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Image-based computational model to predict intracranial aneurysm rupture

Intracranial aneurysms are bulging, weak areas of an artery that supply blood to the brain which are relatively common. While most aneurysms do not show symptoms, 1% spontaneously rupture which can be fatal or it can leave the survivor with permanent disabilities. This catastrophic outcome has motivated surgeons to operate on approximately 30% of aneurysms despite their rate of complications arising and cost of operation.The impact of aneurysm morphology on blood flow shear stress and rupture could educate surgical decision-making …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Faculty of Engineering
School
School of Mechanical, Medical and Process Engineering
Research centre(s)
Centre for Biomedical Technologies
Centre for Biomedical Technologies

Image-based assessment of atherosclerotic plaque vulnerability: Towards a computational tool for early detection and prediction

Plaque characteristics and local haemodynamic/mechanical forces keep changing during plaque progression and rupture. Quantifying these changes and discovering the progression-stress correlation can improve our understanding of plaque progression/rupture. This will lead to a quantitative assessment tool for early detection of vulnerable plaques and prediction of possible ruptures. Our research project aims to combine medical imaging, computational modelling, phantom experiments and pathological analysis to investigate plaque progression and vulnerability to rupture in both animal models and patients with carotid stenosis. We …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Faculty of Engineering
School
School of Mechanical, Medical and Process Engineering
Research centre(s)
Centre for Biomedical Technologies
Centre for Biomedical Technologies

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