Assessment of Transverse Isotropy in Clinical-Level CT Images of Trabecular Bone Using the Gradient Structure Tensor

A simple projection method to correlate the principal mechanical direction with the principal microstructural direction of human osteoporotic femoral heads

The mechanics of the trabecular bone is related to its structure; this work aimed to propose a simple projection method to clarify the correlation between the principal mechanical direction (PMD) and the principal microstructural direction (PMSD) of trabecular bones from osteoporotic femoral heads. A total of 529 trabecular cubes were cropped from five osteoporotic femoral heads. The micro computed tomography (μCT) sequential images of each cube were first projected onto the three Cartesian coordinate planes to have three overlapped images, and the trabecular orientation distribution in the three images was analyzed. The PMSD corresponding to the greatest distribution frequency of the trabecular orientation in the three images was defined. Then, the voxel finite element (FE) models of the cubes were reconstructed and simulated to obtain their compliance matrices, and the matrices were subjected to transversal rotation to find their maximum elastic constants. The PMD corresponding to the maximum elastic constant was defined. Subsequently, the correlation of the defined PMSD and PMD was analyzed. The results showed that PMSD and PMD of the trabecular cubes did not show a significant difference at the xy- and yz-planes except that at the zx-plane. Despite this, the mean PMSD-PMD deviations at the three coordinate planes were close to 0°, and the PMSD-PMD fitting to the line PMSD = PMD demonstrated their high correlation. This study might be helpful to identify the loading direction of anisotropic trabecular bones in experiments by examining the PMSD and also to guide bone scaffold design for bone tissue repair.

Bone mineral density modeling via random field: Normality, stationarity, sex and age dependence

BACKGROUND AND OBJECTIVE

Capturing the population variability of bone properties is of paramount importance to biomedical engineering. The aim of the present paper is to describe variability and correlations in bone mineral density with a spatial random field inferred from routine computed tomography data.

METHODS

Random fields were simulated by transforming pairwise uncorrelated Gaussian random variables into correlated variables through the spectral decomposition of an age-detrended correlation matrix. The validity of the random field model was demonstrated in the spatiotemporal analysis of bone mineral density. The similarity between the computed tomography samples and those generated via random fields was analyzed with the energy distance metric.

RESULTS

The random field of bone mineral density was found to be approximately Gaussian/slightly left-skewed/strongly right-skewed at various locations. However, average bone density could be simulated well with the proposed Gaussian random field for which the energy distance, i.e., a measure that quantifies discrepancies between two distribution functions, is convergent with respect to the number of correlation eigenpairs.

CONCLUSIONS

The proposed random field model allows the enhancement of computational biomechanical models with variability in bone mineral density, which could increase the usability of the model and provides a step forward in in-silico medicine.

Effect of fabric on the accuracy of computed tomography-based finite element analyses of the vertebra

Quantitative computed tomography (QCT)-based finite element (FE) models of the vertebra are widely used in studying spine biomechanics and mechanobiology, but their accuracy has not been fully established. Although the models typically assign material properties based only on local bone mineral density (BMD), the mechanical behavior of trabecular bone also depends on fabric. The goal of this study was to determine the effect of incorporating measurements of fabric on the accuracy of FE predictions of vertebral deformation. Accuracy was assessed by using displacement fields measured via digital volume correlation-applied to time-lapse microcomputed tomography (μCT)-as the gold standard. Two QCT-based FE models were generated from human L1 vertebrae (n = 11): the entire vertebral body and a cuboid-shaped portion of the trabecular centrum [dimensions: (20-30) × (15-20) × (15-20) mm3]. For axial compression boundary conditions, there was no difference (p = 0.40) in the accuracy of the FE-computed displacements for models using material properties based on local values of BMD versus those using material properties based on local values of fabric and volume fraction. However, when using BMD-based material properties, errors were higher for the vertebral-body models (8.4-50.1%) than cuboid models (1.5-19.6%), suggesting that these properties are inaccurate in the peripheral regions of the centrum. Errors also increased when assuming that the cuboid region experienced uniaxial loading during axial compression of the vertebra. These findings indicate that a BMD-based constitutive model is not sufficient for the peripheral region of the vertebral body when seeking accurate QCT-based FE modeling of the vertebra.

