Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall

Understand

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.