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In large animal studies, the mechanical reintegration of the bone fragments is measured using postmortem physical testing, but these assessments can only be performed once, after sacrifice. Image‐based virtual mechanical testing is an attractive alternative because it could be used to monitor healing longitudinally. However, the procedures and software required to perform finite element analysis (FEA) on subject‐specific models for virtual mechanical testing can be time consuming and costly. Accordingly, the goal of this study was to determine whether a simpler image‐based geometric measure—the torsion constant, sometimes known as polar moment of inertia—can be reliably used as a surrogate measure of bone healing in large animals. To achieve this, postmortem biomechanical testing and microCT scans were analyzed for a total of 33 operated and 20 intact ovine tibiae. An image‐processing procedure to compute the attenuation‐weighted torsion constant from the microCT scans was developed in MATLAB and this code has been made freely available. Linear regression analysis was performed between the postmortem biomechanical data, the results of virtual mechanical testing using FEA, and the torsion constants measured from the scans. The results showed that virtual mechanical testing is the most reliable surrogate measure of postmortem torsional rigidity, having strong correlations and high absolute agreement. However, when FEA is not practical, the torsion constant is a viable alternative surrogate measure that is moderately correlated with postmortem torsional rigidity and can be readily calculated. 

In image-based finite element analysis of bone, partial volume effects (PVEs) arise from image blur at tissue boundaries and as a byproduct of geometric reconstruction and meshing during model creation. In this study, we developed and validated a material assignment approach to mitigate partial volume effects. Our validation data consisted of physical torsion testing of intact tibiae from N = 20 Swiss alpine sheep. We created finite element models from micro-CT scans of these tibiae using three popular element types (10-node tetrahedral, 8-node hexahedral, and 20-node hexahedral). Without partial volume management, the models over-predicted the torsional rigidity compared to physical biomechanical tests. To address this problem, we implemented a dual-zone material model to treat elements that overlap low-density surface voxels as soft tissue rather than bone. After in situ inverse optimization, the dual-zone material model produced strong correlations and high absolute agreement between the virtual and physical tests. This suggests that with appropriate partial volume management, virtual mechanical testing can be a reliable surrogate for physical biomechanical testing. For maximum flexibility in partial volume management regardless of element type, we recommend the use of the following dual-zone material model for ovine tibiae: soft-tissue cutoff density of 665 mgHA/cm3 with a soft tissue modulus of 50 MPa (below cutoff) and a density-modulus conversion slope of 10,225 MPa-cm3/mgHA for bone (above cutoff). 

As scientific advancements continue to reshape the world, it becomes increasingly crucial to uphold ethical standards and minimize the potentially adverse impact of research activities. In this context, the implementation of the 3R principles—Replacement, Reduction, and Refinement—has emerged as a prominent framework for promoting ethical research practices in the use of animals. This article aims to explore recent advances in integrating the 3R principles into fracture healing research, highlighting their potential to enhance animal welfare, scientific validity, and societal trust. The review focuses on in vitro, in silico, ex vivo, and refined in vivo methods, which have the potential to replace, reduce, and refine animal experiments in musculoskeletal, bone, and fracture healing research. Here, we review material that was presented at the workshop “Implementing 3R Principles into Fracture Healing Research” at the 2023 Orthopedic Research Society (ORS) Annual Meeting in Dallas, Texas.

 

Bone fractures commonly repair by forming a bridging structure called callus, which begins as soft tissue and gradually ossifies to restore rigidity to the bone. Virtual mechanical testing is a promising technique for image-based assessment of structural bone healing in both preclinical and clinical settings, but its accuracy depends on the validity of the material model used to assign tissue mechanical properties. The goal of this study was to develop a constitutive model for callus that captures the heterogeneity and biomechanical duality of the callus, which contains both soft tissue and woven bone. To achieve this, a large-scale optimization analysis was performed on 2363 variations of 3D finite element models derived from computed tomography (CT) scans of 33 osteotomized sheep under normal and delayed healing conditions. A piecewise material model was identified that produced high absolute agreement between virtual and physical tests by differentiating between soft and hard callus based on radiodensity. The results showed that the structural integrity of a healing long bone is conferred by an internal architecture of mineralized hard callus that is supported by interstitial soft tissue. These findings suggest that with appropriate material modeling, virtual mechanical testing is a reliable surrogate for physical biomechanical testing.

 

Bone healing has been traditionally described as a four-phase process: inflammatory response, soft callus formation, hard callus development, and remodeling. The remodeling phase has been largely neglected in most numerical mechanoregulation models of fracture repair in favor of capturing early healing using a pre-defined callus domain. However, in vivo evidence suggests that remodeling occurs concurrently with repair and causes changes in cortical bone adjacent to callus that are typically neglected in numerical models of bone healing. The objective of this study was to use image processing techniques to quantify this early-stage remodeling in ovine osteotomies. To accomplish this, we developed a numerical method for radiodensity profilometry with optimization-based curve fitting to mathematically model the bone density gradients in the radial direction across the cortical wall and callus. After assessing data from 26 sheep, we defined a dimensionless density fitting function that revealed significant remodeling occurring in the cortical wall adjacent to callus during early healing, a 23% average reduction in density compared to intact. This fitting function is robust for modeling radial density gradients in both intact bone and fracture repair scenarios and can capture a wide variety of the healing responses. The fitting function can also be scaled easily for comparison to numerical model predictions and may be useful for validating future mechanoregulatory models of coupled fracture repair and remodeling.