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Non‐invasive coronary CT angiography‐derived fractional flow reserve: A benchmark study comparing the diagnostic performance of four different computational methodologies
Non‐invasive coronary computed tomography (CT) angiography‐derived fractional flow reserve (cFFR) is an emergent approach to determine the functional relevance of obstructive coronary lesions. Its feasibility and diagnostic performance has been reported in several studies. It is unclear if differenc...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6851543/ https://www.ncbi.nlm.nih.gov/pubmed/31315158 http://dx.doi.org/10.1002/cnm.3235 |
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author | Carson, Jason Matthew Pant, Sanjay Roobottom, Carl Alcock, Robin Javier Blanco, Pablo Alberto Bulant, Carlos Vassilevski, Yuri Simakov, Sergey Gamilov, Timur Pryamonosov, Roman Liang, Fuyou Ge, Xinyang Liu, Yue Nithiarasu, Perumal |
author_facet | Carson, Jason Matthew Pant, Sanjay Roobottom, Carl Alcock, Robin Javier Blanco, Pablo Alberto Bulant, Carlos Vassilevski, Yuri Simakov, Sergey Gamilov, Timur Pryamonosov, Roman Liang, Fuyou Ge, Xinyang Liu, Yue Nithiarasu, Perumal |
author_sort | Carson, Jason Matthew |
collection | PubMed |
description | Non‐invasive coronary computed tomography (CT) angiography‐derived fractional flow reserve (cFFR) is an emergent approach to determine the functional relevance of obstructive coronary lesions. Its feasibility and diagnostic performance has been reported in several studies. It is unclear if differences in sensitivity and specificity between these studies are due to study design, population, or "computational methodology." We evaluate the diagnostic performance of four different computational workflows for the prediction of cFFR using a limited data set of 10 patients, three based on reduced‐order modelling and one based on a 3D rigid‐wall model. The results for three of these methodologies yield similar accuracy of 6.5% to 10.5% mean absolute difference between computed and measured FFR. The main aspects of modelling which affected cFFR estimation were choice of inlet and outlet boundary conditions and estimation of flow distribution in the coronary network. One of the reduced‐order models showed the lowest overall deviation from the clinical FFR measurements, indicating that reduced‐order models are capable of a similar level of accuracy to a 3D model. In addition, this reduced‐order model did not include a lumped pressure‐drop model for a stenosis, which implies that the additional effort of isolating a stenosis and inserting a pressure‐drop element in the spatial mesh may not be required for FFR estimation. The present benchmark study is the first of this kind, in which we attempt to homogenize the data required to compute FFR using mathematical models. The clinical data utilised in the cFFR workflows are made publicly available online. |
format | Online Article Text |
id | pubmed-6851543 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-68515432019-11-18 Non‐invasive coronary CT angiography‐derived fractional flow reserve: A benchmark study comparing the diagnostic performance of four different computational methodologies Carson, Jason Matthew Pant, Sanjay Roobottom, Carl Alcock, Robin Javier Blanco, Pablo Alberto Bulant, Carlos Vassilevski, Yuri Simakov, Sergey Gamilov, Timur Pryamonosov, Roman Liang, Fuyou Ge, Xinyang Liu, Yue Nithiarasu, Perumal Int J Numer Method Biomed Eng Research Article ‐ Applications Non‐invasive coronary computed tomography (CT) angiography‐derived fractional flow reserve (cFFR) is an emergent approach to determine the functional relevance of obstructive coronary lesions. Its feasibility and diagnostic performance has been reported in several studies. It is unclear if differences in sensitivity and specificity between these studies are due to study design, population, or "computational methodology." We evaluate the diagnostic performance of four different computational workflows for the prediction of cFFR using a limited data set of 10 patients, three based on reduced‐order modelling and one based on a 3D rigid‐wall model. The results for three of these methodologies yield similar accuracy of 6.5% to 10.5% mean absolute difference between computed and measured FFR. The main aspects of modelling which affected cFFR estimation were choice of inlet and outlet boundary conditions and estimation of flow distribution in the coronary network. One of the reduced‐order models showed the lowest overall deviation from the clinical FFR measurements, indicating that reduced‐order models are capable of a similar level of accuracy to a 3D model. In addition, this reduced‐order model did not include a lumped pressure‐drop model for a stenosis, which implies that the additional effort of isolating a stenosis and inserting a pressure‐drop element in the spatial mesh may not be required for FFR estimation. The present benchmark study is the first of this kind, in which we attempt to homogenize the data required to compute FFR using mathematical models. The clinical data utilised in the cFFR workflows are made publicly available online. John Wiley and Sons Inc. 2019-08-16 2019-10 /pmc/articles/PMC6851543/ /pubmed/31315158 http://dx.doi.org/10.1002/cnm.3235 Text en © 2019 The Authors International Journal for Numerical Methods in Biomedical Engineering Published by John Wiley & Sons Ltd This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article ‐ Applications Carson, Jason Matthew Pant, Sanjay Roobottom, Carl Alcock, Robin Javier Blanco, Pablo Alberto Bulant, Carlos Vassilevski, Yuri Simakov, Sergey Gamilov, Timur Pryamonosov, Roman Liang, Fuyou Ge, Xinyang Liu, Yue Nithiarasu, Perumal Non‐invasive coronary CT angiography‐derived fractional flow reserve: A benchmark study comparing the diagnostic performance of four different computational methodologies |
title | Non‐invasive coronary CT angiography‐derived fractional flow reserve: A benchmark study comparing the diagnostic performance of four different computational methodologies |
title_full | Non‐invasive coronary CT angiography‐derived fractional flow reserve: A benchmark study comparing the diagnostic performance of four different computational methodologies |
title_fullStr | Non‐invasive coronary CT angiography‐derived fractional flow reserve: A benchmark study comparing the diagnostic performance of four different computational methodologies |
title_full_unstemmed | Non‐invasive coronary CT angiography‐derived fractional flow reserve: A benchmark study comparing the diagnostic performance of four different computational methodologies |
title_short | Non‐invasive coronary CT angiography‐derived fractional flow reserve: A benchmark study comparing the diagnostic performance of four different computational methodologies |
title_sort | non‐invasive coronary ct angiography‐derived fractional flow reserve: a benchmark study comparing the diagnostic performance of four different computational methodologies |
topic | Research Article ‐ Applications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6851543/ https://www.ncbi.nlm.nih.gov/pubmed/31315158 http://dx.doi.org/10.1002/cnm.3235 |
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