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  • https://doi.org/10.1161/str.52.suppl_1.p304Copy DOI Icon

Abstract P304: A Novel Method for Modeling Flow in Cerebral Vasculature Diagnostics

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Abstract

Introduction: We examined the viability of pairing wire-obtained flow pressure data with computational fluid dynamic (CFD) analysis for accurate patient-specific 3D reconstruction of flow through cerebral vessels—an approach that provides comprehensive velocity data and other flow parameters to assist the preventative diagnosis of cerebral stenosis. Methods: Two physical phantom models of a cerebral stenosis—differing in regard to vascular morphology at and around the stenosis—were prepared, filled with physiological saline and connected individually to a flow pump (Vascular Simulations LLC Left Heart Replicator) applying pulsatile flow. Pressure measurements were taken upstream of the stenosis by microcatheter-guided wires to define an inlet boundary condition for CFD analysis. Pressure and velocity at 2 cm proximal to stenosis, the stenosis inlet, the stenosis outlet, and 2 cm distal to stenosis were measured to validate the CFD simulated flow. Results: Excellent agreement was observed between CFD and wire-measured time-dependent flow pressure, with the expected pulsatile behavior observed at each location. Between the two methods, differences in maximum systolic pressures ranged between |0.73±0.72|% and |3.90±0.74|%. Differences in minimum diastolic pressures ranged between |1.79±1.33|% and |9.08±1.42|%. Velocity data from wire measurements lacked pulsatile behavior and showed differences ranging between |2.93±5.20|% and |120.10±2.91|% from CFD-obtained measurements, which did show correct pulsatile behavior. Larger discrepancies were strongly associated with areas of higher CFD predicted velocities (r=0.86). Conclusion: Guidewire flow measurements provided CFD analysis with accurate inlet boundary conditions and validated CFD simulation results. CFD analysis allowed for detailed visualization of key flow parameters that may be difficult to physically measure—such as velocity—that are relevant for understanding risk factors associated with cerebral stenosis on a case-by-case basis.

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