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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
Author(s) -
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
Publication year - 2019
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.3235
Subject(s) - fractional flow reserve , benchmark (surveying) , pressure drop , population , computational fluid dynamics , stenosis , computer science , mathematics , coronary angiography , radiology , medicine , cardiology , engineering , myocardial infarction , geology , physics , environmental health , geodesy , aerospace engineering , thermodynamics
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.