SE(3) symmetry lets graph neural networks learn arterial velocity estimation from small datasets

• URL: http://arxiv.org/abs/2302.08780v3
• Date: Fri, 4 Aug 2023 06:55:24 GMT
• Title: SE(3) symmetry lets graph neural networks learn arterial velocity estimation from small datasets
• Authors: Julian Suk, Christoph Brune, Jelmer M. Wolterink
• Abstract summary: Hemodynamic velocity fields in coronary arteries could be the basis of valuable biomarkers for diagnosis, prognosis and treatment planning. Velocity fields are typically obtained from patient-specific 3D artery models via computational fluid dynamics (CFD) We propose graph neural networks (GNN) as an efficient black-box surrogate method to estimate 3D velocity fields.
• Score: 3.861633648502351
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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