Non-invasive prediction of the mouse tibia mechanical properties from microCT images: comparison between different finite element models

Combining PTH(1-34) and mechanical loading has increased benefit to tibia bone mechanics in ovariectomised mice

Combined treatment with PTH(1-34) and mechanical loading confers increased structural benefits to bone than monotherapies. However, it remains unclear how this longitudinal adaptation affects the bone mechanics. This study quantified the individual and combined longitudinal effects of PTH(1-34) and mechanical loading on the bone stiffness and strength evaluated in vivo with validated micro-finite element (microFE) models. C57BL/6 mice were ovariectomised at 14-week-old and treated either with injections of PTH(1-34), compressive tibia loading or both interventions concurrently. Right tibiae were in vivo microCT-scanned every 2 weeks from 14 until 24-week-old. MicroCT images were rigidly registered to reference tibia and the cortical organ level (whole bone) and tissue level (midshaft) morphometric properties and bone mineral content were quantified. MicroCT images were converted into voxel-based homogeneous, linear elastic microFE models to estimate the bone stiffness and strength. This approach allowed us for the first time to quantify the longitudinal changes in mechanical properties induced by combined treatments in a model of accelerated bone resorption. Both changes of stiffness and strength were higher with co-treatment than with individual therapies, consistent with increased benefits with the tibia bone mineral content and cortical area, properties strongly associated with the tibia mechanics. The longitudinal data shows that the two bone anabolics, both individually and combined, had persistent benefit on estimated mechanical properties, and that benefits (increased stiffness and strength) remained after treatment was withdrawn.

The loading direction dramatically affects the mechanical properties of the mouse tibia

Introduction: The in vivo tibial loading mouse model has been extensively used to evaluate bone adaptation in the tibia after mechanical loading treatment. However, there is a prevailing assumption that the load is applied axially to the tibia. The aim of this in silico study was to evaluate how much the apparent mechanical properties of the mouse tibia are affected by the loading direction, by using a validated micro-finite element (micro-FE) model of mice which have been ovariectomized and exposed to external mechanical loading over a two-week period. Methods: Longitudinal micro-computed tomography (micro-CT) images were taken of the tibiae of eleven ovariectomized mice at ages 18 and 20 weeks. Six of the mice underwent a mechanical loading treatment at age 19 weeks. Micro-FE models were generated, based on the segmented micro-CT images. Three models using unitary loads were linearly combined to simulate a range of loading directions, generated as a function of the angle from the inferior-superior axis (θ, 0°-30° range, 5° steps) and the angle from the anterior-posterior axis (ϕ, 0°: anterior axis, positive anticlockwise, 0°-355° range, 5° steps). The minimum principal strain was calculated and used to estimate the failure load, by linearly scaling the strain until 10% of the nodes reached the critical strain level of -14,420 με. The apparent bone stiffness was calculated as the ratio between the axial applied force and the average displacement along the longitudinal direction, for the loaded nodes. Results: The results demonstrated a high sensitivity of the mouse tibia to the loading direction across all groups and time points. Higher failure loads were found for several loading directions (θ = 10°, ϕ 205°-210°) than for the nominal axial case (θ = 0°, ϕ = 0°), highlighting adaptation of the bone for loading directions far from the nominal axial one. Conclusion: These results suggest that in studies which use mouse tibia, the loading direction can significantly impact the failure load. Thus, the magnitude and direction of the applied load should be well controlled during the experiments.

The Effect of Omega-9 on Bone Viscoelasticity and Strength in an Ovariectomized Diet-Fed Murine Model

Few studies have investigated the effect of a monosaturated diet high in ω-9 on osteoporosis. We hypothesized that omega-9 (ω-9) protects ovariectomized (OVX) mice from a decline in bone microarchitecture, tissue loss, and mechanical strength, thereby serving as a modifiable dietary intervention against osteoporotic deterioration. Female C57BL/6J mice were assigned to sham-ovariectomy, ovariectomy, or ovariectomy + estradiol treatment prior to switching their feed to a diet high in ω-9 for 12 weeks. Tibiae were evaluated using DMA, 3-point-bending, histomorphometry, and microCT. A significant decrease in lean mass (p = 0.05), tibial area (p = 0.009), and cross-sectional moment of inertia (p = 0.028) was measured in OVX mice compared to the control. A trend was seen where OVX bone displayed increased elastic modulus, ductility, storage modulus, and loss modulus, suggesting the ω-9 diet paradoxically increased both stiffness and viscosity. This implies beneficial alterations on the macro-structural, and micro-tissue level in OVX bone, potentially decreasing the fracture risk. Supporting this, no significant differences in ultimate, fracture, and yield stresses were measured. A diet high in ω-9 did not prevent microarchitectural deterioration, nevertheless, healthy tibial strength and resistance to fracture was maintained via mechanisms independent of bone structure/shape. Further investigation of ω-9 as a therapeutic in osteoporosis is warranted.

