Patient-specific lattice-Boltzmann simulations with inflow conditions from magnetic resonance velocimetry measurements for analyzing cerebral aneurysms

Abstract

Magnetic resonance velocimetry (MRV) measurements were used as inflow conditions for lattice-Boltzmann (LB) simulations to analyze cerebral aneurysms. Unlike previous studies on larger vascular structures, aneurysm analysis involves smaller scales and higher pressure differences, making near-wall velocity measurements challenging with standard 3 Tesla scanners. To address this, the aneurysm geometry was scaled 5-fold for sufficient magnetic resonance velocimetry (MRV) resolution, with inflow measurements interpolated onto the simulation grid while ensuring dimensionless equivalence via the Reynolds number. Zero-velocity points were included near walls to enforce the no-slip condition if measurement points exceed the simulation domain. The proposed interpolation-based inflow method was compared to a nearest-neighbor approach and a parabolic velocity profile. It achieved the best agreement with MRV centerline velocity measurements (mean error: 3.12%), followed by the nearest-neighbor method (3.18%) and the parabolic profile (9.85%). The parabolic inflow led to centerline velocity overpredictions and total pressure underpredictions, while the nearest-neighbor approach underestimated the wall shear stress (WSS) and exhibited inconsistencies in wall normal stress (e.g., maximum WSS was 18.3% lower than with interpolation). Using the interpolated inflow method, Newtonian and non-Newtonian flows based on the Carreau–Yasuda model were compared. The non-Newtonian model showed lower centerline velocities and total pressure but higher WSS than the Newtonian case. These findings highlight the importance of accurate, patient-specific inflow conditions and the necessity of non-Newtonian modeling for reliable WSS predictions. Combining MRV measurements with non-Newtonian LB simulations provides a robust framework for personalized cerebral aneurysm hemodynamic evaluation.