Reconstructing the 3D shape and bone mineral density distribution of the proximal femur from dual-energy X-ray absorptiometry

DXA-based 3D finite element models predict hip fractures better than areal BMD in elderly women

Bone strength is a major contributor to fracture risk. Areal bone mineral density (aBMD) obtained from dual-energy X-ray absorptiometry (DXA) is used as a surrogate for bone strength in fracture risk prediction. 3D finite element (FE) models predict bone strength better than aBMD but need 3D computed tomography and are not automated. We have earlier developed a method to automatically reconstruct the 3D hip anatomy from a 2D hip DXA image, followed by subject-specific FE-based prediction of proximal femoral strength. In this study, we evaluate the method's ability to predict incident hip fractures in a population-based cohort of women (OSTPRE). We used a sub-cohort including 46 cases with a hip fracture (<10 years from DXA scan) and 2 healthy controls to each hip fracture case, matched by age, height, and body mass index. We automatically reconstructed the 3D hip anatomy and predicted proximal femoral strength using FE analysis for all the subjects of the sub-cohort. The FE-predicted proximal femoral strength was a significantly better predictor of incident hip fractures than aBMD (difference in area under the receiver operating characteristics curve, ΔAUROC = 0.10). This is the first time that 3D FE models obtained from a 2D hip DXA scan outperform aBMD in predicting incident hip fractures in a population-based prospectively followed cohort of women. Our approach provided an improved fracture risk prediction in a clinically feasible manner (only one single DXA image is needed) and without additional costs compared to the current clinical approach.

Exercise for Postmenopausal Bone Health - Can We Raise the Bar?

PURPOSE OF REVIEW

This review summarises the latest evidence on effects of exercise on falls prevention, bone mineral density (BMD) and fragility fracture risk in postmenopausal women, explores hypotheses underpinning exercise-mediated effects on BMD and sheds light on innovative concepts to better understand and harness the skeletal benefits of exercise.

RECENT FINDINGS

Multimodal exercise programs incorporating challenging balance exercises can prevent falls. Emerging clinical trial evidence indicates supervised progressive high-intensity resistance and impact training (HiRIT) is efficacious in increasing lumbar spine BMD and is safe and well-tolerated in postmenopausal women with osteoporosis/osteopenia. There remains uncertainty regarding durability of this load-induced osteogenic response and safety in patients with recent fractures. Muscle-derived myokines and small circulating extracellular vesicles have emerged as potential sources of exercise-induced muscle-bone crosstalk but require validation in postmenopausal women. Exercise has the potential for multi-modal skeletal benefits with i) HiRIT to build bone, and ii) challenging balance exercises to prevent falls, and ultimately fractures. The therapeutic effect of such exercise in combination with osteoporosis pharmacotherapy should be considered in future trials.

A framework for three-dimensional statistical shape modeling of the proximal femur in Legg-Calvé-Perthes disease

PURPOSE

The pathomorphology of Legg-Calvé-Perthes disease (LCPD) is a key contributor to poor long-term outcomes such as hip pain, femoroacetabular impingement, and early-onset osteoarthritis. Plain radiographs, commonly used for research and in the clinic, cannot accurately represent the full extent of LCPD deformity. The purpose of this study was to develop and evaluate a methodological framework for three-dimensional (3D) statistical shape modeling (SSM) of the proximal femur in LCPD.

METHODS

We developed a framework consisting of three core steps: segmentation, surface mesh preparation, and particle-based correspondence. The framework aims to address challenges in modeling this rare condition, characterized by highly heterogeneous deformities across a wide age range and small sample sizes. We evaluated this framework by producing a SSM from clinical magnetic resonance images of 13 proximal femurs with LCPD deformity from 11 patients between the ages of six and 12 years.

RESULTS

After removing differences in scale and pose, the dominant shape modes described morphological features characteristic of LCPD, including a broad and flat femoral head, high-riding greater trochanter, and reduced neck-shaft angle. The first four shape modes were chosen for the evaluation of the model's performance, together describing 87.5% of the overall cohort variance. The SSM was generalizable to unfamiliar examples with an average point-to-point reconstruction error below 1mm. We observed strong Spearman rank correlations (up to 0.79) between some shape modes, 3D measurements of femoral head asphericity, and clinical radiographic metrics.

CONCLUSION

In this study, we present a framework, based on SSM, for the objective description of LCPD deformity in three dimensions. Our methods can accurately describe overall shape variation using a small number of parameters, and are a step toward a widely accepted, objective 3D quantification of LCPD deformity.