Using Finite Element Modeling in Bone Mechanoadaptation

PURPOSE OF THE REVIEW

Bone adapts structure and material properties in response to its mechanical environment, a process called mechanoadpatation. For the past 50 years, finite element modeling has been used to investigate the relationships between bone geometry, material properties, and mechanical loading conditions. This review examines how we use finite element modeling in the context of bone mechanoadpatation.

RECENT FINDINGS

Finite element models estimate complex mechanical stimuli at the tissue and cellular levels, help explain experimental results, and inform the design of loading protocols and prosthetics. FE modeling is a powerful tool to study bone adaptation as it complements experimental approaches. Before using FE models, researchers should determine whether simulation results will provide complementary information to experimental or clinical observations and should establish the level of complexity required. As imaging technics and computational capacity continue increasing, we expect FE models to help in designing treatments of bone pathologies that take advantage of mechanoadaptation of bone.

Mechanoregulation may drive osteolysis during bone metastasis: A finite element analysis of the mechanical environment within bone tissue during bone metastasis and osteolytic resorption

Metastatic bone disease occurs in 70-80% of advanced breast cancer patients and bone tissue is accepted to have attractive physical properties that facilitate cancer cell attraction, adhesion, and invasion. Bone cells also facilitate tumour invasion by biochemical signalling and through resorption of the bone matrix (osteolysis), which releases factors that further stimulate tumour cell activity. The evolving mechanical environment during tumour invasion might play an important role in these processes, as the activity of both bone and cancer cells is regulated by mechanical cues. In particular bone loss and altered mineralisation have been reported, yet how these alter the mechanical environment local to bone and tumour cells is unknown. The objective of this study is to quantify changes in the mechanical environment within bone tissue, during bone metastasis and osteolytic resorption, using finite element analysis (FEA) models reconstructed from high-resolution μCT images of metastatic mouse bone. In particular, we quantify time-dependent changes in mechanical stimuli, local to and distant from an invading tumour mass, to investigate putative mechanobiological cues for osteolysis during bone metastasis. We report here that in early metastasis (3 weeks after tumour inoculation), there was a decrease in strain distribution within the proximal femur trabecular and distal cortical bone tissue. These changes in the mechanical environment preceded extensive osteolytic destruction, but coincided with the onset of early osteolysis, cortical thickening and mineralisation of proximal and distal femur bone. We propose that early changes in the mechanical environment within bone tissue may activate resorption by osteoclast cells and thereby contribute to the extensive osteolytic bone loss at later stage (6 weeks) bone metastasis.

Mechanostat parameters estimated from time-lapsed in vivo micro-computed tomography data of mechanically driven bone adaptation are logarithmically dependent on loading frequency

Mechanical loading is a key factor governing bone adaptation. Both preclinical and clinical studies have demonstrated its effects on bone tissue, which were also notably predicted in the mechanostat theory. Indeed, existing methods to quantify bone mechanoregulation have successfully associated the frequency of (re)modeling events with local mechanical signals, combining time-lapsed in vivo micro-computed tomography (micro-CT) imaging and micro-finite element (micro-FE) analysis. However, a correlation between the local surface velocity of (re)modeling events and mechanical signals has not been shown. As many degenerative bone diseases have also been linked to impaired bone (re)modeling, this relationship could provide an advantage in detecting the effects of such conditions and advance our understanding of the underlying mechanisms. Therefore, in this study, we introduce a novel method to estimate (re)modeling velocity curves from time-lapsed in vivo mouse caudal vertebrae data under static and cyclic mechanical loading. These curves can be fitted with piecewise linear functions as proposed in the mechanostat theory. Accordingly, new (re)modeling parameters can be derived from such data, including formation saturation levels, resorption velocity moduli, and (re)modeling thresholds. Our results revealed that the norm of the gradient of strain energy density yielded the highest accuracy in quantifying mechanoregulation data using micro-finite element analysis with homogeneous material properties, while effective strain was the best predictor for micro-finite element analysis with heterogeneous material properties. Furthermore, (re)modeling velocity curves could be accurately described with piecewise linear and hyperbola functions (root mean square error below 0.2 µm/day for weekly analysis), and several (re)modeling parameters determined from these curves followed a logarithmic relationship with loading frequency. Crucially, (re)modeling velocity curves and derived parameters could detect differences in mechanically driven bone adaptation, which complemented previous results showing a logarithmic relationship between loading frequency and net change in bone volume fraction over 4 weeks. Together, we expect this data to support the calibration of in silico models of bone adaptation and the characterization of the effects of mechanical loading and pharmaceutical treatment interventions in vivo.

