From biaxial tests to cardiac digital twins: a morphomechanics agenda for passive myocardium.

Authors

Fulufhelo Nemavhola, Thanyani Pandelani

Published in

Frontiers in physiology. Volume 17. Pages 1757669. Epub Jun 23, 2026.

Abstract

Passive myocardial mechanics shape ventricular filling, ventricular-ventricular interaction, and the mechanical environment experienced by cardiac cells. Yet many cardiac finite-element and digital-twin models still estimate passive material behaviour mainly from chamber-level pressure-volume or imaging data, which can reproduce global observables while leaving tissue-scale parameters poorly identifiable. In this Perspective, we define morphomechanics as an operational model-data integration framework in which myocardial structure, tissue-scale stress-strain behaviour, and organ-level function jointly determine constitutive-law selection, parameter priors, and calibration constraints for cardiac digital twins. The term is used to fill a practical gap between conventional multiscale modelling and digital-twin calibration: it specifies how experimentally measured tissue mechanics and quantitative microstructure should constrain organ-scale model parameters. We review insights from planar biaxial testing of ventricular myocardium, including porcine, rat, and sheep datasets that reveal nonlinear elasticity, directional anisotropy, and regional heterogeneity. We then examine how passive myocardial properties are currently estimated in digital-twin workflows and identify where tissue-level evidence remains underused. Finally, we propose an actionable roadmap in which biaxial data inform constitutive-law selection and probabilistic parameter priors; diffusion imaging and histology define local material axes and structural anisotropy; and inverse finite-element calibration incorporates tissue-derived constraints through Bayesian priors, regularization penalties, or staged calibration. By explicitly linking myocardial microstructure, tissue-scale mechanics, and organ-scale simulation, a morphomechanics-driven approach could improve physiological fidelity, parameter identifiability, and translational confidence in cardiac digital twins.

PMID:
42416406
Bibliographic data and abstract were imported from PubMed on 08 Jul 2026.

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