Mapping anisotropy improves QCT-based finite element estimation of hip strength in pooled stance and side-fall load configurations

Hip fractures are one of the most severe consequences of osteoporosis. Compared to the clinical standard of DXA-based aBMD at the femoral neck, QCT-based FEA delivers a better surrogate of femoral strength and gains acceptance for the calculation of hip fracture risk when a CT reconstruction is available. Isotropic, homogenised voxel-based, finite element (hvFE) models are widely used to estimate femoral strength in cross-sectional and longitudinal clinical studies. However, fabric anisotropy is a classical feature of the architecture of the proximal femur and the second determinant of the homogenised mechanical properties of trabecular bone. Due to the limited resolution, fabric anisotropy cannot be derived from clinical CT reconstructions. Alternatively, fabric anisotropy can be extracted from HR-pQCT images of cadaveric femora. In this study, fabric anisotropy from HR-pQCT images was mapped onto QCT-based hvFE models of 71 human proximal femora for which both HR-pQCT and QCT images were available. Stiffness and ultimate load computed from anisotropic hvFE models were compared with previous biomechanical tests in both stance and side-fall configurations. The influence of using the femur-specific versus a mean fabric distribution on the hvFE predictions was assessed. Femur-specific and mean fabric enhance the prediction of experimental ultimate force for the pooled, i.e. stance and side-fall, (isotropic: r²=0.81, femur-specific fabric: r²=0.88, mean fabric: r²=0.86,p<0.001) but not for the individual configurations. Fabric anisotropy significantly improves bone strength prediction for the pooled configurations, and mapped fabric provides a comparable prediction to true fabric. The mapping of fabric anisotropy is therefore expected to help generate more accurate QCT-based hvFE models of the proximal femur for personalised or multiple load configurations.

Accounting for spatial variation of trabecular anisotropy with subject-specific finite element modeling moderately improves predictions of local subchondral bone stiffness at the proximal tibia

INTRODUCTION

Previously, a finite element (FE) model of the proximal tibia was developed and validated against experimentally measured local subchondral stiffness. This model indicated modest predictions of stiffness (R²=0.77, normalized root mean squared error (RMSE%)=16.6%). Trabecular bone though was modeled with isotropic material properties despite its orthotropic anisotropy. The objective of this study was to identify the anisotropic FE modeling approach which best predicted (with largest explained variance and least amount of error) local subchondral bone stiffness at the proximal tibia.

METHODS

Local stiffness was measured at the subchondral surface of 13 medial/lateral tibial compartments using in situ macro indentation testing. An FE model of each specimen was generated assuming uniform anisotropy with 14 different combinations of cortical- and tibial-specific density-modulus relationships taken from the literature. Two FE models of each specimen were also generated which accounted for the spatial variation of trabecular bone anisotropy directly from clinical CT images using grey-level structure tensor and Cowin's fabric-elasticity equations. Stiffness was calculated using FE and compared to measured stiffness in terms of R² and RMSE%.

RESULTS

The uniform anisotropic FE model explained 53-74% of the measured stiffness variance, with RMSE% ranging from 12.4 to 245.3%. The models which accounted for spatial variation of trabecular bone anisotropy predicted 76-79% of the variance in stiffness with RMSE% being 11.2-11.5%.

CONCLUSIONS

Of the 16 evaluated finite element models in this study, the combination of Synder and Schneider (for cortical bone) and Cowin's fabric-elasticity equations (for trabecular bone) best predicted local subchondral bone stiffness.