Contribution to the 3R Principle: Description of a Specimen-Specific Finite Element Model Simulating 3-Point-Bending Tests in Mouse Tibiae

Bone mechanical properties are classically determined by biomechanical tests, which normally destroy the bones and disable further histological or molecular analyses. Thus, obtaining biomechanical data from bone usually requires an additional group of animals within the experimental setup. Finite element models (FEMs) may non-invasively and non-destructively simulate mechanical characteristics based on material properties. The present study aimed to establish and validate an FEM to predict the mechanical properties of mice tibiae. The FEM was established based on µCT (micro-Computed Tomography) data of 16 mouse tibiae. For validating the FEM, simulated parameters were compared to biomechanical data obtained from 3-point bending tests of the identical bones. The simulated and the measured parameters correlated well for bending stiffness (R2 = 0.9104, p < 0.0001) and yield displacement (R2 = 0.9003, p < 0.0001). The FEM has the advantage that it preserves the bones’ integrity, which can then be used for other analytical methods. By eliminating the need for an additional group of animals for biomechanical tests, the established FEM can contribute to reducing the number of research animals in studies focusing on bone biomechanics. This is especially true when in vivo µCT data can be utilized where multiple bone scans can be performed with the same animal at different time points. Thus, by partially replacing biomechanical experiments, FEM simulations may reduce the overall number of animals required for an experimental setup investigating bone biomechanics, which supports the 3R (replace, reduce, and refine) principle.

Beam theory for rapid strain estimation in the mouse tibia compression model

The mouse tibia compression model is a leading model for studying bone's mechanoadaptive response to load. In studying this mechanoadaptive response, (FE) modelling is often used to determine the stress/strain within the tibia. The development of such models can be challenging and computationally expensive. An alternate approach is to use continuum mechanics based analytical theories, such as beam theory (BT). However, applying BT to the mouse tibia requires the fibula be neglected, introducing error in the stress/strain distribution. While several studies have applied BT to the mouse tibia, no study has explored the accuracy of this approach. To address these questions, this work investigates the use of BT in determining stress/strain within the mouse tibia. By comparing BT against FE modelling, it was found that BT can accurately predict tibial stress/strain if correction factors are applied to account for the effect of the fibula. The 25, 37, 50 and 75% cross sections are studied. Focusing on the 37% cross section, without correction, BT can have errors of approximately 21.6%. With correction, this is reduced to 6.6%. Such correction factors are presented. The developed BT model is applicable in the diaphysis and distal metaphysis, where the assumptions of BT are valid. This work verifies BT for determining localised strains in a mouse tibia compression model. This is anticipated to provide efficiency dividends, allowing for high throughput modelling of the mouse tibia, advancing study of bone's mechanoadaptive response.

Reproducibility of Densitometric and Biomechanical Assessment of the Mouse Tibia From In Vivo Micro-CT Images

Interventions for bone diseases (e.g. osteoporosis) require testing in animal models before clinical translation and the mouse tibia is among the most common tested anatomical sites. In vivo micro-Computed Tomography (microCT) based measurements of the geometrical and densitometric properties are non-invasive and therefore constitute an important tool in preclinical studies. Moreover, validated micro-Finite Element (microFE) models can be used for predicting the bone mechanical properties non-invasively. However, considering that the image processing pipeline requires operator-dependant steps, the reproducibility of these measurements has to be assessed. The aim of this study was to evaluate the intra- and inter-operator reproducibility of several bone parameters measured from microCT images. Ten in vivo microCT images of the right tibia of five mice (at 18 and 22 weeks of age) were processed. One experienced operator (intra-operator analysis) and three different operators (inter-operator) aligned each image to a reference through a rigid registration and selected a volume of interest below the growth plate. From each image the following parameters were measured: total bone mineral content (BMC) and density (BMD), BMC in 40 subregions (ten longitudinal sections, four quadrants), microFE-based stiffness and failure load. Intra-operator reproducibility was acceptable for all parameters (precision error, PE < 3.71%), with lowest reproducibility for stiffness (3.06% at week 18, 3.71% at week 22). The inter-operator reproducibility was slightly lower (PE < 4.25%), although still acceptable for assessing the properties of most interventions. The lowest reproducibility was found for BMC in the lateral sector at the midshaft (PE = 4.25%). Densitometric parameters were more reproducible than most standard morphometric parameters calculated in the proximal trabecular bone. In conclusion, microCT and microFE models provide reproducible measurements for non-invasive assessment of the mouse tibia properties.

