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 favorably to previously published work. This shows the feasibility of CFD surrogate modelling using mesh convolutional neural networks for hemodynamic parameter estimation in artery models.
翻译:计算流体动态(CFD)是个人化、非侵入性地评估动脉动动脉的有益工具,但是其复杂性和耗时性的性质不允许在实践中大规模使用。最近,对利用深度学习快速估计CFD参数(如地表草列壁剪切压力(WSS)等)进行了调查。然而,现有方法通常取决于对表层网格的手工重新校正,以匹配神经神经网络结构。在这项工作中,我们提议使用直接在与CFD中使用的相同的有限元素表面网状网状上运行的网状神经神经神经网络。我们用CFD模拟获得的地面真相,用合成心动动动动动动动动脉动模型(WSS)快速估算CFD参数等。我们展示了我们灵活的深层学习模型可以准确预测表面网状网状内3D WSS矢量的3D矢量。我们的方法在不到5个的模型中,始终在C-qual-qual-ral-al-al-ral-ral-ral-ral-ral-ral-ral-ral-ral-ral-ral-ral-lxxxxxxxxxxxxxxxxxxxxxxxxxxxxx90xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx