Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.529047
Title: Carotid plaque stress analysis by fluid structure interaction based on in-vivo MRI : implications to plaque vulnerability assessment
Author: Gao, Hao
Awarding Body: Brunel University
Current Institution: Brunel University
Date of Award: 2010
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Abstract:
Stroke is one of the leading causes of death in the world, resulting mostly from the sudden rupture of atherosclerotic plaques. From a biomechanical view, plaque rupture can be considered as a mechanical failure caused by extremely high plaque stress. In this PhD project, we are aiming to predict 3D plaque stress based on in-vivo MRI by using fluid structure interaction (FSI) method, and provide information for plaque rupture risk assessment. Fluid structure interaction was implemented with ANSYS 11.0, followed by a parameter study on fibrous cap thickness and lipid core size with realistic carotid plaque geometry. Twenty patients with carotid plaques imaged by in-vivo MRI were provided in the project. A framework of reconstructing 3D plaque geometry from in-vivo multispectral MRI was designed. The followed reproducibility study on plaque geometry reconstruction procedure and its effect on plaque stress analysis filled the gap in the literature on imaging based plaque stress modeling. The results demonstrated that current MRI technology can provide sufficient information for plaque structure characterization; however stress analysis result is highly affected by MRI resolution and quality. The application of FSI stress analysis to 4 patients with different plaque burdens has showed that the whole procedure from plaque geometry reconstruction to FSI stress analysis was applicable. In the study, plaque geometries from three patients with recent transient ischemic attack were reconstructed by repairing ruptured fibrous cap. The well correlated relationship between local stress concentrations and plaque rupture sites indicated that extremely high plaque stress could be a factor responsible for plaque rupture. Based on the 20 reconstructed carotid plaques from two groups (symptomatic and asymptomatic), fully coupled fluid structure interaction was performed. It was found that there is a significant difference between symptomatic and asymptomatic patients in plaque stress levels, indicating plaque stress could be used as one of the factors for plaque vulnerability assessment. A corresponding plaque morphological feature study showed that plaque stress is significantly affected by fibrous cap thickness, lipid core size and fibrous cap surface irregularities (curvedness). A procedure was proposed for predicting plaque stress by using fibrous cap thickness and curvedness, which requires much less computational time, and has the potential for clinical routine application. The effects of residual stress on plaque stress analysis and arterial wall material property characterization by using in-vivo MRI data were also discussed for patient specific modeling. As the further development, histological study of plaque sample has been combined with conventional plaque stress analysis by assigning material properties to each computational element, based on the data from histological analysis. This method could bridge the gap between biochemistry and biomechanical study of atherosclerosis plaques. In conclusion, extreme stress distributions in the plaque region can be predicted by modern numerical methods, and used for plaque rupture risk assessment, which will be helpful in clinical practice. The combination of plaque MR imaging analysis, computational modelling, and clinical study/ validation would advance our understandings of plaque rupture, prediction of future rupture, and establish new procedures for patient diagnose, management, and treatment.
Supervisor: Long, Q. Sponsor: British Heart Foundation
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.529047  DOI: Not available
Keywords: Atherosclerosis ; Computational fluid dynamics ; Patient specific modeling
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