Abstract WMP64: From Static Imaging to Hemodynamic Modelling: Validation of Intracranial Blood Flow Simulations Against Population-Based PC-MRI Studies

Abstract

Purpose: Modelling blood flow in cerebral arteries presents an opportunity to go beyond current luminal stenosis. These models could improve stroke prognostication by adding hemodynamic biomarkers such as translesional shear stress and pressure gradient. Such models, however, are rarely validated against real clinical data. We evaluated the accuracy of a computational fluid dynamics (CFD) model which uses allometric scaling laws against population-based Phase Contrast MRI (PC-MRI) measurements of blood flow in the brain. Methods: 3D models of the anterior circulation of 23 healthy subjects were reconstructed based on their MRA Time of Flight (TOF) images and CFD was used for modelling the blood flow. Lumped parameter Windkessel models were used as boundary conditions and allometric scaling laws were used to divide the flow and tune these boundary conditions, i.e. the resistance at each boundary was automatically adjusted based on the dimensions and branching of upstream vasculature. The results were compared against 4 PC-MRI studies from literature, covering a combined number of 417 healthy subjects. Results: The flow rate across 3 major intracranial arteries (ICA, MCA, and ACA) was obtained from the CFD simulations and was in good agreement with population-based PC-MRI studies. The agreement was 82.6% for the ICA, 85.9% for the MCA, and 80.4% for the ACA when comparing the average flow rate. The inter-patient variability of the flow rate (i.e. standard deviation) obtained from CFD simulations matched closely with the variation range measured in the literature. Allometric scaling enabled the model to accurately divide the flow at bifurcations (MCA/ICA flow ratio was 58.6% for the model vs 56.3% in the literature). We observed an average of 8 mmHg in pressure drop along the anterior circulation of healthy subjects (from ICA to MCA-M1 and ACA-A1), which agrees with the physiological range reported in the literature. Conclusion: This study shows the potential of accurately modelling blood flow based only on static images and demonstrates the validity of using allometric scaling in intracranial CFD simulations. The current modelling approach can potentially assist in stroke prognostication by obtaining hemodynamic risk factors from standard MRA TOF or CTA.