mlsanityData to 14 Oct 2026

Deep vectorised operators for pulsatile hemodynamics estimation in coronary arteries from a steady-state prior

arXiv 2410.11920 ↗First posted 15 Oct 2024q-bio.QM cs.LGcited by 6

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Abstract (arXiv)

Cardiovascular hemodynamic fields provide valuable medical decision markers for coronary artery disease. Computational fluid dynamics (CFD) is the gold standard for accurate, non-invasive evaluation of these quantities in silico. In this work, we propose a time-efficient surrogate model, powered by machine learning, for the estimation of pulsatile hemodynamics based on steady-state priors. We introduce deep vectorised operators, a modelling framework for discretisation-independent learning on infinite-dimensional function spaces. The underlying neural architecture is a neural field conditioned on hemodynamic boundary conditions. Importantly, we show how relaxing the requirement of point-wise action to permutation-equivariance leads to a family of models that can be parametrised by message passing and self-attention layers. We evaluate our approach on a dataset of 74 stenotic coronary arteries extracted from coronary computed tomography angiography (CCTA) with patient-specific pulsatile CFD simulations as ground truth. We show that our model produces accurate estimates of the pulsatile velocity and pressure (approximation disparity 0.368 ± 0.079) while being agnostic (p < 0.05 in a one-way ANOVA test) to re-sampling of the source domain, i.e. discretisation-independent. This shows that deep vectorised operators are a powerful modelling tool for cardiovascular hemodynamics estimation in coronary arteries and beyond.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 19 of its 51 references that are in the map. First posted on arXiv 15 Oct 2024.

Builds on

19 of 51 references.

References · 19

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7 more
  1. 2023Learning Reduced-Order Models for Cardiovascular Simulations with Graph Neural Networkscited by 20
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2025Active Learning for Deep Learning-Based Hemodynamic Parameter Estimationcited by 2
  2. 2025Wall Shear Stress Estimation in Abdominal Aortic Aneurysms: Towards Generalisable Neural Surrogate Modelscited by 1
  3. 2026Multi-fidelity graph-based neural networks architectures to learn Navier-Stokes solutions on non-parametrized 2D domainscited by 0
  4. 2026Real-Time Pulsatile Flow Prediction for Realistic, Diverse Intracranial Aneurysm Morphologies using a Graph Transformer and Steady-Flow Data Augmentationcited by 0
  5. 2026Stable Long-Horizon Spatiotemporal Prediction on Meshes Using Latent Multiscale Recurrent Graph Neural Networkscited by 0
  6. 2026When Does Equivariance Help? Canonical Alignment in Neural Fluid Surrogatescited by 0