Advanced Modelling Approach of Carotid Artery Atherosclerosis

Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 11)


With a fast progression of computational methods and medical imaging techniques, the advanced simulations of carotid arteries can be approached aiming to address different medical conditions and support the clinical practice. Within this context, the main purpose of this study was to computationally model the biological and mechanical processes related to the plaque progression, as well as to predict plaque regions and mechanisms which are prone to atherosclerosis development within the carotid artery. We have focused on two patient-specific models and application of Finite Element Analysis (FEA) which together enable investigation of the parameter such as shear stress distribution, as well as mechanical response of stenotic zones. After performed the three-dimensional (3D) simulation of plaque progression, the results have shown stenoses in Internal Carotid Artery (ICA), in case of both patients. The degree of ICA stenosis is the most important predictor of cerebral infarction among patients with atherosclerosis. Therefore, its estimation is significant for further steps in medical treatment. The increased shear stress was present at the stenoses due to high blood velocities, while low shear stress was present at the carotid bifurcation, which may indicate the possibility for further plaque progression. This approach will be further improved and used for risk stratification models, by detecting the parameters of unstable and stable carotid plaques related to the risk of stroke, which is objective of our future studies.



This paper is supported by TAXINOMISIS project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 755320. 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 is also supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia (project numbers III41007 and OI174028).


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of EngineeringUniversity of Kragujevac (FINK)KragujevacSerbia
  2. 2.Bioengineering Research and Development Center (BioIRC)KragujevacSerbia

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