Mesh neural networks for SE(3)-equivariant hemodynamics estimation on the artery wall

Comput Biol Med. 2024 May:173:108328. doi: 10.1016/j.compbiomed.2024.108328. Epub 2024 Mar 19.

Abstract

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.

Keywords: Computational fluid dynamics; Coronary arteries; Graph convolutional networks; Group-equivariance; Wall shear stress.

MeSH terms

  • Blood Flow Velocity
  • Computer Simulation
  • Coronary Vessels / diagnostic imaging
  • Coronary Vessels / physiology
  • Hemodynamics* / physiology
  • Humans
  • Hydrodynamics
  • Models, Cardiovascular*
  • Neural Networks, Computer
  • Stress, Mechanical