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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
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.
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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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