Ten tips on how to assess bone health in patients with chronic kidney disease

Patients with chronic kidney disease (CKD) experience a several-fold increased risk of fracture. Despite the high incidence and the associated excess morbidity and premature mortality, bone fragility in CKD, or CKD-associated osteoporosis, remains a blind spot in nephrology with an immense treatment gap. Defining the bone phenotype is a prerequisite for the appropriate therapy of CKD-associated osteoporosis at the patient level. In the present review, we suggest 10 practical 'tips and tricks' for the assessment of bone health in patients with CKD. We describe the clinical, biochemical, and radiological evaluation of bone health, alongside the benefits and limitations of the available diagnostics. A bone biopsy, the gold standard for diagnosing renal bone disease, is invasive and not widely available; although useful in complex cases, we do not consider it an essential component of bone assessment in patients with CKD-associated osteoporosis. Furthermore, we advocate for the deployment of multidisciplinary expert teams at local, national, and potentially international level. Finally, we address the knowledge gaps in the diagnosis, particularly early detection, appropriate "real-time" monitoring of bone health in this highly vulnerable population, and emerging diagnostic tools, currently primarily used in research, that may be on the horizon of clinical practice.

Bone mineral density estimation from a plain X-ray image by learning decomposition into projections of bone-segmented computed tomography

Osteoporosis is a prevalent bone disease that causes fractures in fragile bones, leading to a decline in daily living activities. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) are highly accurate for diagnosing osteoporosis; however, these modalities require special equipment and scan protocols. To frequently monitor bone health, low-cost, low-dose, and ubiquitously available diagnostic methods are highly anticipated. In this study, we aim to perform bone mineral density (BMD) estimation from a plain X-ray image for opportunistic screening, which is potentially useful for early diagnosis. Existing methods have used multi-stage approaches consisting of extraction of the region of interest and simple regression to estimate BMD, which require a large amount of training data. Therefore, we propose an efficient method that learns decomposition into projections of bone-segmented QCT for BMD estimation under limited datasets. The proposed method achieved high accuracy in BMD estimation, where Pearson correlation coefficients of 0.880 and 0.920 were observed for DXA-measured BMD and QCT-measured BMD estimation tasks, respectively, and the root mean square of the coefficient of variation values were 3.27 to 3.79% for four measurements with different poses. Furthermore, we conducted extensive validation experiments, including multi-pose, uncalibrated-CT, and compression experiments toward actual application in routine clinical practice.

The accuracy of statistical shape models in predicting bone shape: A systematic review

BACKGROUND

This systematic review aims to ascertain how accurately 3D models can be predicted from two-dimensional (2D) imaging utilising statistical shape modelling.

METHODS

A systematic search of published literature was conducted in September 2022. All papers which assessed the accuracy of 3D models predicted from 2D imaging utilising statistical shape models and which validated the models against the ground truth were eligible.

RESULTS

2127 papers were screened and a total of 34 studies were included for final data extraction. The best overall achievable accuracy was 0.45 mm (root mean square error) and 0.16 mm (average error).

CONCLUSION

Statistical shape modelling can predict detailed 3D anatomical models from minimal 2D imaging. Future studies should report the intended application domain of the model, the level of accuracy required, the underlying demographics of subjects, and the method in which accuracy was calculated, with root mean square error recommended if appropriate.

3D reconstruction of proximal femoral fracture from biplanar radiographs with fractural representative learning

A femoral fracture is a severe injury occurring in traumatic and pathologic causes. Diagnosis and Preoperative planning are indispensable procedures relying on preoperative radiographs such as X-ray and CT images. Nevertheless, CT imaging has a higher cost, radiation dose, and longer acquisition time than X-ray imaging. Thus, the fracture 3D reconstruction from X-ray images had been needed and remains a challenging problem, as well as a lack of dataset. This paper proposes a 3D proximal femoral fracture reconstruction from biplanar radiographs to improve the 3D visualization of bone fragments during preoperative planning. A novel Fracture Reconstruction Network (FracReconNet) is proposed to retrieve the femoral bone shape with fracture details, including the 3D Reconstruction Network (3DReconNet), novel Auxiliary class (AC), and Fractural augmentation (FA). The 3D reconstruction network applies a deep learning-based, fully Convolutional Network with Feature Pyramid Network architecture. Specifically, the auxiliary class is proposed, which refers to fracture representation. It encourages network learning to reconstruct the fracture. Since the samples are scarce to acquire, the fractural augmentation is invented to enlarge the fracture training samples and improve reconstruction accuracy. The evaluation of FracReconNet achieved a mIoU of 0.851 and mASSD of 0.906 mm. The proposed FracReconNet's results show fracture detail similar to the real fracture, while the 3DReconNet cannot offer.