Fast estimation of Colles' fracture load of the distal section of the radius by homogenized finite element analysis based on HR-pQCT

Fractures of the distal section of the radius (Colles' fractures) occur earlier in life than other osteoporotic fractures. Therefore, they can be interpreted as a warning signal for later, more deleterious fractures of vertebral bodies or the femoral neck. In the past decade, the advent of HR-pQCT allowed a detailed architectural analysis of the distal radius and an automated but time-consuming estimation of its strength with linear micro-finite element (μFE) analysis. Recently, a second generation of HR-pQCT scanner (XtremeCT II, SCANCO Medical, Switzerland) with a resolution beyond 61 μm became available for even more refined biomechanical investigations in vivo. This raises the question how biomechanical outcome variables compare between the original (LR) and the new (HR) scanner resolution. Accordingly, the aim of this work was to validate experimentally a patient-specific homogenized finite element (hFE) analysis of the distal section of the human radius for the fast prediction of Colles' fracture load based on the last generation HR-pQCT. Fourteen pairs of fresh frozen forearms (mean age = 77.5±9) were scanned intact using the high (61 μm) and the low (82 μm) resolution protocols that correspond to the new and original HR-pQCT systems. From each forearm, the 20mm most distal section of the radius were dissected out, scanned with μCT at 16.4 μm and tested experimentally under compression up to failure for assessment of stiffness and ultimate load. Linear and nonlinear hFE models together with linear micro finite element (μFE) models were then generated based on the μCT and HR-pQCT reconstructions to predict the aforementioned mechanical properties of 24 sections. Precision errors of the short term reproducibility of the FE analyses were measured based on the repeated scans of 12 sections. The calculated failure loads correlated strongly with those measured in the experiments: accounting for donor as a random factor, the nonlinear hFE provided a marginal coefficient of determination (R_m²) of 0.957 for the high resolution (HR) and 0.948 for the low resolution (LR) protocols, the linear hFE with R_m² of 0.957 for the HR and 0.947 for the LR protocols. Linear μFE predictions of the ultimate load were similar with an R_m² of 0.950 for the HR and 0.954 for the LR protocols, respectively. Nonlinear hFE strength computation led to precision errors of 2.2 and 2.3% which were higher than the ones calculated based on the linear hFE (1.6 and 1.9%) and linear μFE (1.2 and 1.6%) for the HR and LR protocols respectively. Computation of the fracture load with nonlinear hFE demanded in average 6h of CPU time which was 3 times faster than with linear μFE, while computation with linear hFE took only a few minutes. This study delivers an extensive experimental and numerical validation for the application of an accurate and fast hFE diagnostic tool to help in identifying individuals who may be at risk of an osteoporotic wrist fracture and to follow up pharmacological and other treatments in such patients.

μCT-based trabecular anisotropy can be reproducibly computed from HR-pQCT scans using the triangulated bone surface

The trabecular structure can be assessed at the wrist or tibia via high-resolution peripheral quantitative computed tomography (HR-pQCT). Yet on this modality, the performance of the existing methods, evaluating trabecular anisotropy is usually overlooked, especially in terms of reproducibility. We thus proposed to compare the TRI routine used by SCANCO Medical AG (Brüttisellen, Switzerland), the classical mean intercept length (MIL), and the grey-level structure tensor (GST) to the mean surface length (MSL), a new method for evaluating a second-order fabric tensor based on the triangulation of the bone surface. The distal radius of 24 fresh-frozen human forearms was scanned three times via HR-pQCT protocols (61μm, 82μm nominal voxel size), dissected, and imaged via micro computed tomography (μCT) at 16μm nominal voxel size. After registering the scans, we compared for each resolution the fabric tensors, determined by the mentioned techniques for 182 trabecular regions of interest. We then evaluated the reproducibility of the fabric information measured by HR-pQCT via precision errors. On μCT, TRI and GST were respectively the best and worst surrogates for MIL_μCT (MIL computed on μCT) in terms of eigenvalues and main direction of anisotropy. On HR-pQCT, however, MSL provided the best approximation of MIL_μCT. Surprisingly, surface-based approaches (TRI, MSL) also proved to be more precise than both MIL and GST. Our findings confirm that MSL can reproducibly estimate MIL_μCT, the current gold standard. MSL thus enables the direct mapping of the fabric-dependent material properties required in homogenised HR-pQCT-based finite element models.

