3D Reconstruction and Simulation of Atherosclerosis Progression within the Patient-Specific Arterial Models
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Computational modelling of atherosclerosis has been performed using atherosclerosis mechanisms to provide more detailed understanding of processes which lead to plaque initiation and progression. The simulation of time-dependent plaque progression includes coupled 3D reconstruction of patient-specific carotid arteries [1] and PAK-Athero software based on finite element analysis (FEA) [2]. The program integrates mass transport equations to model the distribution of Low-Density Lipoprotein (LDL), oxidized LDL particles, cytokines, monocytes, and macrophages within the bloodstream. To comprehensively model the atherosclerotic process on cellular level, the software also includes agent-based modeling (ABM) to simulate the behavior and interactions of cells [3]. This approach was under evaluation in the international multicenter TAXINOMISIS clinical study [4], involving longitudinal data collection (baseline examination and three follow-ups) among 345 patients with moderate to severe carotid stenosis (stenosis degree >50%) with total duration of two years. The 3D patient-specific models of carotid arteries are created using the imaging data of those patients. Dynamic evolution of atherosclerotic plaque under different patient-specific geometries and conditions is quantified with the velocity, shear stress distribution and LDL concentration. The resulted values correlate with the zones of plaque formation and progression which is confirmed by comparing the formed plaque with clinical data in different time steps. This study presents the advanced computational model for atherosclerotic plaque progression in carotid arteries using mathematical models and simulations which enable the accurate prediction of disease progression and better risk assessment. The additional advantage is 3D reconstruction of carotid artery for each patient which allows more realistic, patient-specific evaluation of atherosclerotic plaque positioning and progression. Acknowledgments This research is supported by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 755320 – TAXINOMISIS. This article reflects only the author's view. The Commission is not responsible for any use that may be made of the information it contains. The research was also funded by Serbian Ministry of Education, Science, and Technological Development, grants 451-03-47/2023-01/200378 and 451-03-47/2023-01/200107.
