(G-275) Investigation of hemodynamic and morphologic characteristics in stable and growing intracranial aneurysms using computational fluid dynamics

Introduction::

Intracranial aneurysms (IAs) are abnormal dilations within cranial vasculature, estimated to be present in 2-5% of adult humans. Unruptured IAs are categorized as either stable or unstable. Stable IAs remain the same size, while unstable IAs show growth and have a significantly greater chance of rupture. Advances in medical imaging have led to increased incidental detection of unruptured IAs. Interventions for these detected IAs are associated with significant complication risks but can be necessary in preventing rupture. However, most IAs remain stable, so treatment should be reserved for high-risk aneurysms. Traditional risk stratification of IAs relies on patient clinical history and aneurysm size and location. Hemodynamic characterization of IAs could improve risk assessment by providing quantitative measures associated with aneurysm progression. This includes wall shear stress (WSS), a hemodynamic metric that has been shown to influence endothelial modeling pathways within the vasculature. In this study, image-based computational fluids dynamics (CFD) modeling was conducted to determine IA hemodynamic metrics.

Materials and Methods::

Five patient-specific geometries were obtained from time-of-flight magnetic resonance angiography, according to an IRB approved protocol. The geometries contain both stable and unstable aneurysms. The MRI data was segmented in the open-source software ITK-snap, and the resulting model was imported into the open-source modeling platform SimVascular to run the CFD. Mesh independence studies were performed with respect to WSS at a threshold of 5% difference. The blood was assumed to be Newtonian, and wall was assumed to be rigid. The inlet boundary conditions correspond to patient-specific flow, if available, or generalized vessel-specific flow. The outlet boundary conditions were set as a lumped parameter RCR model. Within the RCR model, the proximal and distal resistance were modeled at a 1:10 ratio respectively, total resistance was assigned based on Murray’s Law, and capacitance was tuned to get periodic and physiologically reasonable pressure values. The simulations were run by solving unsteady Navier-Stokes equations using second order temporal discretization. The CFD simulation was post-processed in the open-source visualization software ParaView.

Results, Conclusions, and Discussions::

A total of five geometries were simulated from the following four arteries: anterior communicating artery, right anterior cerebral artery, left anterior cerebral artery, and basilar artery. Two geometries were from the basilar artery of the same patient, taken at different time points, during which the aneurysm underwent growth. The following hemodynamics parameters were computed from the simulated flow fields: time averaged WSS, oscillatory shear index (OSI), and Q-criterion. OSI indicates the change in WSS direction over the cardiac cycle, and Q-criterion shows regions of elevated vorticity. Additionally, morphological characteristics of the intracranial aneurysms were measured. These included aneurysm surface area, aspect ratio (aneurysm depth to neck diameter), size ratio (aneurysm size to vessel diameter), bottle neck factor (aneurysm width to neck diameter) and height-to-width ratio (aneurysm height to aneurysm width). This investigation’s results consider potential associations between morphologic and hemodynamic variables in both stable and growing IAs, as shown in figure 1. This preliminary data will be used as the groundwork for a longitudinal study connecting IA characteristics to aneurysm stability. In this longitudinal study, patients with low risk unruptured intracranial aneurysm will be imaged twice, a baseline and year-long follow-up. Like the basilar artery in the above study, their hemodynamic metrics and morphological characteristics will be correlated to their observed growth (or lack thereof). With this increased data set, there is the potential to elucidate key variables that may improve IA rupture risk in a clinical setting.

Acknowledgements (Optional): : The team acknowledges Northwestern University and University of California San Francisco for providing MRI data.

The team thanks the American Heart Association for the Institutional Award for Undergraduate Student Training Grant (23IAUST1032828) and the Purdue University Summer Undergraduate Research Fellowship (SURF) Program for providing funding.

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