Why the study?
Can a 3D computational model incorporating hemodynamic and biological factors predict atherosclerotic plaque progression compared to follow-up CT?
Can a 3D computational model incorporating hemodynamic and biological factors predict atherosclerotic plaque progression compared to follow-up CT?
A novel 3D computational model incorporating both hemodynamic and biological factors shows promise in predicting atherosclerotic plaque progression based on CT imaging.
Hypothesis-generating for computational plaque prediction; leaves open validation and clinical utility in larger cohorts.
In this study, we present an innovative model for plaque growth utilizing a 3-Dimensional (3D) left coronary arterial tree reconstructed from computed tomographic (CT) data. The proposed model takes into consideration not only the effect of the local hemodynamic factors but also major biological processes such as the low density lipoprotein (LDL) and high density lipoprotein (HDL) transport, the macrophages recruitment and the foam cells formation. The endothelial membrane is considered semi-permeable and endothelial shear stress dependent, while its permeability is modeled using the Kedem-Katscalsky equations. Patient specific biological data are used for the accurate modeling of plaque formation process. The finite element method (FEM) is employed for the solution of the system of partial differential equations. The results of the simulation are compared to the plaque progression in a follow-up CT examination performed three years after the initial investigation. The results show that the proposed model can be used to predict regions prone for plaque development of progression.
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Sakellarios et al. (2013) studied this question.
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