Improved Flow Prediction in Intracranial Aneurysms Using Data Assimilation

Abstract

Rupture of intracranial aneurysms often leads to irreversible disabilities or even death. The investigation of hemodynamics increases the understanding of cardiovascular diseases, this gain of knowledge can support physicians in outcome prediction and therapy planning. Hemodynamic simulations are restricted by modeling assumptions and uncertain initial conditions, whereas PC-MRI data is affected by measurement noise and artifacts. To overcome the limitations of both techniques, the current study uses a Localization Ensemble Transform Kalman Filter (LETKF) to incorporate uncertain Phase-Contrast MRI data into an ensemble of numerical simulations. The analysis output provides an improved state estimate of the threedimensional blood flow field. Benchmark measurements are carried out in a silicone phantom model of an idealized aneurysm under user-specific inflow conditions. Validation is ensured with high-resolution Particle Imaging Velocimetry (PIV) obtained from a vertical slice in the center of the same geometry. Results show that even velocity peaks smaller than the PC-MRI resolution can be reconstructed using the employed approach. The root mean square error (RMSE) of the analysis state estimate is reduced by 27 % to 89 % in comparison to interpolation of the PC-MRI data onto the PIV grid resolution.