Mesh convolutional neural networks for wall shear stress estimation in 3D artery models

• URL: http://arxiv.org/abs/2109.04797v1
• Date: Fri, 10 Sep 2021 11:32:05 GMT
• Title: Mesh convolutional neural networks for wall shear stress estimation in 3D artery models
• Authors: Julian Suk, Pim de Haan, Phillip Lippe, Christoph Brune, Jelmer M. Wolterink
• Abstract summary: We propose to use mesh convolutional neural networks that directly operate on the same finite-element surface mesh as used in CFD. We show that our flexible deep learning model can accurately predict 3D wall shear stress vectors on this surface mesh.
• Score: 7.7393800633675465
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computational fluid dynamics (CFD) is a valuable tool for personalised, non-invasive evaluation of hemodynamics in arteries, but its complexity and time-consuming nature prohibit large-scale use in practice. Recently, the use of deep learning for rapid estimation of CFD parameters like wall shear stress (WSS) on surface meshes has been investigated. However, existing approaches typically depend on a hand-crafted re-parametrisation of the surface mesh to match convolutional neural network architectures. In this work, we propose to instead use mesh convolutional neural networks that directly operate on the same finite-element surface mesh as used in CFD. We train and evaluate our method on two datasets of synthetic coronary artery models with and without bifurcation, using a ground truth obtained from CFD simulation. We show that our flexible deep learning model can accurately predict 3D WSS vectors on this surface mesh. Our method processes new meshes in less than 5 [s], consistently achieves a normalised mean absolute error of $\leq$ 1.6 [%], and peaks at 90.5 [%] median approximation accuracy over the held-out test set, comparing favorably to previously published work. This shows the feasibility of CFD surrogate modelling using mesh convolutional neural networks for hemodynamic parameter estimation in artery models.

Related papers

• Trajectory Flow Matching with Applications to Clinical Time Series Modeling [77.58277281319253]
  Trajectory Flow Matching (TFM) trains a Neural SDE in a simulation-free manner, bypassing backpropagation through the dynamics. We demonstrate improved performance on three clinical time series datasets in terms of absolute performance and uncertainty prediction.
  arXiv  Detail & Related papers  (2024-10-28T15:54:50Z)
• Aero-Nef: Neural Fields for Rapid Aircraft Aerodynamics Simulations [1.1932047172700866]
  This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains. The proposed models can be applied directly to unstructured domains for different flow conditions. Remarkably, the method can perform inference five order of magnitude faster than the high fidelity solver on the RANS transonic airfoil dataset.
  arXiv  Detail & Related papers  (2024-07-29T11:48:44Z)
• Enhancing lattice kinetic schemes for fluid dynamics with Lattice-Equivariant Neural Networks [79.16635054977068]
  We present a new class of equivariant neural networks, dubbed Lattice-Equivariant Neural Networks (LENNs) Our approach develops within a recently introduced framework aimed at learning neural network-based surrogate models Lattice Boltzmann collision operators. Our work opens towards practical utilization of machine learning-augmented Lattice Boltzmann CFD in real-world simulations.
  arXiv  Detail & Related papers  (2024-05-22T17:23:15Z)
• Voxel2Hemodynamics: An End-to-end Deep Learning Method for Predicting Coronary Artery Hemodynamics [24.8579242043367]
  Local hemodynamic forces play an important role in determining the functional significance of coronary arterial stenosis. We propose an end-to-end deep learning framework, which could predict the coronary artery hemodynamics from CCTA images.
  arXiv  Detail & Related papers  (2023-05-30T15:12:52Z)
• SE(3) symmetry lets graph neural networks learn arterial velocity estimation from small datasets [3.861633648502351]
  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.
  arXiv  Detail & Related papers  (2023-02-17T09:42:38Z)
• Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall [13.113110989699571]
  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. 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.
  arXiv  Detail & Related papers  (2022-12-09T18:16:06Z)
• Learned Vertex Descent: A New Direction for 3D Human Model Fitting [64.04726230507258]
  We propose a novel optimization-based paradigm for 3D human model fitting on images and scans. Our approach is able to capture the underlying body of clothed people with very different body shapes, achieving a significant improvement compared to state-of-the-art. LVD is also applicable to 3D model fitting of humans and hands, for which we show a significant improvement to the SOTA with a much simpler and faster method.
  arXiv  Detail & Related papers  (2022-05-12T17:55:51Z)
• Deep Implicit Statistical Shape Models for 3D Medical Image Delineation [47.78425002879612]
  3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models that imposed anatomical constraints and produced high quality surfaces were a core technology. We present deep implicit statistical shape models (DISSMs), a new approach to delineation that marries the representation power of CNNs with the robustness of SSMs.
  arXiv  Detail & Related papers  (2021-04-07T01:15:06Z)
• Probabilistic 3D surface reconstruction from sparse MRI information [58.14653650521129]
  We present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction. Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets.
  arXiv  Detail & Related papers  (2020-10-05T14:18:52Z)
• Cephalometric Landmark Regression with Convolutional Neural Networks on 3D Computed Tomography Data [68.8204255655161]
  Cephalometric analysis performed on lateral radiographs doesn't fully exploit the structure of 3D objects due to projection onto the lateral plane. We present a series of experiments with state of the art 3D convolutional neural network (CNN) based methods for keypoint regression. For the first time, we extensively evaluate the described methods and demonstrate their effectiveness in the estimation of Frankfort Horizontal and cephalometric points locations.
  arXiv  Detail & Related papers  (2020-07-20T12:45:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.