Predicting Atherosclerotic Plaque Onset and Growth in Carotid Arteries: A CFD-Driven Approach

We present a computational platform designed to forecast the onset and progression of atherosclerotic plaque in carotid arteries. Our approach involves integrating Computational Fluid Dynamics (CFD) simulations with in-vivo data and employing two distinct plaque growth models. The first is the Wall Shear Stress (WSS) model, which correlates the thickening of the innermost intimal layer of the arterial vessel with the CFD-computed maximum component of the WSS through a linear algebraic relation. The second is the Low-Density Lipoprotein (LDL) model, considering the initial phases of the inflammation process, where ordinary differential equations describe plaque growth in relation to the magnitude of the WSS and LDL concentration in the intima. In both cases, we account for the thickening of the intima normal to the wall toward the arterial lumen using a morphing procedure. Our findings reveal that steady CFD simulations yield comparable averaged-WSS values in magnitude to unsteady simulations, allowing for the coupling of plaque growth models with steady simulations and thus reducing computational costs. Through a comparison of numerical predictions with in-vivo data, we highlight the need for a modification in the WSS-based plaque growth model to achieve adequate results. We discovered that both the modified WSS model and the LDL model accurately predict the onset region of the disease. Moreover, the LDL model proves to be more effective in estimating the plaque growth rate during the early stages of the pathology.