2D-3D reconstruction of the proximal femur from DXA scans: Evaluation of the 3D-Shaper software

Introduction: Osteoporosis is currently diagnosed based on areal bone mineral density (aBMD) computed from 2D DXA scans. However, aBMD is a limited surrogate for femoral strength since it does not account for 3D bone geometry and density distribution. QCT scans combined with finite element (FE) analysis can deliver improved femoral strength predictions. However, non-negligible radiation dose and high costs prevent a systematic usage of this technique for screening purposes. As an alternative, the 3D-Shaper software (3D-Shaper Medical, Spain) reconstructs the 3D shape and density distribution of the femur from 2D DXA scans. This approach could deliver a more accurate estimation of femoral strength than aBMD by using FE analysis on the reconstructed 3D DXA. Methods: Here we present the first independent evaluation of the software, using a dataset of 77 ex vivo femora. We extend a prior evaluation by including the density distribution differences, the spatial correlation of density values and an FE analysis. Yet, cortical thickness is left out of this evaluation, since the cortex is not resolved in our FE models. Results: We found an average surface distance of 1.16 mm between 3D DXA and QCT images, which shows a good reconstruction of the bone geometry. Although BMD values obtained from 3D DXA and QCT correlated well (r ² = 0.92), the 3D DXA BMD were systematically lower. The average BMD difference amounted to 64 mg/cm³, more than one-third of the 3D DXA BMD. Furthermore, the low correlation (r ² = 0.48) between density values of both images indicates a limited reconstruction of the 3D density distribution. FE results were in good agreement between QCT and 3D DXA images, with a high coefficient of determination (r ² = 0.88). However, this correlation was not statistically different from a direct prediction by aBMD. Moreover, we found differences in the fracture patterns between the two image types. QCT-based FE analysis resulted mostly in femoral neck fractures and 3D DXA-based FE in subcapital or pertrochanteric fractures. Discussion: In conclusion, 3D-Shaper generates an altered BMD distribution compared to QCT but, after careful density calibration, shows an interesting potential for deriving a standardized femoral strength from a DXA scan.

The impact of obesity on the accuracy of DXA BMD for DXA-equivalent BMD estimation

INTRODUCTION

As the radiomics technique using texture features in CT is adopted for accessing DXA-equivalent bone mineral density (BMD), this study aims to compare BMD by DXA and predicted BMD to investigate the impact of obesity and central obesity in general patients.

MATERIALS AND METHODS

A total of 710 cases (621 patients) obtained from May 6, 2012, to June 30, 2021, were used in the study. We focused both their abdomen & pelvis CT's first lumbar vertebrae axial cuts to predict estimated BMD and bone mineral content (BMC). In each patient's CT, we extracted the largest trabecular region of the L1 vertebral body as a region of interest (ROI) using the gray-level co-occurrence matrices (GLCM) technique, and linear regression was applied to predict the indices. Cases were divided by central obesity/overall obesity and normal group by body mass index (BMI), waist circumference (WC), or index of central obesity (ICO) standard.

RESULTS

The coefficients were all above 0.73, respectively. P-values from ICO were over 0.05 when the measures were Hip BMD and Hip BMC. In contrast, those from ICO were 0.0131 and 0.0351 when the measures were L1 BMD and L1 BMC, respectively, which show a difference between the two groups.

CONCLUSIONS

The CT HU texture analysis method was an effective and economical method for measuring estimated BMD and BMC and evaluating the impact of obesity. We found that central obesity especially exerted an effect on the disturbance of the clinical BMD measurements since groups were significantly different under the ICO standard.

Adding liver R2* quantification to proton density fat fraction MRI of vertebral bone marrow improves the prediction of osteoporosis

OBJECTIVES

To assess the predictive value of the combination of bone marrow (BM) proton density fat fraction (PDFF) and liver R2* for osteopenia and osteoporosis and the additional role of liver R2*.

METHODS

A total of 107 healthy women were included between June 2019 and January 2021. Each participant underwent dual-energy X-ray absorptiometry (DXA) and chemical shift-encoded 3.0-T MRI. PDFF measurements were performed for each lumbar vertebral body, and R2* measurements were performed in liver segments. Agreement among measurements was assessed by Bland-Altman analysis. Receiver operating characteristic (ROC) curves were generated to select optimised cut-offs for BM PDFF and liver R2*. Univariable and multivariable logistic regressions were performed. The C statistic and continuous net reclassification improvement (NRI) were adopted to explore the incremental predictive ability of liver R2*.

