially in radiation-sensitive populations, such as neonates. This paper presents a methodological review of EIT image reconstruction approaches spanning from traditional linear regularization and back-projection to more recent techniques, including deep learning, sparse Bayesian learning and non-linear shape-driven reconstruction. Linear and non-linear reconstruction approaches are distinguished, as well as time, frequency difference and absolute reconstruction ones. The exposition includes a concise elaboration of the methodologies' mathematical foundations and algorithmic deployment, with particular attention to recent advancements. For each approach, an assessment of its merits and drawbacks is given, providing implementation considerations, imaging performance and relevant applications.

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Section: Discussionmentioning

confidence: 99%

Advances in Electrical Impedance Tomography Inverse Problem Solution Methods: From Traditional Regularization to Deep Learning

Dimas,

Alimisis,

Uzunoglu

et al. 2024

IEEE Access

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“…Electrical impedance tomography (EIT) is a bioimaging technique that relies on measuring electrical impedance through body-surface electrodes. Medical EIT has been used in various clinical and experimental settings, including pulmonary, brain, and tissue monitoring [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

“…The evaluation includes productivity, international cooperation, identification of hot spots, and emerging trends. Several studies have utilized bibliometric analysis to construct a research map of EIT clinical applications and hardware [3,[8][9][10]. However, a specific bibliometric analysis of the topic of the EIT algorithm has not yet been conducted.…”

Section: Introductionmentioning

confidence: 99%

Research Trends and Hotspots of Medical Electrical Impedance Tomography Algorithms: A Bibliometric Analysis From 1987 to 2021

Tan,

Lu,

Yang

et al. 2023

Cureus

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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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Emerging trends and hot spots on electrical impedance tomography extrapulmonary applications

Cited by 2 publications

References 49 publications

Advances in Electrical Impedance Tomography Inverse Problem Solution Methods: From Traditional Regularization to Deep Learning

Advances in Electrical Impedance Tomography Inverse Problem Solution Methods: From Traditional Regularization to Deep Learning

Research Trends and Hotspots of Medical Electrical Impedance Tomography Algorithms: A Bibliometric Analysis From 1987 to 2021

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