MicroFE models of porcine vertebrae with induced bone focal lesions: Validation of predicted displacements with digital volume correlation

The evaluation of the local mechanical behavior as a result of metastatic lesions is fundamental for the characterization of the mechanical competence of metastatic vertebrae. Micro finite element (microFE) models have the potential of addressing this challenge through laboratory studies but their predictions of local deformation due to the complexity of the bone structure compromized by the lesion must be validated against experiments. In this study, the displacements predicted by homogeneous, linear and isotropic microFE models of vertebrae were validated against experimental Digital Volume Correlation (DVC) measurements. Porcine spine segments, with and without mechanically induced focal lesions, were tested in compression within a micro computed tomography (microCT) scanner. The displacement within the bone were measured with an optimized global DVC approach (BoneDVC). MicroFE models of the intact and lesioned vertebrae, including or excluding the growth plates, were developed from the microCT images. The microFE and DVC boundary conditions were matched. The displacements measured by the DVC and predicted by the microFE along each Cartesian direction were compared. The results showed an excellent agreement between the measured and predicted displacements, both for intact and metastatic vertebrae, in the middle of the vertebra, in those cases where the structure was not loaded beyond yield (0.69 < R² < 1.00). Models with growth plates showed the worst correlations (0.02 < R² < 0.99), while a clear improvement was observed if the growth plates were excluded (0.56 < R² < 1.00). In conclusion, these simplified models can predict complex displacement fields in the elastic regime with high reliability, more complex non-linear models should be implemented to predict regions with high deformation, when the bone is loaded beyond yield.

Positive interactions of mechanical loading and PTH treatments on spatio-temporal bone remodelling

Osteoporosis is one of the most common skeletal diseases, but current therapies are limited to generalized antiresorptive or anabolic interventions, which do not target regions that would benefit from improvements to skeletal health. To improve the evaluation of treatment plans, we used a spatio-temporal multiscale approach that combines longitudinal in vivo micro-computed tomography (micro-CT) and in silico subject-specific finite element modeling to quantitatively map bone adaptation changes due to disease and treatment at high resolution. Our findings show time and region-dependent modifications in bone remodelling following one and two sets of mechanical loading and/or pharmacological interventions. The multiscale results highlighted that the distal section was unaffected by mechanical loading alone but the proximal tibia had the greatest gain from positive interactions of combined therapies. Mechanical loading abated the catabolic effect of PTH, but the main benefit of combined treatments occurred from the additive interactions of the two therapies in periosteal apposition. These results provide detailed insight into the efficacy of combined treatments, facilitating the optimisation of dosage and treatment duration in preclinical mouse studies, and the development of novel interventions for skeletal diseases. STATEMENT OF SIGNIFICANCE: Combined mechanical loading and pharmacotherapy have the potential to slow osteoporosis-induced bone loss but current therapies do not target the regions in need of strengthening. We show for the first time spatial region-dependant interactions between PTH and mechanical loading treatment in OVX mouse tibiae, highlighting local regions in the tibia that benefitted from separate and combined treatments. Combined experimental-computational analysis also detailed the lasting period of each treatment per location in the tibia, the extent of positive (or negative) interactions of the combined therapies, and the impact of each treatment on the regulation of bone adaptation spatio-temporally. This approach can be used to create hypothesis about the interactions of different treatments to optimise the design of biomaterials and medical interventions.

The Role of the Loading Condition in Predictions of Bone Adaptation in a Mouse Tibial Loading Model

The in vivo mouse tibial loading model is used to evaluate the effectiveness of mechanical loading treatment against skeletal diseases. Although studies have correlated bone adaptation with the induced mechanical stimulus, predictions of bone remodeling remained poor, and the interaction between external and physiological loading in engendering bone changes have not been determined. The aim of this study was to determine the effect of passive mechanical loading on the strain distribution in the mouse tibia and its predictions of bone adaptation. Longitudinal micro-computed tomography (micro-CT) imaging was performed over 2 weeks of cyclic loading from weeks 18 to 22 of age, to quantify the shape change, remodeling, and changes in densitometric properties. Micro-CT based finite element analysis coupled with an optimization algorithm for bone remodeling was used to predict bone adaptation under physiological loads, nominal 12N axial load and combined nominal 12N axial load superimposed to the physiological load. The results showed that despite large differences in the strain energy density magnitudes and distributions across the tibial length, the overall accuracy of the model and the spatial match were similar for all evaluated loading conditions. Predictions of densitometric properties were most similar to the experimental data for combined loading, followed closely by physiological loading conditions, despite no significant difference between these two predicted groups. However, all predicted densitometric properties were significantly different for the 12N and the combined loading conditions. The results suggest that computational modeling of bone's adaptive response to passive mechanical loading should include the contribution of daily physiological load.