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

Zahra Darbandi , Mahkame Sharbatdar , Mehrdad Raisee
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Abstract

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.
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治疗脑动脉瘤的分流支架几何特性的稳健优化:概念验证研究
本研究提出了一种新方法来优化用于治疗脑动脉瘤(CA)的分流(FD)支架的设计。通过解决心血管模拟中的不确定性来源(包括几何和物理特性以及边界条件),我们旨在评估稳健优化技术在分流支架设计中的适用性,为获得能在各种实际情况下稳定运行的稳健分流支架奠定基础。通过计算流体动力学(CFD)模拟了简化的二维 CA 和 FD 模型中的血流。采用设计空间探索方法,结合拉丁超立方采样和 Kriging 代理模型,获得了最佳解决方案。目标是最大限度地降低 CA 囊内的速度和涡度。本研究采用非侵入式多项式混沌扩展(PCE)来量化输入不确定性并通过计算模型传播,同时计算速度和涡度降低的统计矩。为了评估不确定源对目标函数的影响,采用了基于索布尔指数的敏感性分析方法。稳健优化包括同时优化速度降低的平均值和标准偏差。此外,我们还考虑了患者的具体情况,并重复进行了稳健优化。结果表明,血液血细胞比容和入口速度是 FD 优化中影响最大的不确定因素。此外,获得的帕累托前沿显示,在稳健设计中,FD 支杆集中在 CA 颈部的远端区域,而优化设计则在近端区域有更多的支杆。本研究提出了一种兼顾稳健性和优化性的 FD,速度降低了 72.31%,标准偏差为 0.00343。
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5.90
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0.00%
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审稿时长
10 weeks
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