Geometry-based pressure drop prediction in mildly diseased human coronary arteries.

Abstract

Pressure drop (△p) estimations in human coronary arteries have several important applications, including determination of appropriate boundary conditions for CFD and estimation of fractional flow reserve (FFR). In this study a △p prediction was made based on geometrical features derived from patient-specific imaging data. Twenty-two mildly diseased human coronary arteries were imaged with computed tomography and intravascular ultrasound. Each artery was modelled in three consecutive steps: from straight to tapered, to stenosed, to curved model. CFD was performed to compute the additional △p in each model under steady flow for a wide range of Reynolds numbers. The correlations between the added geometrical complexity and additional △p were used to compute a predicted △p. This predicted △p based on geometry was compared to CFD results. The mean △p calculated with CFD was 855±666Pa. Tapering and curvature added significantly to the total △p, accounting for 31.4±19.0% and 18.0±10.9% respectively at Re=250. Using tapering angle, maximum area stenosis and angularity of the centerline, we were able to generate a good estimate for the predicted △p with a low mean but high standard deviation: average error of 41.1±287.8Pa at Re=250. Furthermore, the predicted △p was used to accurately estimate FFR (r=0.93). The effect of the geometric features was determined and the pressure drop in mildly diseased human coronary arteries was predicted quickly based solely on geometry. This pressure drop estimation could serve as a boundary condition in CFD to model the impact of distal epicardial vessels.