Simulation of atherosclerotic plaque growth using computational biomechanics and patient-specific data
| Autor: | Antonis I. Sakellarios, Dimitrios I. Fotiadis, Silvia Rocchiccioli, Panagiota Tsompou, Danilo Neglia, Juhani Knuuti, Gualtiero Pelosi, Vassiliki I. Kigka, Lampros K. Michalis, Savvas Kyriakidis, Dimitrios S. Pleouras |
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| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
0206 medical engineering lcsh:Medicine Predictive capability Plaque growth Coronary Artery Disease 02 engineering and technology 030204 cardiovascular system & hematology Computational biomechanics Article Nitric oxide 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine Text mining Internal medicine Humans Medicine lcsh:Science Multidisciplinary business.industry lcsh:R Computational Biology Patient specific 020601 biomedical engineering Plaque Atherosclerotic Biomechanical Phenomena 3. Good health Lipoproteins LDL Coronary arteries Cardiovascular diseases medicine.anatomical_structure chemistry Serial imaging Disease Progression Cardiology lcsh:Q Lipoproteins HDL business Biomedical engineering |
| Zdroj: | Scientific Reports Scientific Reports, Vol 10, Iss 1, Pp 1-14 (2020) |
| ISSN: | 2045-2322 |
| Popis: | Atherosclerosis is the one of the major causes of mortality worldwide, urging the need for prevention strategies. In this work, a novel computational model is developed, which is used for simulation of plaque growth to 94 realistic 3D reconstructed coronary arteries. This model considers several factors of the atherosclerotic process even mechanical factors such as the effect of endothelial shear stress, responsible for the initiation of atherosclerosis, and biological factors such as the accumulation of low and high density lipoproteins (LDL and HDL), monocytes, macrophages, cytokines, nitric oxide and formation of foams cells or proliferation of contractile and synthetic smooth muscle cells (SMCs). The model is validated using the serial imaging of CTCA comparing the simulated geometries with the real follow-up arteries. Additionally, we examine the predictive capability of the model to identify regions prone of disease progression. The results presented good correlation between the simulated lumen area (P |
| Databáze: | OpenAIRE |
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