Optimizing finite element predictions of local subchondral bone structural stiffness using neural network-derived density-modulus relationships for proximal tibial subchondral cortical and trabecular bone

BACKGROUND

Quantitative computed tomography based subject-specific finite element modeling has potential to clarify the role of subchondral bone alterations in knee osteoarthritis initiation, progression, and pain. However, it is unclear what density-modulus equation(s) should be applied with subchondral cortical and subchondral trabecular bone when constructing finite element models of the tibia. Using a novel approach applying neural networks, optimization, and back-calculation against in situ experimental testing results, the objective of this study was to identify subchondral-specific equations that optimized finite element predictions of local structural stiffness at the proximal tibial subchondral surface.

METHODS

Thirteen proximal tibial compartments were imaged via quantitative computed tomography. Imaged bone mineral density was converted to elastic moduli using multiple density-modulus equations (93 total variations) then mapped to corresponding finite element models. For each variation, root mean squared error was calculated between finite element prediction and in situ measured stiffness at 47 indentation sites. Resulting errors were used to train an artificial neural network, which provided an unlimited number of model variations, with corresponding error, for predicting stiffness at the subchondral bone surface. Nelder-Mead optimization was used to identify optimum density-modulus equations for predicting stiffness.

FINDINGS

Finite element modeling predicted 81% of experimental stiffness variance (with 10.5% error) using optimized equations for subchondral cortical and trabecular bone differentiated with a 0.5g/cm³ density.

INTERPRETATION

In comparison with published density-modulus relationships, optimized equations offered improved predictions of local subchondral structural stiffness. Further research is needed with anisotropy inclusion, a smaller voxel size and de-blurring algorithms to improve predictions.

Head-Neck Osteoplasty has Minor Effect on the Strength of an Ovine Cam-FAI Model: In Vitro and Finite Element Analyses

BACKGROUND

Osteochondroplasty of the head-neck region is performed on patients with cam femoroacetabular impingement (FAI) without fully understanding its repercussion on the integrity of the femur. Cam-type FAI can be surgically and reproducibly induced in the ovine femur, which makes it suitable for studying corrective surgery in a consistent way. Finite element models built on quantitative CT (QCT) are computer tools that can be used to predict femoral strength and evaluate the mechanical effect of surgical correction.

QUESTIONS/PURPOSES

We asked: (1) What is the effect of a resection of the superolateral aspect of the ovine femoral head-neck junction on failure load? (2) How does the failure load after osteochondroplasty compare with reported forces from activities of daily living in sheep? (3) How do failure loads and failure locations from the computer simulations compare with the experiments?

METHODS

Osteochondroplasties (3, 6, 9 mm) were performed on one side of 18 ovine femoral pairs with the contralateral intact side as a control. The 36 femurs were scanned via QCT from which specimen-specific computer models were built. Destructive compression tests then were conducted experimentally using a servohydraulic testing system and numerically via the computer models. Safety factors were calculated as the ratio of the maximal force measured in vivo by telemeterized hip implants during the sheep's walking and running activities to the failure load. The simulated failure loads and failure locations from the computer models were compared with the experimental results.

RESULTS

Failure loads were reduced by 5% (95% CI, 2%-8%) for the 3-mm group (p = 0.0089), 10% (95% CI, 6%-14%) for the 6-mm group (p = 0.0015), and 19% (95% CI, 13%-26%) for the 9-mm group (p = 0.0097) compared with the controls. Yet, the weakest specimen still supported more than 2.4 times the peak load during running. Strong correspondence was found between the simulated and experimental failure loads (R² = 0.83; p < 0.001) and failure locations.

CONCLUSIONS

The resistance of ovine femurs to fracture decreased with deeper resections. However, under in vitro testing conditions, the effect on femoral strength remains small even after 9 mm correction, suggesting that femoral head-neck osteochondroplasty could be done safely on the ovine femur. QCT-based finite element models were able to predict weakening of the femur resulting from the osteochondroplasty.

CLINICAL RELEVANCE

The ovine femur provides a seemingly safe platform for scientific evaluation of FAI. It also appears that computer models based on preoperative CT scans may have the potential to provide patient-specific guidelines for preventing overcorrection of cam FAI.

