Biomechanical modeling of aneurysm in posterior cerebral artery and posterior communicating artery: Progression and rupture risk

Abstract

An aneurysm is a medical condition where a section of the artery bulges out under high pressure. Patients suffering from aneurysm rupture have a mortality rate of around 20% and a morbidity rate of up to 40%. The present imaging methods, such as MRI and CT scans, only offer geometrical information on the aneurysm and cannot predict the risk of rupture associated with its progression. To address this gap, a novel computational modeling framework was developed to describe aneurysm growth and analyze the rupture risk under varying pressure loading conditions. The aneurysms were modeled at the vulnerable posterior cerebral artery (PCA) and posterior communicating artery (PCoA), extracted using image segmentation. Five different aneurysm diameters and two wall thicknesses were considered to simulate different phases of aneurysm progression. The realistic pressure loadings on the posterior cerebral arteries were described using three pressures (diastolic, systolic, and hypertensive), and the stress distributions across all models were evaluated to estimate the rupture risk. For PCA, the value of max. von-Mises stress varied between 5.334 MPa and 13.324 MPa for different models with wall thickness of 0.05 mm and from 2.579 MPa to 7.582 MPa for 0.1 mm wall thickness models. For PCoA, the value of max. von-Mises stress ranged from 2.073 MPa to 11.383 MPa for artery-aneurysm models with 0.075 mm thickness and from 2.817 MPa to 10.779 MPa for artery-aneurysm models with 0.15 mm thickness. It was found that the stress values on the aneurysm walls significantly varies with change in blood pressure and aneurysm diameter. An aneurysm with a large diameter and thin wall was also observed to pose a significant risk of rupture, particularly at high blood pressures. These results are expected to provide valuable information to the medical practitioners and help in the prediction of rupture risks using image analysis of aneurysm size and in making timely treatment decisions.

Statement of Significance

The points of significance of our work are:

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  A novel computational modeling framework to evaluate the aneurysm growth and analyze the rupture risk.

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  The realistic pressure loadings conditions (i.e., diastolic, systolic, and hypertensive) of the cardiac cycle were considered and the stress distributions were evaluated to estimate the rupture risk.

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  To date, such extensive research on cerebral aneurysms has not been reported. The results are anticipated to provide valuable information to the medical practitioners in predicting the rupture risks using structural parameters of the aneurysm.