ABSTRACT
Objective To optimize the baseline on the trapezoidal cross section of stent wires, so as to reduce the risk of intracranial saccular aneurysm rupture after the implantation of such stents. Methods Thirty-eight trapezoidal cross-section wire stents with different baselines were constructed to establish the finite element models. Numerical simulation by fluid-solid interaction method was conducted to calculate 38 maximum pressure gradients on the aneurysm wall. GRNN (general regression neural network) and GA (genetic algorithm) were used to optimize the baseline on the cross-section of stents with trapezoidal cross-section wire so as to minimize the maximal pressure gradient on the aneurysm wall. Results Compared with the traditional stent with rectangular cross-section wire, the maximal pressure gradient on the a neurysm wall was reduced by 7.86% after the implantation with the optimized stent with trapezoidal cross-section wire. Conclusions The combination of GRNN and GA is an effective approach for stent optimization.