Bayesian inference on a microstructural, hyperelastic model of tendon deformation. Issue 190 (18th May 2022)

• Record Type: Journal Article
• Authors: Haughton, James; Cotter, Simon L.; Parnell, William J.; Shearer, Tom
• Is Part Of: Journal of the Royal Society interface. Volume 19:Issue 190(2022)
• Publication Date: 2022-05-18
• Subjects: tendon -- modelling -- microstructural -- hyperelastic -- Bayesian -- uncertainty; Physical sciences -- Research -- Periodicals; Life sciences -- Research -- Periodicals; Interdisciplinary research -- Periodicals; 570.5
• DOI: 10.1098/rsif.2022.0031
• Languages: English
• ISSNs: 1742-5689

Abstract

Abstract : Microstructural models of soft-tissue deformation are important in applications including artificial tissue design and surgical planning. The basis of these models, and their advantage over their phenomenological counterparts, is that they incorporate parameters that are directly linked to the tissue's microscale structure and constitutive behaviour and can therefore be used to predict the effects of structural changes to the tissue. Although studies have attempted to determine such parameters using diverse, state-of-the-art, experimental techniques, values ranging over several orders of magnitude have been reported, leading to uncertainty in the true parameter values and creating a need for models that can handle such uncertainty. We derive a new microstructural, hyperelastic model for transversely isotropic soft tissues and use it to model the mechanical behaviour of tendons. To account for parameter uncertainty, we employ a Bayesian approach and apply an adaptive Markov chain Monte Carlo algorithm to determine posterior probability distributions for the model parameters. The obtained posterior distributions are consistent with parameter measurements previously reported and enable us to quantify the uncertainty in their values for each tendon sample that was modelled. This approach could serve as a prototype for quantifying parameter uncertainty in other soft tissues.