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Computational fluid dynamics (CFD) is a valuable tool for personalised,non-invasive evaluation of hemodynamics in arteries, but its complexity andtime-consuming nature prohibit large-scale use in practice. Recently, the useof deep learning for rapid estimation of CFD parameters like wall shear stress(WSS) on surface meshes has been investigated. However, existing approachestypically depend on a hand-crafted re-parametrisation of the surface mesh tomatch convolutional neural network architectures. In this work, we propose toinstead use mesh convolutional neural networks that directly operate on thesame finite-element surface mesh as used in CFD. We train and evaluate ourmethod on two datasets of synthetic coronary artery models with and withoutbifurcation, using a ground truth obtained from CFD simulation. We show thatour flexible deep learning model can accurately predict 3D WSS vectors on thissurface mesh. Our method processes new meshes in less than 5 [s], consistentlyachieves a normalised mean absolute error of $\leq$ 1.6 [%], and peaks at 90.5[%] median approximation accuracy over the held-out test set, comparingfavourably to previously published work. This demonstrates the feasibility ofCFD surrogate modelling using mesh convolutional neural networks forhemodynamic parameter estimation in artery models.

Mesh convolutional neural networks for wall shear stress estimation in 3D artery models