Key result
Physics-informed neural networks accurately reconstruct cardiac stress fields and detect scars using sparse kinematic data.
Why the study?
Accurate assessment of active stress parameters is fundamental for understanding myocardial function but remains difficult to achieve clinically when only displacement and strain imaging data are available.
A novel physics-informed neural network framework enables accurate estimation of active myocardial contractility and scar detection from sparse imaging data, offering a new tool for computational cardiac biomechanics.
• Ad hoc network architecture and optimiser selection improve PINN learning efficiency. • Adapted regularisation and residual-based attention enhance parameter identifiability. • Decoupled space-time representation reduces computational cost for dynamic simulations. • Pareto front analysis provides insights into optimal loss weight combinations in PINNs. • Scar detection achieved without shape assumptions using only sparse kinematic data. Active stress models in cardiac biomechanics account for the mechanical deformation caused by muscle activity, thus providing a link between the electrophysiological and mechanical properties of the tissue. The accurate assessment of active stress parameters is fundamental for a precise understanding of myocardial function but remains difficult to achieve in a clinical setting, especially when only displacement and strain data from medical imaging modalities are available. This study introduces a novel physics-informed neural networks (PINNs) framework for inferring active contractility in time-dependent cardiac biomechanics from these types of imaging data. By leveraging adaptive weighting schemes, residual-based attention mechanisms, ad-hoc regularisation strategies, Fourier Features, the proposed method achieves robust and accurate reconstruction of active stress fields in various settings, in the presence of noise and with a high spatial resolution. In addition, we perform a thorough Pareto front analysis to study the influence of the loss weights in the reconstruction of active stress parameters. Finally, we apply the method to the characterisation of tissue inhomogeneities and detection of fibrotic scars in myocardial tissue. This work represents a significant methodological advancement, addressing key challenges in inverse problems for computational biomechanics and it opens a new pathway to significantly improve the diagnosis, treatment planning, and management of heart conditions associated with cardiac diseases and conditions associated with tissue heterogeneity, such as cardiac fibrosis or myocardial infarction.
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Höfler et al. (2026) studied this question. A novel physics-informed neural network framework accurately reconstructed active cardiac stress fields and achieved scar detection without shape assumptions using only sparse kinematic data.
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