Automatic characterization of soft tissues material properties during mechanical tests

Abstract

Introduction:The estimation of the non-linear viscoelastic characteristics of human soft tissues, such as ligaments and tendon, is often affected by the implemented procedure. This study aims at developing and validating a protocol, associated with a contactless and automatic procedure, enabling the determination of the material behavior and properties of any soft tissues. Methods: Several markers were drawn onto the soft tissue specimen analyzed under uniaxial tensile test. An automatic contactless procedure, that uses a camera for recording the position of the markers during the test, was developed to compute the displacement, and the force applied, enabling the calculation of the true-stress/strain curve of the material. Young's modulus and Poisson's ratio can be calculated, on demand, for selected regions of interest of the soft tissues. The repeatability and reproducibility of the procedure were analyzed. The procedure was initially tested and verified on an artificial silicone material and later applied for investigating the mechanical behavior of a pig Achilles tendon and of a human patellar tendon. Results: The procedure show a high repeatability, independent by the operator, reliability and accuracy for the tested synthetic material (with a maximum error of 3.7% for Young's modulus). Additionally, the developed protocol was also suitable for the analysis of animal and human soft tissues. Conclusion: A protocol to automatically and ac-curately determine material properties in soft tissues was developed, tested and validated. Such approach could successfully be implemented for the mechanical characterization of any biological soft-tissue. Level of evidence: V.

A proof of concept to define the parameters affecting poly-l-lactide-co-poly-ε-caprolactone shape memory electrospun nanofibers for biomedical applications

“…Uniaxial stress was applied to stretch the material and a relationship established with the resulting strain. Loading velocity was 5 mm/min because, according to the literature, this speed is used for soft tissue material characterization [ 25 , 26 ].…”

Hybrid 3D-Printed and Electrospun Scaffolds Loaded with Dexamethasone for Soft Tissue Applications

“…With regard to the 20% infill hybrid scaffolds, the presence of the fibers with the drug (#5) seemed to increase the E value and, therefore, the mechanical resistance. Instead, for the hybrid scaffolds with 50% infill, there were no substantial E value differences that exceeded 40 MPa, and so they are not very usable in the field of soft tissue regeneration [43].…”

“…This particular shape ensures analysis reproducibility, allowing the force to be applied at the same sample point (ISO 37:2017) [41]. The dog-bone-shaped scaffolds were subjected to an axial tensile test at room temperature (25 ± 3 • C) using a loading velocity of 5 mm/min, according to the literature, for soft tissue characterization [42,43]. The Young's modulus (E) was computed as the slope of the stress-strain curve and compared with previously produced hybrid scaffolds [36].…”

Shape or size matters? Towards standard reporting of tensile testing parameters for human soft tissues: systematic review and finite element analysis

“…In mechanical testing of soft tissues samples, sample geometries (e.g., sample shape and dimensions, especially in the middle gauge section) are known to influence the stress-strain distribution throughout a sample ( Fung, 1993 ; Anssari-Benam et al, 2012b ; Innocenti et al, 2017 ; Fischer et al, 2020 ; Wale et al, 2021 ). For tensile testing, evaluation of the stress-strain state in the gauge section is relevant, since the intrinsic material properties can be derived based on the assumption of a homogenous, uniaxial stress-strain state in this area, with the most dominant stress anticipated in the loading direction.…”

“…For biological soft-tissue samples, however, such standards do not exist. As a result, highly diverse experimental setups have been reported, which can contribute to systematic bias (Innocenti et al, 2017;Zwirner et al, 2020b;Fischer et al, 2020;Wale et al, 2021;Chanda and Singh, 2023).…”