Abstract T MP31: 3D Computational Flow Model of Intracranial Arterial Stenosis With Physiologic Boundary Conditions

Abstract

Introduction: Intracranial arterial stenosis (IAS) is a significant cause of ischemic stroke worldwide. Prior studies have identified degree-of-stenosis, time from symptom onset, and gender as predictors of recurrent stroke. However, the role of wall shear stress (WSS), turbulent kinetic energy (TKE), flow velocity (FV), and pressure gradients have not been studied. We present a computational fluid dynamics (CFD) model of IAS extracted from diagnostic imaging with added anatomical details to further investigate these factors. Methods: The vascular anatomy was reconstructed from a semi-automatically delineated biplane angiogram to obtain the geometry of the internal carotid artery bifurcation, middle cerebral artery (MCA) and anterior cerebral artery (ACA). Lenticulostriate arteries (LSAs) and 0%, 70% and 90% stenoses were added. A volumetric mesh was generated with more than 2 million tetrahedral cells and used to evaluate the hemodynamic parameters of the models using the ANSYS: Simulation Technology package. The boundary conditions of the MCA and ACA outlets were adjusted based on an autoregulatory model of the distal vasculature. Results: In the 0% stenosis model, flow throughout the MCA was laminar observed through the LSAs. Pressure drop, FV, WSS, and TKE were unremarkable. At 70% stenosis, there was an elevated FV through the stenosis (5.4m/s), pressure drop across the stenosis (8.04kPa), distal TKE (0.39m^2/s^2), and WSS distal to the stenosis (25.3Pa) (Figure 1). The 90% stenosis model had high FV and pressure drop (4.2m/s, 10.3kPa), elevated WSS (32.2Pa), but a negligible TKE. Conclusions: A CFD model of the IAS based on patient images with added anatomical details can reproduce hemodynamic parameters associated with IAS. The addition of patient-specific flow patterns and arterial wall elasticity could provide further insight into IAS progression and patient outcomes.