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• W2605542018 abstract "There are many hemodynamic factors which have been suggested to relate to artery shape regulation but it has not been certain which factors are more effective. We propose a new method to evaluate the relevance of hemodynamic factors to artery shape. First, after selecting some associated factors said to be related with artery shapes, we performed multi-objective optimization that sets two of their factors as objectives to obtain optimized shapes using computational fluid dynamics and genetic algorithms. Then, we checked how similar the original shape was to the optimized shapes using Objective Difference Index (ODI). After this process was applied to seven typical carotid artery bifurcation shapes (seven actual cases), the relevance of each combination of two factors was evaluated. We selected five factors: a) to minimize maximum time-averaged Wall Shear Stress (WSS), b) to maximize minimum time-averaged WSS, c) to minimize WSS gradient, d) to minimize WSS temporary gradient, and e) to minimize inner surface area. At the first stage, shapes were optimized by using only radius as the variable using a fixed center line. We set six kinds of combinations of factors that have trade-off relationships. As a result, ODI in the cases of a) and e) was the smallest, having a value was about one-twentieth that of ODI for the second smallest combination factors of c) and e). Combination factors of minimizing both maximum WSS and artery radius were evaluated to be the most relevant to artery radius in the six kinds of tested combinations. At the next stage, by setting both the radius and center line as variables, it became clear that this combination was also related to the position of the center line. We confirmed our method effectively evaluates the relevance of factors to artery bifurcation shapes." @default.
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• W2605542018 date "2017-01-01" @default.
• W2605542018 modified "2023-10-03" @default.
• W2605542018 title "A method to evaluate relevance of hemodynamic factors to artery bifurcation shapes using computational fluid dynamics and genetic algorithms" @default.
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• W2605542018 doi "https://doi.org/10.1299/mej.16-00476" @default.
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