Maher Gabriel D, Fleeter Casey M, Schiavazzi Daniele E, Marsden Alison L
Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.
Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA.
Comput Methods Appl Mech Eng. 2021 Dec 1;386. doi: 10.1016/j.cma.2021.114038. Epub 2021 Aug 14.
We propose a novel approach to generate samples from the conditional distribution of patient-specific cardiovascular models given a clinically aquired image volume. A convolutional neural network architecture with dropout layers is first trained for vessel lumen segmentation using a regression approach, to enable Bayesian estimation of vessel lumen surfaces. This network is then integrated into a path-planning patient-specific modeling pipeline to generate families of cardiovascular models. We demonstrate our approach by quantifying the effect of geometric uncertainty on the hemodynamics for three patient-specific anatomies, an aorto-iliac bifurcation, an abdominal aortic aneurysm and a sub-model of the left coronary arteries. A key innovation introduced in the proposed approach is the ability to learn geometric uncertainty directly from training data. The results show how geometric uncertainty produces coefficients of variation comparable to or larger than other sources of uncertainty for wall shear stress and velocity magnitude, but has limited impact on pressure. Specifically, this is true for anatomies characterized by small vessel sizes, and for local vessel lesions seen infrequently during network training.
我们提出了一种新颖的方法，用于在给定临床获取的图像体积的情况下，从患者特异性心血管模型的条件分布中生成样本。首先使用回归方法训练一个带有随机失活层的卷积神经网络架构，用于血管腔分割，以实现血管腔表面的贝叶斯估计。然后将该网络集成到路径规划的患者特异性建模管道中，以生成心血管模型族。我们通过量化几何不确定性对三种患者特异性解剖结构（主动脉-髂动脉分叉、腹主动脉瘤和左冠状动脉子模型）血流动力学的影响来展示我们的方法。所提出的方法中引入的一个关键创新是能够直接从训练数据中学习几何不确定性。结果表明，几何不确定性如何产生与壁面剪应力和速度大小的其他不确定性来源相当或更大的变异系数，但对压力的影响有限。具体而言，对于以小血管尺寸为特征的解剖结构以及在网络训练期间很少出现的局部血管病变来说，情况确实如此。
