Prediction of femoral strength using 3D finite element models reconstructed from DXA images: validation against experiments

Computed tomography (CT)-based finite element (FE) models may improve the current osteoporosis diagnostics and prediction of fracture risk by providing an estimate for femoral strength. However, the need for a CT scan, as opposed to the conventional use of dual-energy X-ray absorptiometry (DXA) for osteoporosis diagnostics, is considered a major obstacle. The 3D shape and bone mineral density (BMD) distribution of a femur can be reconstructed using a statistical shape and appearance model (SSAM) and the DXA image of the femur. Then, the reconstructed shape and BMD could be used to build FE models to predict bone strength. Since high accuracy is needed in all steps of the analysis, this study aimed at evaluating the ability of a 3D FE model built from one 2D DXA image to predict the strains and fracture load of human femora. Three cadaver femora were retrieved, for which experimental measurements from ex vivo mechanical tests were available. FE models were built using the SSAM-based reconstructions: using only the SSAM-reconstructed shape, only the SSAM-reconstructed BMD distribution, and the full SSAM-based reconstruction (including both shape and BMD distribution). When compared with experimental data, the SSAM-based models predicted accurately principal strains (coefficient of determination >0.83, normalized root-mean-square error <16%) and femoral strength (standard error of the estimate 1215 N). These results were only slightly inferior to those obtained with CT-based FE models, but with the considerable advantage of the models being built from DXA images. In summary, the results support the feasibility of SSAM-based models as a practical tool to introduce FE-based bone strength estimation in the current fracture risk diagnostics

How accurately can subject-specific finite element models predict strains and strength of human femora? Investigation using full-field measurements

Subject-specific finite element models have been proposed as a tool to improve fracture risk assessment in individuals. A thorough laboratory validation against experimental data is required before introducing such models in clinical practice. Results from digital image correlation can provide full-field strain distribution over the specimen surface during in vitro test, instead of at a few pre-defined locations as with strain gauges. The aim of this study was to validate finite element models of human femora against experimental data from three cadaver femora, both in terms of femoral strength and of the full-field strain distribution collected with digital image correlation. The results showed a high accuracy between predicted and measured principal strains (R2=0.93, RMSE=10%, 1600 validated data points per specimen). Femoral strength was predicted using a rate dependent material model with specific strain limit values for yield and failure. This provided an accurate prediction (<2% error) for two out of three specimens. In the third specimen, an accidental change in the boundary conditions occurred during the experiment, which compromised the femoral strength validation. The achieved strain accuracy was comparable to that obtained in state-of-the-art studies which validated their prediction accuracy against 10–16 strain gauge measurements. Fracture force was accurately predicted, with the predicted failure location being very close to the experimental fracture rim. Despite the low sample size and the single loading condition tested, the present combined numerical-experimental method showed that finite element models can predict femoral strength by providing a thorough description of the local bone mechanical response

Generation of 3D shape, density, cortical thickness and finite element mesh of proximal femur from a DXA image

Areal bone mineral density (aBMD), as measured by dual-energy X-ray absorptiometry (DXA), predicts hip fracture risk only moderately. Simulation of bone mechanics based on DXA imaging of the proximal femur, may help to improve the prediction accuracy. Therefore, we collected three (1-3) image sets, including CT images and DXA images of 34 proximal cadaver femurs (set1, including 30 males, 4 females), 35 clinical patient CT images of the hip (set 2, including 27 males, 8 females) and both CT and DXA images of clinical patients (set 3, including 12 female patients). All CT images were segmented manually and landmarks were placed on both femurs and pelvises. Two separate statistical appearance models (SAMs) were built using the CT images of the femurs and pelvises in sets 1 and 2, respectively. The 3D shape of the femur was reconstructed from the DXA image by matching the SAMs with the DXA images. The orientation and modes of variation of the SAMs were adjusted to minimize the sum of the absolute differences between the projection of the SAMs and a DXA image. The mesh quality and the location of the SAMs with respect to the manually placed control points on the DXA image were used as additional constraints. Then, finite element (FE) models were built from the reconstructed shapes. Mean point-to-surface distance between the reconstructed shape and CT image was 1.0mm for cadaver femurs in set 1 (leave-one-out test) and 1.4mm for clinical subjects in set 3. The reconstructed volumetric BMD showed a mean absolute difference of 140 and 185mg/cm3 for set 1 and set 3 respectively. The generation of the SAM and the limitation of using only one 2D image were found to be the most significant sources of errors in the shape reconstruction. The noise in the DXA images had only small effect on the accuracy of the shape reconstruction. DXA-based FE simulation was able to explain 85% of the CT-predicted strength of the femur in stance loading. The present method can be used to accurately reconstruct the 3D shape and internal density of the femur from 2D DXA images. This may help to derive new information from clinical DXA images by producing patient-specific FE models for mechanical simulation of femoral bone mechanics

