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.

Atherosclerotic diseases of coronary vessels are the main reasons of myocardial ischemia. The value of the fractional flow reserve (FFR) factor is the golden standard for making decision on coronary network surgical treatment. The FFR measurements require expensive endovascular diagnostics. We propose a noninvasive method of the virtual FFR assessment in patient-specific coronary network based on angiography and computer tomography data. Also we analyze sensitivity of the model to the heart stroke volume.

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In this work we propose a method for analysis of postsurgical haemodynamics after femoral artery treatment of occlusive vascular disease. Patient specific reconstruction algorithm of 1D core network based on MRI data is proposed as a tool for such analysis. Along with presurgical ultrasound data fitting it provides effective personalizing predictive method that is validated with clinical observations.