RESULTS

Bone mass decreased in 42 cases (39.3%) and nonbone mass decreased in 65 cases (60.7%). There were significant differences among the age groups, menopausal status groups, PDFF > 45.0% groups, and R2* > 67.7 groups. Each measurement had good reproducibility. The odds ratios (95% CIs) were 4.05 (1.22-13.43) for PDFF and 4.34 (1.41-13.35) for R2*. The C statistic (95% CI) without R2* was 0.888 (0.827-0.950), and with R2* was 0.900 (0.841-0.960). The NRI resulting from the combination of PDFF and R2* was 75.6% (p < 0.01).

CONCLUSION

The predictive improvement over the use of BM PDFF and other traditional risk factors demonstrates the potential of liver R2* as a biomarker for osteopenia and osteoporosis in healthy women.

KEY POINTS

• Liver R2* is a biomarker for the assessment of osteopenia and osteoporosis. • Liver R2* improved the ability to predict osteopenia and osteoporosis. • The intra- and interobserver measurements showed high agreement.

Femoral strength can be predicted from 2D projections using a 3D statistical deformation and texture model with finite element analysis

Ultimate force of the proximal human femur can be predicted using Finite Element Analysis (FEA), but the models rely on 3D computed tomography images. Landmark-based statistical appearance models (SAM) and B-Spline transformation-based statistical deformation models (SDM) have been used to estimate 3D images from 2D projections, which facilitates model generation and reduces the radiation dose. However, there is no literature on the accuracy of SDM-based FEA models of bones with respect to experimental results. In this study, a methodology for an enhanced SDM with textural information is presented. The statistical deformation and texture models (SDTMs) are based on a set of 37 quantitative CT (QCT) images. They were used to estimate 3D images from two or one projections of the set in a leave-one-out setup. These estimations where then used to create FEA models. The ultimate force predicted by FEA models estimated from two or one projection using the SDTMs were compared to the experimental ultimate force from a previous study on the same femora and to the results of standard QCT-based FEA models. High correlations between predictions and experimental measurements were found for FEA models reconstructed from 2D projections with R²=0.835 when based on two projections and R²=0.724 when using one projection. The correlations were comparable to those reached with standard QCT-based FE-models with the experimental results (R²=0.795). This study shows the high potential of SDTM-based 3D image reconstruction and FEA modelling from 2D projections to predict femoral ultimate force.

Validation of 3D finite element models from simulated DXA images for biofidelic simulations of sideways fall impact to the hip

Computed tomography (CT)-derived finite element (FE) models have been proposed as a tool to improve the current clinical assessment of osteoporosis and personalized hip fracture risk by providing an accurate estimate of femoral strength. However, this solution has two main drawbacks, namely: (i) 3D CT images are needed, whereas 2D dual-energy x-ray absorptiometry (DXA) images are more generally available, and (ii) quasi-static femoral strength is predicted as a surrogate for fracture risk, instead of predicting whether a fall would result in a fracture or not. The aim of this study was to combine a biofidelic fall simulation technique, based on 3D computed tomography (CT) data with an algorithm that reconstructs 3D femoral shape and BMD distribution from a 2D DXA image. This approach was evaluated on 11 pelvis-femur constructs for which CT scans, ex vivo sideways fall impact experiments and CT-derived biofidelic FE models were available. Simulated DXA images were used to reconstruct the 3D shape and bone mineral density (BMD) distribution of the left femurs by registering a projection of a statistical shape and appearance model with a genetic optimization algorithm. The 2D-to-3D reconstructed femurs were meshed, and the resulting FE models inserted into a biofidelic FE modeling pipeline for simulating a sideways fall. The median 2D-to-3D reconstruction error was 1.02 mm for the shape and 0.06 g/cm³ for BMD for the 11 specimens. FE models derived from simulated DXAs predicted the outcome of the falls in terms of fracture versus non-fracture with the same accuracy as the CT-derived FE models. This study represents a milestone towards improved assessment of hip fracture risk based on widely available clinical DXA images.

Morphologic and radiologic parameters correlating to shoulder function at diagnosis for patients with rotator cuff tear

BACKGROUND

The magnetic resonance imaging (MRI) parameters used to diagnose rotator cuff tears are weakly correlated to shoulder function. Our hypothesis was that adding 3-dimensional morphologic parameters resulting from biplanar radiographs (3DXR parameters) to the MRI parameters would improve this correlation.

