| Summary: | Stroke is the third biggest killer and the leading cause of severe disability in the UK. The most common type of stroke is known as an ischaemic stroke in which blood vessels in the brain become blocked, most often as a result of rupture of atherosclerotic plaques formed in the carotid arteries, especially at the bifurcation and in the entrance to the internal carotid artery which feeds the cerebral circulation. The carotid arteries can be examined through ultrasound scans, which provide images of the vessel allowing assessment of the severity of carotid disease. In this project, ultrasound images of carotid arteries acquired from patients with varying degrees of carotid disease are analysed and computational models are built to predict the mechanical stresses within carotid arteries with or without atherosclerosis. Ultrasound imaging was chosen owing to its non-invasive nature and wide availability. Work presented in this thesis follows a logical progression. First, image analysis tools are developed and presented in Chapter 3. Based on the processed ultrasound images, finite element models of carotid arteries are constructed and biomechanical analyses of varying degrees of complexity are carried out. In Chapter 4, the carotid artery is assumed as an elastic material, and comparisons are made between 2-D and 3-D models based on idealised geometry. In an attempt to improve the predictive ability of the model and achieve better understanding of disease progression, the viscoelastic behaviour of the carotid vessel wall is taken into account, and detailed analyses of the effect of vessel wall viscosity and hysteresis are presented in Chapter 5. Methods used to derive subject-specific viscoelastic material properties from in vivo pressure-diameter data are also described. Finally, in order to understand the effect of interaction between the viscoelastic vessel wall and blood flow in carotid arteries, fully coupled fluid-structure interaction (FSI) models are developed, and the FSI model as well as its validation are presented in Chapter 6. Work reported in this thesis has shown that by combining ultrasound measurement with computational analysis, it is possible to provide additional information that will aid clinicians in their diagnosis and decision making. Carotid biomechanical analysis can be carried out with subject-specific information, such as viscoelastic wall properties and geometry, derived from in vivo data acquired non-invasively. The ultrasound-image based modelling approach developed in this thesis will also help improve our understanding of the role of vessel wall motion in the development and progression of carotid diseases.
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