Evaluation of composition and mineral structure of callus tissue in rat femoral fracture.

ABSTRACT. Callus formation is a critical step for successful fracture healing. Little is known about the molecular composition and mineral structure of the newly formed tissue in the callus. The aim was to evaluate the feasibility of small angle x-ray scattering (SAXS) to assess mineral structure of callus and cortical bone and if it could provide complementary information with the compositional analyses from Fourier transform infrared (FTIR) microspectroscopy. Femurs of 12 male Sprague-Dawley rats at 9 weeks of age were fractured and fixed with an intramedullary 1.1 mm K-wire. Fractures were treated with the combinations of bone morphogenetic protein-7 and/or zoledronate. Rats were sacrificed after 6 weeks and both femurs were prepared for FTIR and SAXS analysis. Significant differences were found in the molecular composition and mineral structure between the fracture callus, fracture cortex, and control cortex. The degree of mineralization, collagen maturity, and degree of orientation of the mineral plates were lower in the callus tissue than in the cortices. The results indicate the feasibility of SAXS in the investigation of mineral structure of bone fracture callus and provide complementary information with the composition analyzed with FTIR. Moreover, this study contributes to the limited FTIR and SAXS data in the field

Bone Loss Rate May Interact with Other Risk Factors for Fractures among Elderly Women: A 15-Year Population-Based Study

Aim was to investigate fracture risk (FR) according to bone loss (BL) rate. A random sample of 1652 women aged 53.5 years was measured with dual X-ray absorptiometry in femoral neck in 1989 and 1994 and divided into tertiles of annual BL rate: high >0.84%, moderate 0.13%–0.84%, and low <0.13%. Low trauma energy fractures during following 10 years were recorded. There were no differences in FR between BL tertiles in Cox regression model. Factors predicting lower FR in Cox model were in high tertile: high T-score (HR 0.71; 95% CI 0.54–0.93, P = .012), no sister's fracture (HR 0.35; 0.19–0.64, P = .001), no mother's fracture (HR 0.52; 0.31–0.88, P = .015), in moderate tertile: high T-score (HR 0.69;0.53–0.91, P = .008) and good grip strength (HR 0.98; 0.97–0.99, P = .022). In low tertile there were no predictors for FR. BL predicted FR in women with mother's fracture in univariate and multivariate model (OR 2.6; 1.15–5.7, P = .021) but with sister's fracture this was observed only in multivariate model (OR 2.66; 1.09–6.7, P = .039). Accordingly, the risk factors for postmenopausal fractures, especially mother's fracture, may interact with BL

In Vivo Evaluation of the Potential of High-Frequency Ultrasound for Arthroscopic Examination of the Shoulder Joint