METHODS

We assessed 52 patients with rotator cuff tears with an EOS Imaging radiographic examination, MRI study, and clinical evaluation of the shoulder, as well as the Constant score. The bones of the 52 shoulders were reconstructed 3-dimensionally, and eleven 3DXR parameters were automatically extracted. First, the trueness and reliability of these parameters were evaluated. Then, bivariate correlations between each parameter and the Constant score were made. A linear regression model was subsequently built to correlate the 11 parameters and 5 MRI findings with shoulder function at diagnosis, as assessed by the Constant score.

RESULTS

The parameters showed good trueness and reliability of most 3DXR parameters. Supraspinatus tear extension, muscle atrophy, and the distance between the greater and deltoid tuberosities were the only parameters with a statistically significant correlation to a lower Constant score (P < .05) in the bivariate study. These correlations were either weak or negligible. A regression model was successfully built with one MRI parameter and four 3DXR parameters. Correlation to function increased from 16.7% to 43.3% with this model.

CONCLUSION

For patients with rotator cuff tears, the combination of MRI and 3DXR parameters of the shoulder in a linear regression model improves the correlation with the Constant score (shoulder function) at diagnosis.

3D Analysis of the Proximal Femur Compared to 2D Analysis for Hip Fracture Risk Prediction in a Clinical Population

Due to the adverse impacts of hip fractures on patients' lives, it is crucial to enhance the identification of people at high risk through accessible clinical techniques. Reconstructing the 3D geometry and BMD distribution of the proximal femur could be beneficial in enhancing hip fracture risk predictions; however, it is associated with a high computational burden. It is also not clear whether it provides a better performance than 2D model analysis. Therefore, the purpose of this study was to compare the 2D and 3D model reconstruction's ability to predict hip fracture risk in a clinical population of patients. The DXA scans and CT scans of 16 cadaveric femurs were used to create training sets for the 2D and 3D model reconstruction based on statistical shape and appearance modeling. Subsequently, these methods were used to predict the risk of sustaining a hip fracture in a clinical population of 150 subjects (50 fractured, and 100 non-fractured) that were monitored for five years in the Canadian Multicentre Osteoporosis Study. 3D model reconstruction was able to improve the identification of patients who sustained a hip fracture more accurately than the standard clinical practice (by 40%). Also, the predictions from the 2D statistical model didn't differ significantly from the 3D ones (p > 0.76). These results indicated that to enhance hip fracture risk prediction in clinical practice implementing 2D statistical modeling has comparable performance with lower associated computational load.

A Spatio-Temporal Ageing Atlas of the Proximal Femur

Osteoporosis is an age-associated disease characterised by low bone mineral density (BMD) and micro-architectural deterioration leading to enhanced fracture risk. Conventional dual-energy X-ray absorptiometry (DXA) analysis has facilitated our understanding of BMD reduction in specific regions of interest (ROIs) within the femur, but cannot resolve spatial BMD patterns nor reflect age-related changes in bone microarchitecture due to its inherent averaging of pixel BMD values into large ROIs. To address these limitations and develop a comprehensive model of involutional bone loss, this paper presents a fully automatic pipeline to build a spatio-temporal atlas of ageing bone in the proximal femur. A new technique, termed DXA region free analysis (DXA RFA), is proposed to eliminate morphological variation between DXA scans by warping each image into a reference template. To construct the atlas, we use unprocessed DXA data from Caucasian women aged 20-97 years participating in three cohort studies in Western Europe ( ,000). A novel calibration procedure, termed quantile matching regression, is proposed to integrate data from different DXA manufacturers. Pixel-wise BMD evolution with ageing was modelled using smooth quantile curves. This technique enables characterisation of spatially-complex BMD change patterns with ageing, visualised using heat-maps. Furthermore, quantile curves plotted at different pixel coordinates showed consistently different rates of bone loss at different regions within the femoral neck. Given the close relationship between spatio-temporal bone loss and osteoporotic fracture, improved understanding of the bone ageing process could lead to enhanced prognostic, preventive and therapeutic strategies for the disease.

Perspectives on the non-invasive evaluation of femoral strength in the assessment of hip fracture risk

We reviewed the experimental and clinical evidence that hip bone strength estimated by BMD and/or finite element analysis (FEA) reflects the actual strength of the proximal femur and is associated with hip fracture risk and its changes upon treatment.

