Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models

• URL: http://arxiv.org/abs/2505.03382v1
• Date: Tue, 06 May 2025 10:01:16 GMT
• Title: Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models
• Authors: Matthias Höfler, Francesco Regazzoni, Stefano Pagani, Elias Karabelas, Christoph Augustin, Gundolf Haase, Gernot Plank, Federica Caforio,
• Abstract summary: This work investigates the application of physics-informed neural networks (PINNs) for inferring active contractility parameters in time-dependent cardiac biomechanical models.<n>We are able to reconstruct in various settings active stress fields in the presence of noise and with a high spatial resolution.<n>This approach opens a new pathway to significantly improve the diagnosis, treatment planning, and management of heart conditions associated with cardiac fibrosis.
• Score: 2.8763745263714005
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 work investigates, through an in-silico study, the application of physics-informed neural networks (PINNs) for inferring active contractility parameters in time-dependent cardiac biomechanical models from these types of imaging data. In particular, by parametrising the sought state and parameter field with two neural networks, respectively, and formulating an energy minimisation problem to search for the optimal network parameters, we are able to reconstruct in various settings active stress fields in the presence of noise and with a high spatial resolution. To this end, we also advance the vanilla PINN learning algorithm with the use of adaptive weighting schemes, ad-hoc regularisation strategies, Fourier features, and suitable network architectures. In addition, we thoroughly analyse 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 approach opens a new pathway to significantly improve the diagnosis, treatment planning, and management of heart conditions associated with cardiac fibrosis.

Related papers

• From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification [1.1142444517901016]
  We utilize the energy strain formulation of Neo-Hookean material to model cardiac tissue deformations.<n>We estimate the local strains within the moving heart and extract a detailed set of features used for cardiovascular disease classification.
  arXiv  Detail & Related papers  (2025-07-08T08:43:05Z)
• IKrNet: A Neural Network for Detecting Specific Drug-Induced Patterns in Electrocardiograms Amidst Physiological Variability [2.017627917643174]
  This study introduces IKrNet, a novel neural network model, which identifies drug-specific patterns in ECGs amidst certain physiological conditions.<n>IKrNet's architecture incorporates spatial and temporal dynamics by using a convolutional backbone with varying receptive field size to capture spatial features.<n>We show that IKrNet outperforms state-of-the-art models' accuracy and stability in varying physiological conditions, underscoring its clinical viability.
  arXiv  Detail & Related papers  (2025-05-12T13:14:47Z)
• PhysLLM: Harnessing Large Language Models for Cross-Modal Remote Physiological Sensing [49.243031514520794]
  Large Language Models (LLMs) excel at capturing long-range signals due to their text-centric design.<n>PhysLLM achieves state-the-art accuracy and robustness, demonstrating superior generalization across lighting variations and motion scenarios.
  arXiv  Detail & Related papers  (2025-05-06T15:18:38Z)
• Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers [43.17768785084301]
  We train an amortized neural posterior estimator on a newly built large dataset of cardiac simulations.<n>We incorporate elements modeling effects to better align simulated data with real-world measurements.<n>The proposed framework can further integrate in-vivo data sources to refine its predictive capabilities on real-world data.
  arXiv  Detail & Related papers  (2024-12-23T13:05:17Z)
• An Optimization Framework to Personalize Passive Cardiac Mechanics [1.3127539363517526]
  This study introduces an inverse finite element analysis (iFEA) framework to estimate the passive mechanical properties of cardiac tissue. With a focus on characterizing the passive mechanical behavior, the framework employs structurally based anisotropic hyperelastic models. The framework is tested in myocardium models of biricle and left atrium derived from cardiac phase-resolved computed tomographic (CT) images.
  arXiv  Detail & Related papers  (2024-04-03T15:23:17Z)
• Physics-informed Neural Network Estimation of Material Properties in Soft Tissue Nonlinear Biomechanical Models [2.8763745263714005]
  We propose a new approach which relies on the combination of physics-informed neural networks (PINNs) with three-dimensional soft tissue nonlinear biomechanical models. The proposed learning algorithm encodes information from a limited amount of displacement and, in some cases, strain data, that can be routinely acquired in the clinical setting. Several benchmarks are presented to show the accuracy and robustness of the proposed method.
  arXiv  Detail & Related papers  (2023-12-15T13:41:20Z)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)
• fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships [68.8204255655161]
  We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data. We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
  arXiv  Detail & Related papers  (2022-10-23T15:11:37Z)
• PhysGNN: A Physics-Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image-Guided Neurosurgery [0.15229257192293202]
  We propose a data-driven model that approximates the solution of finite element analysis (FEA) by leveraging graph neural networks (GNNs) We demonstrate that the proposed architecture, PhysGNN, promises accurate and fast soft tissue deformation approximations while remaining computationally feasible, suitable for neurosurgical settings.
  arXiv  Detail & Related papers  (2021-09-09T15:43:59Z)
• Data-driven generation of plausible tissue geometries for realistic photoacoustic image synthesis [53.65837038435433]
  Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties. We propose a novel approach to PAT data simulation, which we refer to as "learning to simulate" We leverage the concept of Generative Adversarial Networks (GANs) trained on semantically annotated medical imaging data to generate plausible tissue geometries.
  arXiv  Detail & Related papers  (2021-03-29T11:30:18Z)
• Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling [121.50704279659253]
  We propose a cross-verified feature disentangling strategy to disentangle the physiological features with non-physiological representations. We then use the distilled physiological features for robust multi-task physiological measurements. The disentangled features are finally used for the joint prediction of multiple physiological signals like average HR values and r signals.
  arXiv  Detail & Related papers  (2020-07-16T09:39:17Z)

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.