Abstract

The aim of this study was to elucidate the mechanisms and underlying biomechanical factors that may play a role in the risk of rupture of vulnerable plaques (VPs) by studying patient-based geometries of coronary arteries reconstructed from intravascular ultrasound (IVUS) imaging utilizing fluid-structure interaction (FSI) numerical simulations. According to recent estimates, coronary artery disease is responsible for one in six deaths in the USA, and causes about one million heart attacks each year. Among these, the rupture of coronary VPs followed by luminal blockage is widely recognized as a major cause of sudden heart attacks; most importantly, the patients may appear as asymptomatic under routine screening before the occurrence of the index event. FSI simulations of patient-based geometries of coronary arteries reconstructed from IVUS imaging were performed to establish the dependence of the risk of rupture of coronary VP on biomechanical factors, such as the fibrous cap thickness, presence of microcalcification in the fibrous cap, arterial anisotropy, and hypertension. Parametric FSI simulations indicated that mechanical stresses (von Mises stresses) increase exponentially with the thinning of the fibrous cap as well as with increasing levels of hypertension. The inclusion of a microcalcification in the fibrous cap considerably increases the risk of rupture of VP , with an ∼two-fold stress increase in the VP stress burden. Furthermore, the stress-driven reorientation and biochemical degradation of the collagen fibers in the vessel wall because of atherosclerosis (studied with an anisotropic fibrous cap 65° fiber reorientation angle) results in a 30% increase in the stress levels as compared with simulations with isotropic material models, clearly indicating that the latter, which are commonly used in such studies, underestimate the risk of rupture of VP. The results indicate that IVUS-based patient-specific FSI simulations for mapping the wall stresses, followed by analysis of the biomechanical risk factors, may be used as an additional diagnostic tool for clinicians to estimate the plaque burden and determine the proper treatment and intervention.

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