网格卷积神经网络用于三维动脉模型的壁面剪应力估计
Mesh convolutional neural networks for wall shear stress estimation in 3D artery models
摘要：
计算流体动力学（CFD）是一种有价值的工具，用于个性化，无创评估动脉中的血液动力学，但其复杂性和耗时的性质，禁止在实践中大规模使用。最近，已经研究了使用深度学习快速估计表面网格上的壁面剪切应力（WSS）等CFD参数。然而，现有的方法通常依赖于表面网格的手工重新参数化以匹配卷积神经网络架构。在这项工作中，我们建议改为使用网格卷积神经网络，直接在相同的有限元表面网格计算流体力学中使用。我们训练和评估我们的方法在两个数据集的合成冠状动脉模型有和没有分叉，使用从CFD模拟获得的地面真相。我们证明了我们灵活的深度学习模型可以准确地预测这个表面网格上的3D WSS向量。我们的方法在不到5 [s]的时间内处理新的网格，始终实现了$\leq$ 1.6 [%]的归一化平均绝对误差，并在90.5 [%]的中位数近似精度超过了测试集的峰值，与以前发表的工作相比，这是有利的。这证明了使用网格卷积神经网络的CFD替代建模用于动脉模型中的血液动力学参数估计的可行性。
英文摘要：
Computational fluid dynamics (CFD) is a valuable tool for personalised, non-invasive evaluation of hemodynamics in arteries, but its complexity and time-consuming nature prohibit large-scale use in practice. Recently, the use of deep learning for rapid estimation of CFD parameters like wall shear stress (WSS) on surface meshes has been investigated. However, existing approaches typically depend on a hand-crafted re-parametrisation of the surface mesh to match convolutional neural network architectures. In this work, we propose to instead use mesh convolutional neural networks that directly operate on the same finite-element surface mesh as used in CFD. We train and evaluate our method on two datasets of synthetic coronary artery models with and without bifurcation, using a ground truth obtained from CFD simulation. We show that our flexible deep learning model can accurately predict 3D WSS vectors on this surface mesh. Our method processes new meshes in less than 5 [s], consistently achieves a normalised mean absolute error of $\leq$ 1.6 [%], and peaks at 90.5 [%] median approximation accuracy over the held-out test set, comparing favourably to previously published work. This demonstrates the feasibility of CFD surrogate modelling using mesh convolutional neural networks for hemodynamic parameter estimation in artery models.
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