Constitutive parameter inference using physics-informed full volume inverse modeling of intact and torn rotator cuff tendons
Year: 2026
Type: article
Abstract: In this work, we characterized the material properties of an animal model of the rotator cuff tendon using full volume datasets of its intact and injured states. Unlike conventional approaches relying on surface strains or excised specimens, our framework leverages voxel-wise displacement data to infer constitutive behavior while preserving native architecture. Our experimental setup activated volumetric, tensile, and shear mechanisms given the tendon’s complex geometry. We implemented an approach to model inference termed variational system identification (VSI) to solve the weak form of the stress equilibrium equation using these full volume displacements, enabling identification of dominant deformation mechanisms. Three constitutive models were used for parameter inference: a neo-Hookean model, a modified Holzapfel-Gasser-Ogden (HGO) model with higher-order terms in the first and second invariants, and a reduced polynomial model based on the first, second, and fiber-related invariants. VSI-inferred parameters were further refined using an adjoint-based partial differential equation (PDE)-constrained optimization framework. The modified HGO model captures the tendon’s deformation mechanisms with reasonable accuracy, while the neo-Hookean model fails to reproduce key internal features, particularly the shear behavior in injured tendons. Surprisingly, the simplified polynomial model performs comparably to the modified HGO formulation using only three terms. These findings suggest that while current constitutive models do not fully replicate the complex internal mechanics of the tendon, they can capture key trends in intact and damaged tissue, using a homogeneous modeling approach. Continued model development is needed to bridge this gap and enable clinical-grade, predictive simulations of tendon injury and repair.
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,220
Award: G8235554494
OpenAlex ID: W7160263971
