A Mixture Theory Model for Blood Combined With Low-Density Lipoprotein Transport to Predict Early Atherosclerosis Regions in Idealized and Patient-Derived Abdominal Aorta.

Genesis and onset of atherosclerosis are greatly influenced by hemodynamic forces. Two-phase transient computational fluid dynamic (CFD) simulations are performed using a mixture theory model for blood, and a transport equation for low-density lipoprotein (LDL), in idealized and patient-derived abdominal aorta to predict the sites at risk for atherosclerosis. Flow patterns at different time instants and relevant hemodynamic indicators-wall shear stress (WSS)-based (time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and relative residence time (RRT)), and LDL concentration-are used concurrently to predict the susceptible sites of atherosclerosis. In the case of idealized geometry, flow recirculations are observed on the posterior wall opposite the superior mesenteric artery and below the renal bifurcations. Low TAWSS, high OSI, high RRT and high concentration of LDL are observed in these regions. This suggests that in idealized abdominal aorta, the posterior wall proximal to the renal artery junction is more prone to atherosclerosis. This matches qualitatively with the experimental and simulation data in the literature. In the case of patient-derived geometry, flow reversal, low TAWSS, high OSI and high RRT are observed infrarenal on the anterior wall. Further, high concentration of LDL is observed at the same location on the anterior wall suggesting anterior wall distal to the renal artery junction is more prone to atherosclerosis. These findings demonstrate the use of a novel method to predict the sites at risk for atherosclerosis in geometries where complexities like junctions and curvature play a major role.