Kolachalama Vijaya B, Bressloff Neil W, Nair Prasanth B
Biomedical Engineering Center, Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02139, USA.
Biomed Eng Online. 2007 Dec 13;6:47. doi: 10.1186/1475-925X-6-47.
Arterial geometry variability is inevitable both within and across individuals. To ensure realistic prediction of cardiovascular flows, there is a need for efficient numerical methods that can systematically account for geometric uncertainty.
A statistical framework based on Bayesian Gaussian process modeling was proposed for mining data generated from computer simulations. The proposed approach was applied to analyze the influence of geometric parameters on hemodynamics in the human carotid artery bifurcation. A parametric model in conjunction with a design of computer experiments strategy was used for generating a set of observational data that contains the maximum wall shear stress values for a range of probable arterial geometries. The dataset was mined via a Bayesian Gaussian process emulator to estimate: (a) the influence of key parameters on the output via sensitivity analysis, (b) uncertainty in output as a function of uncertainty in input, and (c) which settings of the input parameters result in maximum and minimum values of the output. Finally, potential diagnostic indicators were proposed that can be used to aid the assessment of stroke risk for a given patient's geometry.
个体内部和个体之间动脉几何形状的变异性是不可避免的。为了确保对心血管流动进行现实的预测，需要有能够系统地考虑几何不确定性的有效数值方法。
提出了一种基于贝叶斯高斯过程建模的统计框架，用于挖掘计算机模拟生成的数据。将所提出的方法应用于分析几何参数对人体颈动脉分叉处血流动力学的影响。结合参数模型和计算机实验设计策略，生成了一组观测数据，其中包含一系列可能的动脉几何形状下的最大壁面切应力值。通过贝叶斯高斯过程模拟器对数据集进行挖掘，以估计：(a) 通过敏感性分析关键参数对输出的影响；(b) 作为输入不确定性函数的输出不确定性；(c) 输入参数的哪些设置会导致输出的最大值和最小值。最后，提出了潜在的诊断指标，可用于辅助评估给定患者几何形状的中风风险。