INTRODUCTION

The risk of hip fractures increases exponentially with age due to a progressive loss of bone mass, deterioration of bone structure, and increased incidence of falls. Areal bone mineral density (aBMD), measured by dual-energy X-ray absorptiometry (DXA), is the most used surrogate marker of bone strength. However, age-related declines in bone strength exceed those of aBMD, and the majority of fractures occur in those who are not identified as osteoporotic by BMD testing. With hip fracture incidence increasing worldwide, the development of accurate methods to estimate bone strength in vivo would be very useful to predict the risk of hip fracture and to monitor the effects of osteoporosis therapies.

METHODS

We reviewed experimental and clinical evidence regarding the association between aBMD and/orCT-finite element analysis (FEA) estimated femoral strength and hip fracture risk as well as their changes with treatment.

RESULTS

Femoral aBMD and bone strength estimates by CT-FEA explain a large proportion of femoral strength ex vivo and predict hip fracture risk in vivo. Changes in femoral aBMD are strongly associated with anti-fracture efficacy of osteoporosis treatments, though comparable data for FEA are currently not available.

CONCLUSIONS

Hip aBMD and estimated femoral strength are good predictors of fracture risk and could potentially be used as surrogate endpoints for fracture in clinical trials. Further improvements of FEA may be achieved by incorporating trabecular orientations, enhanced cortical modeling, effects of aging on bone tissue ductility, and multiple sideway fall loading conditions.

Automated segmentation of cortical and trabecular bone to generate finite element models for femoral bone mechanics

Finite element (FE) models based on quantitative computed tomography (CT) images are better predictors of bone strength than conventional areal bone mineral density measurements. However, FE models require manual segmentation of the femur, which is not clinically applicable. This study developed a method for automated FE analyses from clinical CT images. Clinical in-vivo CT images of 13 elderly female subjects were collected to evaluate the method. Secondly, proximal cadaver femurs were harvested and imaged with clinical CT (N = 17). Of these femurs, 14 were imaged with µCT and three had earlier been tested experimentally in stance-loading, while collecting surface deformations with digital image correlation. Femurs were segmented from clinical CT images using an automated method, based on the segmentation tool Stradwin. The method automatically distinguishes trabecular and cortical bone, corrects partial volume effect and generates input for FE analysis. The manual and automatic segmentations agreed within about one voxel for in-vivo subjects (0.99 ± 0.23 mm) and cadaver femurs (0.21 ± 0.07 mm). The strains from the FE predictions closely matched with the experimentally measured strains (R² = 0.89). The method can automatically generate meshes suitable for FE analysis. The method may bring us one step closer to enable clinical usage of patient-specific FE analyses.

3-D Subject-Specific Shape and Density Estimation of the Lumbar Spine From a Single Anteroposterior DXA Image Including Assessment of Cortical and Trabecular Bone

Dual Energy X-ray Absorptiometry (DXA) is the standard exam for osteoporosis diagnosis and fracture risk evaluation at the spine. However, numerous patients with bone fragility are not diagnosed as such. In fact, standard analysis of DXA images does not differentiate between trabecular and cortical bone; neither specifically assess of the bone density in the vertebral body, which is where most of the osteoporotic fractures occur. Quantitative computed tomography (QCT) is an alternative technique that overcomes limitations of DXA-based diagnosis. However, due to the high cost and radiation dose, QCT is not used for osteoporosis management. We propose a method that provides a 3-D subject-specific shape and density estimation of the lumbar spine from a single anteroposterior (AP) DXA image. A 3-D statistical shape and density model is built, using a training set of QCT scans, and registered onto the AP DXA image so that its projection matches it. Cortical and trabecular bone compartments are segmented using a model-based algorithm. Clinical measurements are performed at different bone compartments. Accuracy was evaluated by comparing DXA-derived to QCT-derived 3-D measurements for a validation set of 180 subjects. The shape accuracy was 1.51 mm at the total vertebra and 0.66 mm at the vertebral body. Correlation coefficients between DXA and QCT-derived measurements ranged from 0.81 to 0.97. The method proposed offers an insightful 3-D analysis of the lumbar spine, which could potentially improve osteoporosis and fracture risk assessment in patients who had an AP DXA scan of the lumbar spine without any additional examination.