Biomechanical Role of Bone Anisotropy Estimated on Clinical CT Scans by Image Registration

Image-based modeling is a popular approach to perform patient-specific biomechanical simulations. Accurate modeling is critical for orthopedic application to evaluate implant design and surgical planning. It has been shown that bone strength can be estimated from the bone mineral density (BMD) and trabecular bone architecture. However, these findings cannot be directly and fully transferred to patient-specific modeling since only BMD can be derived from clinical CT. Therefore, the objective of this study was to propose a method to predict the trabecular bone structure using a µCT atlas and an image registration technique. The approach has been evaluated on femurs and patellae under physiological loading. The displacement and ultimate force for femurs loaded in stance position were predicted with an error of 2.5% and 3.7%, respectively, while predictions obtained with an isotropic material resulted in errors of 7.3% and 6.9%. Similar results were obtained for the patella, where the strain predicted using the registration approach resulted in an improved mean squared error compared to the isotropic model. We conclude that the registration of anisotropic information from of a single template bone enables more accurate patient-specific simulations from clinical image datasets than isotropic model.

Bone volume fraction and fabric anisotropy are better determinants of trabecular bone stiffness than other morphological variables

As our population ages, more individuals suffer from osteoporosis. This disease leads to impaired trabecular architecture and increased fracture risk. It is essential to understand how morphological and mechanical properties of the cancellous bone are related. Morphology-elasticity relationships based on bone volume fraction (BV/TV) and fabric anisotropy explain up to 98% of the variation in elastic properties. Yet, other morphological variables such as individual trabeculae segmentation (ITS) and trabecular bone score (TBS) could improve the stiffness predictions. A total of 743 micro-computed tomography (μCT) reconstructions of cubic trabecular bone samples extracted from femur, radius, vertebrae, and iliac crest were analyzed. Their morphology was assessed via 25 variables and their stiffness tensor (CFE) was computed from six independent load cases using micro finite element (μFE) analyses. Variance inflation factors were calculated to evaluate collinearity between morphological variables and decide upon their inclusion in morphology-elasticity relationships. The statistically admissible morphological variables were included in a multiple linear regression model of the dependent variable CFE. The contribution of each independent variable was evaluated (ANOVA). Our results show that BV/TV is the best determinant of CFE(r(2) adj  = 0.889), especially in combination with fabric anisotropy (r(2) adj  = 0.968). Including the other independent predictors hardly affected the amount of variance explained by the model (r(2) adj  = 0.975). Across all anatomical sites, BV/TV explained 87% of the variance of the bone elastic properties. Fabric anisotropy further described 10% of the bone stiffness, but the improvement in variance explanation by adding other independent factors was marginal (<1%). These findings confirm that BV/TV and fabric anisotropy are the best determinants of trabecular bone stiffness and show, against common belief, that other morphological variables do not bring any further contribution. These overall conclusions remain to be confirmed for specific bone diseases and postelastic properties.

To what extent can linear finite element models of human femora predict failure under stance and fall loading configurations?

Proximal femur strength estimates from computed tomography (CT)-based finite element (FE) models are finding clinical application. Published models reached a high in-vitro accuracy, yet many of them rely on nonlinear methodologies or internal best-fitting of parameters. The aim of the present study is to verify to what extent a linear FE modelling procedure, fully based on independently determined parameters, can predict the failure characteristics of the proximal femur in stance and sideways fall loading configurations. Fourteen fresh-frozen cadaver femora were CT-scanned. Seven femora were tested to failure in stance loading conditions, and seven in fall. Fracture was monitored with high-speed videos. Linear FE models were built from CT images according to a procedure already validated in the prediction of strains. An asymmetric maximum principal strain criterion (0.73% tensile, 1.04% compressive limit) was used to define a node-based risk factor (RF). FE-predicted failure load, mode (tensile/compressive) and location were determined from the first node reaching RF=1. FE-predicted and measured failure loads were highly correlated (R(2)=0.89, SEE=814N). In all specimens, FE models correctly identified the failure mode (tensile in stance, compressive in fall) and the femoral region where fracture started (supero-lateral neck aspect). The location of failure onset was accurately predicted in eight specimens. In summary, a simple FE model, adaptable in the future to multiple loads (e.g. including muscles), was highly correlated with experimental failure in two loading conditions on specimens ranging from normal to osteoporotic. Thus, it can be suitable for use in clinical studies.