Liu Xiaolong, Aslan Seda, Hess Rachel, Mass Paige, Olivieri Laura, Loke Yue-Hin, Hibino Narutoshi, Fuge Mark, Krieger Axel
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2319-2323. doi: 10.1109/EMBC44109.2020.9176371.
This paper proposes a computational framework for automatically optimizing the shapes of patient-specific tissue engineered vascular grafts. We demonstrate a proof-of-concept design optimization for aortic coarctation repair. The computational framework consists of three main components including 1) a free-form deformation technique exploring graft geometries, 2) high-fidelity computational fluid dynamics simulations for collecting data on the effects of design parameters on objective function values like energy loss, and 3) employing machine learning methods (Gaussian Processes) to develop a surrogate model for predicting results of high-fidelity simulations. The globally optimal design parameters are then computed by multistart conjugate gradient optimization on the surrogate model. In the experiment, we investigate the correlation among the design parameters and the objective function values. Our results achieve a 30% reduction in blood flow energy loss compared to the original coarctation by optimizing the aortic geometry.
本文提出了一种用于自动优化患者特异性组织工程血管移植物形状的计算框架。我们展示了用于主动脉缩窄修复的概念验证设计优化。该计算框架由三个主要部分组成，包括1）探索移植物几何形状的自由形式变形技术，2）用于收集设计参数对诸如能量损失等目标函数值影响的数据的高保真计算流体动力学模拟，以及3）采用机器学习方法（高斯过程）来开发用于预测高保真模拟结果的替代模型。然后通过对替代模型进行多起始共轭梯度优化来计算全局最优设计参数。在实验中，我们研究了设计参数与目标函数值之间的相关性。通过优化主动脉几何形状，我们的结果实现了与原始缩窄相比血流能量损失降低30%。