Review of 2-D/3-D Reconstruction Using Statistical Shape and Intensity Models and X-Ray Image Synthesis: Toward a Unified Framework

Patient-specific three-dimensional (3-D) bone models are useful for a number of clinical applications such as surgery planning, postoperative evaluation, as well as implant and prosthesis design. Two-dimensional-to-3-D (2-D/3-D) reconstruction, also known as model-to-modality or atlas-based 2-D/3-D registration, provides a means of obtaining a 3-D model of a patient's bones from their 2-D radiographs when 3-D imaging modalities are not available. The preferred approach for estimating both shape and density information (that would be present in a patient's computed tomography data) for 2-D/3-D reconstruction makes use of digitally reconstructed radiographs and deformable models in an iterative, non-rigid, intensity-based approach. Based on a large number of state-of-the-art 2-D/3-D bone reconstruction methods, a unified mathematical formulation of the problem is proposed in a common conceptual framework, using unambiguous terminology. In addition, shortcomings, recent adaptations, and persisting challenges are discussed along with insights for future research.

Structural Parameters of the Proximal Femur by 3-Dimensional Dual-Energy X-ray Absorptiometry Software: Comparison With Quantitative Computed Tomography

Structural parameters of the proximal femur evaluate the strength of the bone and its susceptibility to fracture. These parameters are computed from dual-energy X-ray absorptiometry (DXA) or from quantitative computed tomography (QCT). The 3-dimensional (3D)-DXA software solution provides 3D models of the proximal femur shape and bone density from anteroposterior DXA scans. In this paper, we present and evaluate a new approach to compute structural parameters using 3D-DXA software. A cohort of 60 study subjects (60.9 ± 14.7 yr) with DXA and QCT examinations was collected. 3D femoral models obtained by QCT and 3D-DXA software were aligned using rigid registration techniques for comparison purposes. Geometric, cross-sectional, and volumetric structural parameters were computed at the narrow neck, intertrochanteric, and lower shaft regions for both QCT and 3D-DXA models. The accuracy of 3D-DXA structural parameters was evaluated in comparison with QCT. Correlation coefficients (r) between geometric parameters computed by QCT and 3D-DXA software were 0.86 for the femoral neck axis length and 0.71 for the femoral neck shaft angle. Correlation coefficients ranged from 0.86 to 0.96 for the cross-sectional parameters and from 0.84 to 0.97 for the volumetric structural parameters. Our study demonstrated that accurate estimates of structural parameters for the femur can be obtained from 3D-DXA models. This provides clinicians with 3D indexes related to the femoral strength from routine anteroposterior DXA scans, which could potentially improve osteoporosis management and fracture prevention.

High-Intensity Resistance and Impact Training Improves Bone Mineral Density and Physical Function in Postmenopausal Women With Osteopenia and Osteoporosis: The LIFTMOR Randomized Controlled Trial

Optimal osteogenic mechanical loading requires the application of high-magnitude strains at high rates. High-intensity resistance and impact training (HiRIT) applies such loads but is not traditionally recommended for individuals with osteoporosis because of a perceived high risk of fracture. The purpose of the LIFTMOR trial was to determine the efficacy and to monitor adverse events of HiRIT to reduce parameters of risk for fracture in postmenopausal women with low bone mass. Postmenopausal women with low bone mass (T-score < -1.0, screened for conditions and medications that influence bone and physical function) were recruited and randomized to either 8 months of twice-weekly, 30-minute, supervised HiRIT (5 sets of 5 repetitions, >85% 1 repetition maximum) or a home-based, low-intensity exercise program (CON). Pre- and post-intervention testing included lumbar spine and proximal femur bone mineral density (BMD) and measures of functional performance (timed up-and-go, functional reach, 5 times sit-to-stand, back and leg strength). A total of 101 women (aged 65 ± 5 years, 161.8 ± 5.9 cm, 63.1 ± 10.4 kg) participated in the trial. HiRIT (n = 49) effects were superior to CON (n = 52) for lumbar spine (LS) BMD (2.9 ± 2.8% versus -1.2 ± 2.8%, p < 0.001), femoral neck (FN) BMD (0.3 ± 2.6% versus -1.9 ± 2.6%, p = 0.004), FN cortical thickness (13.6 ± 16.6% versus 6.3 ± 16.6%, p = 0.014), height (0.2 ± 0.5 cm versus -0.2 ± 0.5 cm, p = 0.004), and all functional performance measures (p < 0.001). Compliance was high (HiRIT 92 ± 11%; CON 85 ± 24%) in both groups, with only one adverse event reported (HiRIT: minor lower back spasm, 2/70 missed training sessions). Our novel, brief HiRIT program enhances indices of bone strength and functional performance in postmenopausal women with low bone mass. Contrary to current opinion, HiRIT was efficacious and induced no adverse events under highly supervised conditions for our sample of otherwise healthy postmenopausal women with low to very low bone mass. © 2017 American Society for Bone and Mineral Research.

