On outflow boundary conditions for CT-based computation of FFR: Examination using PET images

Abstract

CT-based computations of fractional flow reserve (FFR) have been widely utilized for evaluating functional severity of a coronary artery stenosis. Whilst this approach has been successful clinically, assumptions involved in the analysis still need to be investigated for further improvement of predictive accuracy. To better understand the sensitivity of computational FFRs on outflow boundary condition-typically reflecting patient's own physiology only through anatomical features-FFR computations for 10 patients with different degree of stenosis was conducted. The computations were based on 3D anatomical model reconstructed from CT images and patient-specific in/outflow boundary conditions (BC). Two outflow BCs were considered: (1) conventional morphology-based and (2) PET perfusion-based conditions. The results showed that the FFRs derived from the two boundary conditions agree in general. It was also found that the FFRs computed with the morphology-based BC tend to estimate higher functional severity, especially in patients with reduced vasodilatory response under hyperaemia-an essential physiological condition in FFR measurement. Further investigation was made by varying hyperaemic resistances (30%-90% of the baseline) in the morphology-based BC. The variation of FFR for the varied resistances was narrow for patients with mild stenosis and wider for those who have severe stenosis. This latter approach confirmed that variability of FFR due to outflow condition tends to come from overestimation of vasodilatory response, especially those who have abnormal myocardial perfusion. The results suggest that outflow conditions that are more representative of each patient could be an effective way to improve CT-based FFR computation.

Analysis identifying minimal governing parameters for clinically accurate in silico fractional flow reserve

“…Third, the effect of adenosine was considered generalizable across the cohort. Lo et al ( 83 ) recently compared patient-specific outflow conditions based on myocardial perfusion from positron emission tomography data to the conventional scaling method. They found that the effect of adenosine could be overestimated and result in overestimating FFR severity.…”

“…The FFR vs. FFR slope exceeded unity, which could indicate that our model overestimated hyperemia because of using scaling laws. This could be rectified in future works by acquiring myocardial perfusion data and tuning hyperemia on a per-patient level ( 83 , 84 ). Fourth, the factorial study subdividing patient-specificity by anatomic and hemodynamic classes was limited.…”

Fractional-Order Windkessel Boundary Conditions in a One-Dimensional Blood Flow Model for Fractional Flow Reserve (FFR) Estimation

“…Hyperemia is simulated by decreasing terminal resistances R k by 70% [28]. FFR values below 0.8 are considered to be significant.…”

Computational Analysis of Haemodynamic Indices in Synthetic Atherosclerotic Coronary Netwroks

“…Vasodilatation is simulated with the same model with reduced terminal resistances R k of coronary arteries for the boundary condition (13). We reduce resistances by 70% [11,35].…”