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Electrical Impedance Tomography-Based Abdominal Subcutaneous Fat Estimation Method Using Deep Learning
Joint Authors
Lee, Kyounghun
Yoo, Minha
Jargal, Ariungerel
Kwon, Hyeuknam
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-11
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
This paper proposes a deep learning method based on electrical impedance tomography (EIT) to estimate the thickness of abdominal subcutaneous fat.
EIT for evaluating the thickness of abdominal subcutaneous fat is an absolute imaging problem that aims at reconstructing conductivity distributions from current-to-voltage data.
Existing reconstruction methods based on EIT have difficulty handling the inherent drawbacks of strong nonlinearity and severe ill-posedness of EIT; hence, absolute imaging may not be possible using linearized methods.
To handle nonlinearity and ill-posedness, we propose a deep learning method that finds useful solutions within a restricted admissible set by accounting for prior information regarding abdominal anatomy.
We determined that a specially designed training dataset used during the deep learning process significantly reduces ill-posedness in the absolute EIT problem.
In the preprocessing stage, we normalize current-voltage data to alleviate the effects of electrodeposition and body geometry by exploiting knowledge regarding electrode positions and body geometry.
The performance of the proposed method is demonstrated through numerical simulations and phantom experiments using a 10 channel EIT system and a human-like domain.
American Psychological Association (APA)
Lee, Kyounghun& Yoo, Minha& Jargal, Ariungerel& Kwon, Hyeuknam. 2020. Electrical Impedance Tomography-Based Abdominal Subcutaneous Fat Estimation Method Using Deep Learning. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1139678
Modern Language Association (MLA)
Lee, Kyounghun…[et al.]. Electrical Impedance Tomography-Based Abdominal Subcutaneous Fat Estimation Method Using Deep Learning. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1139678
American Medical Association (AMA)
Lee, Kyounghun& Yoo, Minha& Jargal, Ariungerel& Kwon, Hyeuknam. Electrical Impedance Tomography-Based Abdominal Subcutaneous Fat Estimation Method Using Deep Learning. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1139678
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139678