Towards a standardized workflow for measurement-driven in silico treatment planning of flow diverters

Today, flow diverters (FDs) are commonly used in treating intracranial aneurysms to occlude the sac, thus reconstructing the parent artery. Modeling the effect of FD is challenging but a porous layer approximation in hemodynamic analysis can balance computational efficiency, while maintaining physiological precision. However, the porous FD model requires linear and quadratic coefficients of the pressure drop, which can be obtained from hydrodynamic resistance (HR) measurements. These measurements are difficult to carry out even in a laboratory environment. This article presents an in vitro measurement and data correction technique to acquire the properties and present simulation-ready coefficients incorporated into a numerical in-silico solution based on the lattice-Boltzmann method (LBM). In order to examine post-treatment hemodynamics in patient-specific aneurysm models, these improved HR coefficients were integrated into our computational fluid dynamics (CFD) solver. A test case demonstrates the potential of this strategy by showing, how the deployment of FDs reduces intra-aneurysmal flow. The methodology prepares the ground for future research on more reliable FD characterization by increasing the predictive accuracy of FD simulations.