Abstract P344: Validation of Computational Fluid Dynamics Against PC-MRI Measurements of Blood Flow in Healthy Intracranial Arteries: On the Threshold to Clinical Utility for Stroke Prediction

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Aim: Computational Fluid Dynamics (CFD) modeling of blood flow is a promising technique to obtain hemodynamic biomarkers and predict recurrent strokes. Few studies, however, have validated CFD against other methods for measuring hemodynamics, e.g. phase contrast MRI (PC-MRI). To resolve the uncertainties about the accuracy of CFD, this study validates the results of an automated software that combines deep learning and CFD for simulating blood flow given an MR angiogram against PC-MRI. Methods: Intracranial arteries in 10 healthy volunteers (60% male, mean age 38 ± 16 years) were automatically segmented from TOF MRA obtained at 3T using deep learning. Subject-specific CFD modelling was undertaken. PC-MRI flow rate for each subject was used at the inlet boundary condition, but not at the outlets. Outlet boundary condition was set to lumped parameter Windkessel model, with the resistance programmatically determined using allometric scaling laws based on the dimension and branching of vasculature. For comparison, two commonly used outlet conditions (exponent 2 & 3 in Murray’s Law) were also considered. CFD results for flow rate and wall shear stress were compared against PC-MRI using Pearson’s correlation coefficient. Results: The flow rate across major intracranial arteries from CFD was in excellent agreement with the PC-MRI (MCA: r = 0.83, ACA: r = 0.81 & PCA: r = 0.82). In addition, there was good correlation for both wall shear stress (WSS) and WSS Ratio (WSSR, i.e. the ratio of WSS between a branch and its parent vessel) (MCA (WSS/WSSR): r = 0.6/0.77, ACA (WSS/WSSR): r = 0.86/0.93 & PCA (WSS/WSSR): r = 0.72/0.85). The inter-subject variability of the flow rate (i.e. standard deviation) obtained from CFD matched closely the variation range observed from PC-MRI and the literature. The two alternative outlet models behaved similarly but suffered from vessel truncation and high errors in some cases. Conclusion: CFD modelling of blood flow in intracranial arteries from static images has good agreement with PC-MRI measurements when allometric scaling laws are applied for setting Windkessel outflow conditions. This lays the foundation for using CFD to predict recurrent strokes as the technique uses standard MRA images already commonly acquired for stroke evaluation.