Objective. Accurate arthroscopic evaluation of cartilage lesions could significantly improve the outcome of repair surgery. In this study, we investigated for the first time the potential of intra-articular ultrasound as an arthroscopic tool for grading cartilage defects in the human shoulder joint in vivo and compared the outcome to results from arthroscopic evaluation and magnetic resonance imaging findings. Design. A total of 26 sites from 9 patients undergoing routine shoulder arthroscopy were quantitatively evaluated with a clinical intravascular (40MHz) ultrasound imaging system, using the regular arthroscopy portals. Reflection coefficient (R), integrated reflection coefficient (IRC), apparent integrated backscattering (AIB), and ultrasound roughness index (URI) were calculated, and high-resolution ultrasound images were obtained per site. Each site was visually graded according to the International Cartilage Repair Society (ICRS) system. "Ultrasound scores" corresponding to the ICRS system were determined from the ultrasound images. Magnetic resonance imaging was conducted and cartilage integrity at each site was classified into 5 grades (0 = normal, 4 = severely abnormal) by a radiologist. Results. R and IRC were lower at sites with damaged cartilage surface (P = 0.033 and P = 0.043, respectively) and correlated with arthroscopic ICRS grades (r (s) = -0.444, P = 0.023 and r (s) = -0.426, P = 0.03, respectively). Arthroscopic ICRS grades and ultrasound scores were significantly correlated (rs = 0.472, P = 0.015), but no significant correlation was found between magnetic resonance imaging data and other parameters. Conclusion. The results suggest that ultrasound arthroscopy could facilitate quantitative clinical appraisal of articular cartilage integrity in the shoulder joint and provide information on cartilage lesion depth and severity for quantitative diagnostics in surgery.Peer reviewe

Application of optical coherence tomography enhances reproducibility of arthroscopic evaluation of equine joints

Peer reviewe

Contrast-Enhanced Computed Tomography Enables Quantitative Evaluation of Tissue Properties at Intrajoint Regions in Cadaveric Knee Cartilage

Objective: The aim of this study was to investigate whether the concentration of the anionic contrast agent ioxaglate, as quantitated by contrast-enhanced computed tomography (CECT) using a clinical cone-beam CT (CBCT) instrument, reflects biochemical, histological, and biomechanical characteristics of articular cartilage imaged in an ex vivo, intact human knee joint. Design: An osteoarthritic human cadaveric knee joint (91 years old) was injected with ioxaglate (36 mg I/mL) and imaged using CBCT over 61 hours of ioxaglate diffusion into cartilage. Following imaging, the joint surfaces were excised, rinsed to remove contrast agent, and compressive stiffness (equilibrium and instantaneous compressive moduli) was measured via indentation testing (n = 17 sites). Each site was sectioned for histology and assessed for glycosaminoglycan content using digital densitometry of Safranin-O stained sections, Fourier transform infrared spectroscopy for collagen content, and morphology using both the Mankin and OARSI semiquantitative scoring systems. Water content was determined using mass change after lyophilization. Results: CECT attenuation at all imaging time points, including those <1 hour of ioxaglate exposure, correlated significantly (P < 0.05) with cartilage water and glycosaminoglycan contents, Mankin score, and both equilibrium and instantaneous compressive moduli. Early time points (<30 minutes) also correlated (P < 0.05) with collagen content and OARSI score. Differences in cartilage quality between intrajoint regions were distinguishable at diffusion equilibrium and after brief ioxaglate exposure. Conclusions: CECT with ioxaglate affords biochemical and biomechanical measurements of cartilage health and performance even after short, clinically relevant exposure times, and may be useful in the clinic as a means for detecting early signs of cartilage pathology

Fourier Transform Infrared Spectroscopic Imaging and Multivariate Regression for Prediction of Proteoglycan Content of Articular Cartilage

Fourier Transform Infrared (FT-IR) spectroscopic imaging has been earlier applied for the spatial estimation of the collagen and the proteoglycan (PG) contents of articular cartilage (AC). However, earlier studies have been limited to the use of univariate analysis techniques. Current analysis methods lack the needed specificity for collagen and PGs. The aim of the present study was to evaluate the suitability of partial least squares regression (PLSR) and principal component regression (PCR) methods for the analysis of the PG content of AC. Multivariate regression models were compared with earlier used univariate methods and tested with a sample material consisting of healthy and enzymatically degraded steer AC. Chondroitinase ABC enzyme was used to increase the variation in PG content levels as compared to intact AC. Digital densitometric measurements of Safranin O –stained sections provided the reference for PG content. The results showed that multivariate regression models predict PG content of AC significantly better than earlier used absorbance spectrum (i.e. the area of carbohydrate region with or without amide I normalization) or second derivative spectrum univariate parameters. Increased molecular specificity favours the use of multivariate regression models, but they require more knowledge of chemometric analysis and extended laboratory resources for gathering reference data for establishing the models. When true molecular specificity is required, the multivariate models should be used