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Classification of Polar Maps from Cardiac Perfusion Imaging with Graph-Convolutional Neural Networks
Myocardial perfusion imaging is a non-invasive imaging technique commonly used for the diagnosis of Coronary Artery Disease and is based on the injection of radiopharmaceutical tracers into the blood stream. The patient’s heart is imaged while at rest and under stress in order to determine its capac...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6527613/ https://www.ncbi.nlm.nih.gov/pubmed/31110326 http://dx.doi.org/10.1038/s41598-019-43951-8 |
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author | Spier, Nathalia Nekolla, Stephan Rupprecht, Christian Mustafa, Mona Navab, Nassir Baust, Maximilian |
author_facet | Spier, Nathalia Nekolla, Stephan Rupprecht, Christian Mustafa, Mona Navab, Nassir Baust, Maximilian |
author_sort | Spier, Nathalia |
collection | PubMed |
description | Myocardial perfusion imaging is a non-invasive imaging technique commonly used for the diagnosis of Coronary Artery Disease and is based on the injection of radiopharmaceutical tracers into the blood stream. The patient’s heart is imaged while at rest and under stress in order to determine its capacity to react to the imposed challenge. Assessment of imaging data is commonly performed by visual inspection of polar maps showing the tracer uptake in a compact, two-dimensional representation of the left ventricle. This article presents a method for automatic classification of polar maps based on graph convolutional neural networks. Furthermore, it evaluates how well localization techniques developed for standard convolutional neural networks can be used for the localization of pathological segments with respect to clinically relevant areas. The method is evaluated using 946 labeled datasets and compared quantitatively to three other neural-network-based methods. The proposed model achieves an agreement with the human observer on 89.3% of rest test polar maps and on 91.1% of stress test polar maps. Localization performed on a fine 17-segment division of the polar maps achieves an agreement of 83.1% with the human observer, while localization on a coarse 3-segment division based on the vessel beds of the left ventricle has an agreement of 78.8% with the human observer. Our method could thus assist the decision-making process of physicians when analyzing polar map data obtained from myocardial perfusion images. |
format | Online Article Text |
id | pubmed-6527613 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-65276132019-05-30 Classification of Polar Maps from Cardiac Perfusion Imaging with Graph-Convolutional Neural Networks Spier, Nathalia Nekolla, Stephan Rupprecht, Christian Mustafa, Mona Navab, Nassir Baust, Maximilian Sci Rep Article Myocardial perfusion imaging is a non-invasive imaging technique commonly used for the diagnosis of Coronary Artery Disease and is based on the injection of radiopharmaceutical tracers into the blood stream. The patient’s heart is imaged while at rest and under stress in order to determine its capacity to react to the imposed challenge. Assessment of imaging data is commonly performed by visual inspection of polar maps showing the tracer uptake in a compact, two-dimensional representation of the left ventricle. This article presents a method for automatic classification of polar maps based on graph convolutional neural networks. Furthermore, it evaluates how well localization techniques developed for standard convolutional neural networks can be used for the localization of pathological segments with respect to clinically relevant areas. The method is evaluated using 946 labeled datasets and compared quantitatively to three other neural-network-based methods. The proposed model achieves an agreement with the human observer on 89.3% of rest test polar maps and on 91.1% of stress test polar maps. Localization performed on a fine 17-segment division of the polar maps achieves an agreement of 83.1% with the human observer, while localization on a coarse 3-segment division based on the vessel beds of the left ventricle has an agreement of 78.8% with the human observer. Our method could thus assist the decision-making process of physicians when analyzing polar map data obtained from myocardial perfusion images. Nature Publishing Group UK 2019-05-20 /pmc/articles/PMC6527613/ /pubmed/31110326 http://dx.doi.org/10.1038/s41598-019-43951-8 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Spier, Nathalia Nekolla, Stephan Rupprecht, Christian Mustafa, Mona Navab, Nassir Baust, Maximilian Classification of Polar Maps from Cardiac Perfusion Imaging with Graph-Convolutional Neural Networks |
title | Classification of Polar Maps from Cardiac Perfusion Imaging with Graph-Convolutional Neural Networks |
title_full | Classification of Polar Maps from Cardiac Perfusion Imaging with Graph-Convolutional Neural Networks |
title_fullStr | Classification of Polar Maps from Cardiac Perfusion Imaging with Graph-Convolutional Neural Networks |
title_full_unstemmed | Classification of Polar Maps from Cardiac Perfusion Imaging with Graph-Convolutional Neural Networks |
title_short | Classification of Polar Maps from Cardiac Perfusion Imaging with Graph-Convolutional Neural Networks |
title_sort | classification of polar maps from cardiac perfusion imaging with graph-convolutional neural networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6527613/ https://www.ncbi.nlm.nih.gov/pubmed/31110326 http://dx.doi.org/10.1038/s41598-019-43951-8 |
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