Robust optimization of geometrical properties of flow diverter stents for treating cerebral aneurysm: A proof-of-concept study

This study presents a novel approach to optimize the design of flow diverter (FD) stents for cerebral aneurysm (CA) treatment. By addressing sources of uncertainty in cardiovascular simulations, including geometrical and physical properties and boundary conditions, we aim to assess the applicability of robust optimization techniques to the FD design, establishing a foundation for acquiring robust FDs that are capable of operating consistently under various real-world scenarios. Blood flow in a simplified 2-dimensional CA and FD model was simulated through computational fluid dynamics (CFD). A design space exploration method, incorporating Latin hypercube sampling and Kriging surrogate models, was employed to obtain the optimal solution. The objective was to maximize the reduction in velocity and vorticity within the CA sac. This study used non-intrusive polynomial chaos expansion (PCE) to quantify and propagate the input uncertainties through the computational model and compute the statistical moments of velocity and vorticity reductions. To assess the effect of uncertain sources on objective functions, a sensitivity analysis method based on Sobol indices was applied. Robust optimization involved simultaneously optimizing the mean and standard deviation of velocity reduction. Additionally, we accounted for patients’ specific conditions and repeated the robust optimization. The results indicate that blood Hematocrit and inlet velocity are the most impactful uncertain sources in FD optimization. Moreover, the obtained Pareto front shows that in robust designs, FD struts are concentrated in the distal region of the CA neck, while optimal designs have more struts in the proximal region. This study proposes an FD that compromises robustness and optimality with a velocity reduction of 72.31 % and a standard deviation of 0.00343.