Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall
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arXiv
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| Format: | Preprint |
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2022
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| author | Suk, Julian de Haan, Pim Lippe, Phillip Brune, Christoph Wolterink, Jelmer M. |
| author_facet | Suk, Julian de Haan, Pim Lippe, Phillip Brune, Christoph Wolterink, Jelmer M. |
| contents | Computational fluid dynamics (CFD) is a valuable asset for patient-specific cardiovascular-disease diagnosis and prognosis, but its high computational demands hamper its adoption in practice. Machine-learning methods that estimate blood flow in individual patients could accelerate or replace CFD simulation to overcome these limitations. In this work, we consider the estimation of vector-valued quantities on the wall of three-dimensional geometric artery models. We employ group equivariant graph convolution in an end-to-end SE(3)-equivariant neural network that operates directly on triangular surface meshes and makes efficient use of training data. We run experiments on a large dataset of synthetic coronary arteries and find that our method estimates directional wall shear stress (WSS) with an approximation error of 7.6% and normalised mean absolute error (NMAE) of 0.4% while up to two orders of magnitude faster than CFD. Furthermore, we show that our method is powerful enough to accurately predict transient, vector-valued WSS over the cardiac cycle while conditioned on a range of different inflow boundary conditions. These results demonstrate the potential of our proposed method as a plugin replacement for CFD in the personalised prediction of hemodynamic vector and scalar fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_05023 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall Suk, Julian de Haan, Pim Lippe, Phillip Brune, Christoph Wolterink, Jelmer M. Machine Learning Computer Vision and Pattern Recognition Group Theory Fluid Dynamics Computational fluid dynamics (CFD) is a valuable asset for patient-specific cardiovascular-disease diagnosis and prognosis, but its high computational demands hamper its adoption in practice. Machine-learning methods that estimate blood flow in individual patients could accelerate or replace CFD simulation to overcome these limitations. In this work, we consider the estimation of vector-valued quantities on the wall of three-dimensional geometric artery models. We employ group equivariant graph convolution in an end-to-end SE(3)-equivariant neural network that operates directly on triangular surface meshes and makes efficient use of training data. We run experiments on a large dataset of synthetic coronary arteries and find that our method estimates directional wall shear stress (WSS) with an approximation error of 7.6% and normalised mean absolute error (NMAE) of 0.4% while up to two orders of magnitude faster than CFD. Furthermore, we show that our method is powerful enough to accurately predict transient, vector-valued WSS over the cardiac cycle while conditioned on a range of different inflow boundary conditions. These results demonstrate the potential of our proposed method as a plugin replacement for CFD in the personalised prediction of hemodynamic vector and scalar fields. |
| title | Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall |
| topic | Machine Learning Computer Vision and Pattern Recognition Group Theory Fluid Dynamics |
| url | https://arxiv.org/abs/2212.05023 |