Application of MOGA to Investigating Influence of Factors of Artery Shapes

Abstract

We propose a new method that evaluates the correlativity between factors in natural phenomena by using multi-objective optimization with a genetic algorithm (GA). The effectiveness of this method was tested by applying GAs to determine which hemodynamic factors correlate with artery bifurcation shapes. In the proposed method, first, shapes of each case were optimized by supposing several combinations of hemodynamic factors as objectives using GA. Those factors were a) to minimize maximum Wall Shear Stress (WSS), b) to maximize minimum WSS, c) to minimize WSS gradient, d) to minimize WSS temporary gradient, and e) to minimize inner surface area. We set six combinations in which two of these factors were in a trade-off relationship. Then, we checked how much each optimized shape differed from its original one. After finishing all optimizations, the degree of difference of each combination factor in 14 actual cases was obtained. From the results, the smallest degree of difference was obtained in the combination of minimizing maximum WSS and minimizing inner surface area. The most correlative combination of factors among the six was evaluated by those degrees. We found our method can accurately estimate the correlativity of hemodynamic factors to artery bifurcation shapes.