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Bridging the gap between measurements and modelling: a cardiovascular functional avatar
Lumped parameter models of the cardiovascular system have the potential to assist researchers and clinicians to better understand cardiovascular function. The value of such models increases when they are subject specific. However, most approaches to personalize lumped parameter models have thus far...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5524911/ https://www.ncbi.nlm.nih.gov/pubmed/28740184 http://dx.doi.org/10.1038/s41598-017-06339-0 |
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author | Casas, Belén Lantz, Jonas Viola, Federica Cedersund, Gunnar Bolger, Ann F. Carlhäll, Carl-Johan Karlsson, Matts Ebbers, Tino |
author_facet | Casas, Belén Lantz, Jonas Viola, Federica Cedersund, Gunnar Bolger, Ann F. Carlhäll, Carl-Johan Karlsson, Matts Ebbers, Tino |
author_sort | Casas, Belén |
collection | PubMed |
description | Lumped parameter models of the cardiovascular system have the potential to assist researchers and clinicians to better understand cardiovascular function. The value of such models increases when they are subject specific. However, most approaches to personalize lumped parameter models have thus far required invasive measurements or fall short of being subject specific due to a lack of the necessary clinical data. Here, we propose an approach to personalize parameters in a model of the heart and the systemic circulation using exclusively non-invasive measurements. The personalized model is created using flow data from four-dimensional magnetic resonance imaging and cuff pressure measurements in the brachial artery. We term this personalized model the cardiovascular avatar. In our proof-of-concept study, we evaluated the capability of the avatar to reproduce pressures and flows in a group of eight healthy subjects. Both quantitatively and qualitatively, the model-based results agreed well with the pressure and flow measurements obtained in vivo for each subject. This non-invasive and personalized approach can synthesize medical data into clinically relevant indicators of cardiovascular function, and estimate hemodynamic variables that cannot be assessed directly from clinical measurements. |
format | Online Article Text |
id | pubmed-5524911 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-55249112017-07-26 Bridging the gap between measurements and modelling: a cardiovascular functional avatar Casas, Belén Lantz, Jonas Viola, Federica Cedersund, Gunnar Bolger, Ann F. Carlhäll, Carl-Johan Karlsson, Matts Ebbers, Tino Sci Rep Article Lumped parameter models of the cardiovascular system have the potential to assist researchers and clinicians to better understand cardiovascular function. The value of such models increases when they are subject specific. However, most approaches to personalize lumped parameter models have thus far required invasive measurements or fall short of being subject specific due to a lack of the necessary clinical data. Here, we propose an approach to personalize parameters in a model of the heart and the systemic circulation using exclusively non-invasive measurements. The personalized model is created using flow data from four-dimensional magnetic resonance imaging and cuff pressure measurements in the brachial artery. We term this personalized model the cardiovascular avatar. In our proof-of-concept study, we evaluated the capability of the avatar to reproduce pressures and flows in a group of eight healthy subjects. Both quantitatively and qualitatively, the model-based results agreed well with the pressure and flow measurements obtained in vivo for each subject. This non-invasive and personalized approach can synthesize medical data into clinically relevant indicators of cardiovascular function, and estimate hemodynamic variables that cannot be assessed directly from clinical measurements. Nature Publishing Group UK 2017-07-24 /pmc/articles/PMC5524911/ /pubmed/28740184 http://dx.doi.org/10.1038/s41598-017-06339-0 Text en © The Author(s) 2017 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 Casas, Belén Lantz, Jonas Viola, Federica Cedersund, Gunnar Bolger, Ann F. Carlhäll, Carl-Johan Karlsson, Matts Ebbers, Tino Bridging the gap between measurements and modelling: a cardiovascular functional avatar |
title | Bridging the gap between measurements and modelling: a cardiovascular functional avatar |
title_full | Bridging the gap between measurements and modelling: a cardiovascular functional avatar |
title_fullStr | Bridging the gap between measurements and modelling: a cardiovascular functional avatar |
title_full_unstemmed | Bridging the gap between measurements and modelling: a cardiovascular functional avatar |
title_short | Bridging the gap between measurements and modelling: a cardiovascular functional avatar |
title_sort | bridging the gap between measurements and modelling: a cardiovascular functional avatar |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5524911/ https://www.ncbi.nlm.nih.gov/pubmed/28740184 http://dx.doi.org/10.1038/s41598-017-06339-0 |
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