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Electrical Impedance Tomography-Based Abdominal Subcutaneous Fat Estimation Method Using Deep Learning
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305546/ https://www.ncbi.nlm.nih.gov/pubmed/32587631 http://dx.doi.org/10.1155/2020/9657372 |
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author | Lee, Kyounghun Yoo, Minha Jargal, Ariungerel Kwon, Hyeuknam |
author_facet | Lee, Kyounghun Yoo, Minha Jargal, Ariungerel Kwon, Hyeuknam |
author_sort | Lee, Kyounghun |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7305546 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-73055462020-06-24 Electrical Impedance Tomography-Based Abdominal Subcutaneous Fat Estimation Method Using Deep Learning Lee, Kyounghun Yoo, Minha Jargal, Ariungerel Kwon, Hyeuknam Comput Math Methods Med Research Article 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. Hindawi 2020-06-11 /pmc/articles/PMC7305546/ /pubmed/32587631 http://dx.doi.org/10.1155/2020/9657372 Text en Copyright © 2020 Kyounghun Lee et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Lee, Kyounghun Yoo, Minha Jargal, Ariungerel Kwon, Hyeuknam Electrical Impedance Tomography-Based Abdominal Subcutaneous Fat Estimation Method Using Deep Learning |
title | Electrical Impedance Tomography-Based Abdominal Subcutaneous Fat Estimation Method Using Deep Learning |
title_full | Electrical Impedance Tomography-Based Abdominal Subcutaneous Fat Estimation Method Using Deep Learning |
title_fullStr | Electrical Impedance Tomography-Based Abdominal Subcutaneous Fat Estimation Method Using Deep Learning |
title_full_unstemmed | Electrical Impedance Tomography-Based Abdominal Subcutaneous Fat Estimation Method Using Deep Learning |
title_short | Electrical Impedance Tomography-Based Abdominal Subcutaneous Fat Estimation Method Using Deep Learning |
title_sort | electrical impedance tomography-based abdominal subcutaneous fat estimation method using deep learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305546/ https://www.ncbi.nlm.nih.gov/pubmed/32587631 http://dx.doi.org/10.1155/2020/9657372 |
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