Mechanical effects on the atheroma plaque appearance, growth and vulnerability

  1. Cilla Hernández, Myriam
Supervised by:
  1. Estefanía Peña Baquedano Director
  2. Miguel Angel Martínez Barca Director

Defence university: Universidad de Zaragoza

Fecha de defensa: 15 February 2013

  1. Víctor Alastrue Vera Chair
  2. Mauro Malvè Secretary
  3. Francisco Javier Martínez Torres Committee member
  4. Daniel Kelly Committee member
  5. Gemma Pascual González Committee member

Type: Thesis

Teseo: 337730 DIALNET


In western countries, cardiovascular disease is the most common cause of death, often related to atherosclerosis which can cause narrowing, rupture or erosion of the arterial wall, and eventually reduction or complete blockage of the blood flow. Nowadays, imaging modalities such as Magnetic Resonance Imaging (MRI) or Intravascular ultrasound (IVUS) allow improving the atherosclerosis diagnosis. However, in the recent years, computational techniques, which allow approaching this disease from a mechanical standpoint, have been emerged as an alternative and/or complementary diagnosis techniques, improving the understanding of the cardiovascular pathologies. This Thesis deals with the study of the role of some mechanical factors on atherosclerotic blood vessels within the continuum mechanics framework. In order to achieve this goal, the atherosclerosis disease has been tackled from two different perspectives; from computational and experimental points of view. The Finite Element (FE) method is intensively used throughout this work in order to improve the understanding of the problems at hand. Furthermore, the feasibility of the proposed methodologies as predictive tools for clinicians, which should be one of the most important aims of Computational Biomechanics, is also shown. Regarding computational aspects, this Thesis presents a computational methodology, able to accurately analyze the mechanical environment of atherosclerotic lesions and consequently identify high risk plaques. This is accomplished by means of several finite element idealized parametric studies which predict the influence of the main mechanical and structural aspects of the atheroma plaque vulnerability; geometric risk factors such as the fibrous cap thickness, the stenosis ratio, the lipid core length and width, the remodeling index and the plaque configuration, longitudinal and circumferential residual stresses and presence of microcalcifications. The idealized geometry used in these parametric studies has been validated with a patient specific reconstruction model of human atherosclerotic lesion. Furthermore, considering the important role that the arterial wall compliance and pulsatile blood flow play in the atheroma growth and, in order to reject the effect of fluid shear stress compared to the effect of tensile wall stresses on plaque fracture dynamics, a Fluid Structure Interaction (FSI) model based in this parametric study is presented. The main drawbacks of specific patient finite element analysis to predict the atheroma plaque vulnerability risk are the huge memory required and the long computation times. Therefore, faster and more efficient methods to detect vulnerable atherosclerotic plaque would greatly enhance the ability of clinicians to diagnose and treat patients at risk. This Thesis presents two potential applications of Machine Learning Techniques (MLT), such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), applied to the detection of vulnerable plaques. The experimental study of the anatomical and histological characteristics and the mechanical properties of both healthy and atherosclerotic murine aortas is also tackled. For this purpose, in situ inflation tests, histological analysis and a non-invasive evaluation of atherosclerosis lesions were carried out on Apolipoprotein E-Deficient (ApoE-/-) and C57BL/6J mice feeding on a hyperlipidic and a normal diet, respectively. Very different pressure stretch curves were obtained and analyzed, pointing out the different behaviour of both groups. Finally, focusing on atheroma plaque growth aspects, a numerical model for atheroma plaque growth including the main biological agents and processes is presented. This model based on reaction-convection-diffusion equations provides us a better understanding of the biological and mechanical interaction processes. Summarizing, these phenomena are as follows; the inflammatory process starts with the penetration of Low Density Lipoproteins (LDL) cholesterol from the circulatory system into the arterial wall, promoted by the effects of local Wall Shear Stress (WSS) on the endothelial cell layer and its effects on volume and solute flux. Part of this LDL is oxidized and becomes pathological. In order to remove it, circulating immune cells (monocytes) are recruited. Once in the intima, the monocytes differentiate into macrophages that ingest by phagocytosis the oxidized LDL. The ingestion of large amounts of oxidized LDL transforms the fatty macrophages into foam cells. Moreover, an increase of macrophage concentration in the intima leads to the production of pro-inflammatory cytokines, which contribute to recruit more macrophages and induce the migration and proliferation of smooth muscle cells from the media to the intima layer. Finally, the smooth muscle cells (SMCs) may secrete a complex extracellular matrix containing collagen. Foam cells, SMCs and collagen are consider responsible for the growth of a subendothelial plaque which eventually emerges in the artery wall.