Review on electrical impedance tomography: artificial intelligence methods and its applications
type: Item
instance of: scholarly article
title: Review on electrical impedance tomography: artificial intelligence methods and its applications
zbMATH Open document ID: 1461.94011
author: Talha Ali Khan
author: Sai Ho Ling
published in: Algorithms
publication date: 2020-10-07T00:00:00Z
review text: Summary: Electrical impedance tomography (EIT) has been a hot topic among researchers for the last 30 years. It is a new imaging method and has evolved over the last few decades. By injecting a small amount of current, the electrical properties of tissues are determined and measurements of the resulting voltages are taken. By using a reconstructing algorithm these voltages then transformed into a tomographic image. EIT contains no identified threats and as compared to magnetic resonance imaging (MRI) and computed tomography (CT) scans (imaging techniques), it is cheaper in cost as well. In this paper, a comprehensive review of efforts and advancements undertaken and achieved in recent work to improve this technology and the role of artificial intelligence to solve this non-linear, ill-posed problem are presented. In addition, a review of EIT clinical based applications has also been presented.
Mathematics Subject Classification ID: 94A08
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68U10
Mathematics Subject Classification ID: 92C55
Mathematics Subject Classification ID: 78M15
zbMATH DE Number: 7257154
zbMATH Keywords: electrical impedance tomography
zbMATH Keywords: artificial intelligence methods
zbMATH Keywords: health care
zbMATH Keywords: imaging technique
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/a12050088
OpenAlex ID: W2941524667
cites work: Q5743700
cites work: Principal component analysis and artificial neural network approach to electrical impedance tomography problems approximated by multi-region boundary element method
cites work: Dual imaging modality of granular flow based on ECT sensors
cites work: Q4500251
Wikidata QID: Q127996746
DOI: 10.3390/A12050088
description: scientific article; zbMATH DE number 7257154