Jason Carson, Sanjay Pant, Carl Roobottom, Robin Alcock, Pablo Javier Blanco,  Carlos Alberto Bulant, Yuri Vassilevski, Sergey Simakov, Timur Gamilov, Roman Pryamonosov, Fuyou Liang, Xinyang Ge, Yue Liu, Perumal Nithiarasu

International Journal for Numerical Methods in Biomedical Engineering, 2019, 35:e3235

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¬† FFR 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.