Hemodynamic velocity fields in coronary arteries could be the basis of valuable biomarkers for diagnosis, prognosis and treatment planning in cardiovascular disease. Velocity fields are typically obtained from patient-specific 3D artery models via computational fluid dynamics (CFD). However, CFD simulation requires meticulous setup by experts and is time-intensive, which hinders large-scale acceptance in clinical practice. To address this, we propose graph neural networks (GNN) as an efficient black-box surrogate method to estimate 3D velocity fields mapped to the vertices of tetrahedral meshes of the artery lumen. We train these GNNs on synthetic artery models and CFD-based ground truth velocity fields. Once the GNN is trained, velocity estimates in a new and unseen artery can be obtained with 36-fold speed-up compared to CFD. We demonstrate how to construct an SE(3)-equivariant GNN that is independent of the spatial orientation of the input mesh and show how this reduces the necessary amount of training data compared to a baseline neural network.
@article{arxiv.2302.08780,
title = {SE(3) symmetry lets graph neural networks learn arterial velocity estimation from small datasets},
author = {Julian Suk and Christoph Brune and Jelmer M. Wolterink},
journal= {arXiv preprint arXiv:2302.08780},
year = {2023}
}
Comments
First published in "12th International Conference on Functional Imaging and Modeling of the Heart" (FIMH), pp 445-454, 2023 by Springer Nature