Details

Autor(en) / Beteiligte

• Dalton, David
• Gao, Hao
• Husmeier, Dirk

Titel

Emulation of cardiac mechanics using Graph Neural Networks

Ist Teil von

• Computer methods in applied mechanics and engineering, 2022-11, Vol.401, p.115645, Article 115645

Ort / Verlag

Amsterdam: Elsevier B.V

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Recent progress in Graph Neural Networks (GNNs) has allowed the creation of new methods for surrogate modelling, or emulation, of complex physical systems to a high level of fidelity. The success of such methods has yet to be explored however in the context of soft-tissue mechanics, an area of research which has itself seen substantial developments in recent years. The present work explicates on this by introducing an emulation framework based on a multi-scale, message-passing GNN, before applying it to the modelling of passive left-ventricle mechanics. Through numerical experiments, it is demonstrated that the proposed method delivers strong predictive accuracy when benchmarked against the results of the nonlinear finite-element method (FEM), and significantly outperforms an alternative emulator based on a fully connected neural network. Furthermore, large computational gains are achieved at prediction time against the FEM. •New Graph Neural Network (GNN) emulation or surrogate modelling framework.•Applied to a hyper-elastic, non-isotropic model of the left ventricle in diastole.•The GNN delivers significant improvement over traditional emulation approaches.•Significant gains in prediction speed over the finite-element method are realised.•The results constitute an important step towards eventual deployment in clinic.