An investigation into Abdominal Aortic Aneurysm (AAA) rupture prediction

Cardiovascular Disease (CVD) is one of the leading causes of death and disability in the world. The mortality rate between 1993 -1997 in New Zealand was about 46% and 32% worldwide. CVDs such as Abdominal Aortic Aneurysm (AAA) is life threatening and poses a very high risk for aneurysm patients with particular aneurysm diameter. AAA rupture is a patient-specific problem with evolving structures and on-going growth. Current ultrasound methods are used to probe for and diagnose instantaneous AAA by analysing arterial tissue deformation. However, tracking the progression of potential aneurysms, and predicting their risk of rupturing based on the diameter of the aneurysm is still an insufficient method. AAA image segmentation and analysis using the Patient-Specific Aneurysm Rupture Predictor (P-SARP) protocol is proposed to identify dependent elements that lead to a three-dimensional (3-D) aneurysm reconstructive model. Models of the patient-specific aneurysm images were designed along with biomechanical characterization and specific material properties to be incorporated. Fluid Structure Interaction (FSI) is used to mathematically establish the oscillations of patient-specific cyclic pressure loading in order to visualize the impact of potential pressure distributions on the deformed arterial wall over time. The correlation between the geometric elements and the models’ potential for rupture are extensively investigated to produce a possible AAA rupture mechanism. This research presents a new Computational Fluid Dynamics (CFD) of Patient-Specific Aneurysm Model (PSAM) which is based on the energy strain function combined with the dilated vessel wall stress-strain relationship to predict aneurysm rupture. This thesis focuses on investigating how computer simulation can be incorporated to predict AAA rupture. The personalized model is developed based on instantaneous arterial deformations obtained from ultrasound images using a 6-9 MHz doppler transducer. The PSAM relies on available vii mechanical properties and parameters obtained from the personalised model. Using the strain energy function based on historical stress-strain relationship to extrapolate cyclic loading on the PSAM along with patient-specific pressure, multi variant factors are proposed and considered to predict the actual location of the weakening points to reach rupture. The material properties of the wall are calculated using biaxial tensile tests to observe the time dependency of the material response and formation of the aneurysm wall rupture. The outcomes indicate that the proposed technique of the PSAM model has the ability correlate the wall deformation and tissue failure mode with predicting rupture. Thus, this method can positively be integrated with already established ultrasound techniques for improvements in the accuracy of future diagnoses of potential AAA ruptures