Biomechanical Prediction of Middle Cerebral artery Aneurysm Rupture Location using CFD Modelling and HOLMES Index

The growing fatality rate of intracranial aneurysm (IA) have driven the research trend towards aneurysm rupture risk. Rupture of the middle cerebral artery (MCA) aneurysm is one of the most severe cases of intracranial aneurysms. Predicting aneurysm rupture frequently requires medical knowledge and proficiency for early treatment decisions. In many cases, an aneurysm which is a bulging artery can cause sudden death or paralyzed for a lifetime once ruptured. Sudden rupture is unforeseen due to the difficulty and challenges in accurately anticipating each patient's risk of aneurysm rupture. Hence, there is a need to establish a predictive technique to measure the aneurysm severity, the potential rupture location, and rupture risk according to the aneurysm morphologies and the patient’s blood pressure condition. Thus, this thesis proposed a correlation of biomechanical parameters that relates to the conditions explained. The patient-specific images of MCAs with aneurysms were remodeled with varied aspect ratio (AR) sizes in different blood pressure conditions and were numerically investigated. The hemodynamic and structural effects of healthy MCAs and MCAs with aneurysms were analyzed using computational fluid dynamic (CFD) using ANSYS software. Experimental validation using particle image velocimetry (PIV) analysis was also performed. The HOLMES index was evaluated across MCA aneurysm models with varying AR. Results show that high HOLMES regions expand significantly with increasing AR and blood pressure. The percentage of area exposed to high HOLMES increased approximately fourfold from low-AR normotensive to high-AR hypertensive cases, indicating a strong synergistic effect between morphology and hemodynamic loading. Validation using a clinical MCA case (AR = 0.917) based on Park et al. demonstrated good agreement between predicted and reported rupture locations, supporting HOLMES as a reliable rupture-location estimation tool.