Classification of the trabecular bone structure of osteoporotic patients using machine vision

Osteoporosis is a common bone disease which often leads to fractures. Clinically, the major challenge for the automatic diagnosis of osteoporosis is the complex architecture of bones. The clinical diagnosis of osteoporosis is conventionally done using Dual-energy X-ray Absorptiometry (DXA). This method has specific limitations, however, such as the large size of the instrument, a relatively high cost and limited availability. The method proposed here is based on the automatic processing of X-ray images. The bone X-ray image was statistically processed and strategically reformed to extract discriminatory statistical features of different orders. These features were used for machine learning for the classification of two populations composed of osteoporotic and healthy subjects. Four classifiers - support vector machine (SVM), k-nearest neighbors, Naïve Bayes and artificial neural network - were used to test the performance of the proposed method. Tests were performed on X-ray images of the calcaneus bone collected from the hospital of Orleans. The results are significant in terms of accuracy and time complexity. Experimental results indicate a classification rate of 98% using an SVM classifier which is encouraging for automatic osteoporosis diagnosis using bone X-ray images. The low time complexity of the proposed method makes it suitable for real time applications.

3D-DXA: Assessing the Femoral Shape, the Trabecular Macrostructure and the Cortex in 3D from DXA images

The 3D distribution of the cortical and trabecular bone mass in the proximal femur is a critical component in determining fracture resistance that is not taken into account in clinical routine Dual-energy X-ray Absorptiometry (DXA) examination. In this paper, a statistical shape and appearance model together with a 3D-2D registration approach are used to model the femoral shape and bone density distribution in 3D from an anteroposterior DXA projection. A model-based algorithm is subsequently used to segment the cortex and build a 3D map of the cortical thickness and density. Measurements characterising the geometry and density distribution were computed for various regions of interest in both cortical and trabecular compartments. Models and measurements provided by the "3D-DXA" software algorithm were evaluated using a database of 157 study subjects, by comparing 3D-DXA analyses (using DXA scanners from three manufacturers) with measurements performed by Quantitative Computed Tomography (QCT). The mean point-to-surface distance between 3D-DXA and QCT femoral shapes was 0.93 mm. The mean absolute error between cortical thickness and density estimates measured by 3D-DXA and QCT was 0.33 mm and 72 mg/cm3. Correlation coefficients (R) between the 3D-DXA and QCT measurements were 0.86, 0.93, and 0.95 for the volumetric bone mineral density at the trabecular, cortical, and integral compartments respectively, and 0.91 for the mean cortical thickness. 3D-DXA provides a detailed analysis of the proximal femur, including a separate assessment of the cortical layer and trabecular macrostructure, which could potentially improve osteoporosis management while maintaining DXA as the standard routine modality.

Focal osteoporosis defects play a key role in hip fracture

BACKGROUND

Hip fractures are mainly caused by accidental falls and trips, which magnify forces in well-defined areas of the proximal femur. Unfortunately, the same areas are at risk of rapid bone loss with ageing, since they are relatively stress-shielded during walking and sitting. Focal osteoporosis in those areas may contribute to fracture, and targeted 3D measurements might enhance hip fracture prediction. In the FEMCO case-control clinical study, Cortical Bone Mapping (CBM) was applied to clinical computed tomography (CT) scans to define 3D cortical and trabecular bone defects in patients with acute hip fracture compared to controls. Direct measurements of trabecular bone volume were then made in biopsies of target regions removed at operation.

METHODS

The sample consisted of CT scans from 313 female and 40 male volunteers (158 with proximal femoral fracture, 145 age-matched controls and 50 fallers without hip fracture). Detailed Cortical Bone Maps (c.5580 measurement points on the unfractured hip) were created before registering each hip to an average femur shape to facilitate statistical parametric mapping (SPM). Areas where cortical and trabecular bone differed from controls were visualised in 3D for location, magnitude and statistical significance. Measures from the novel regions created by the SPM process were then tested for their ability to classify fracture versus control by comparison with traditional CT measures of areal Bone Mineral Density (aBMD). In women we used the surgical classification of fracture location ('femoral neck' or 'trochanteric') to discover whether focal osteoporosis was specific to fracture type. To explore whether the focal areas were osteoporotic by histological criteria, we used micro CT to measure trabecular bone parameters in targeted biopsies taken from the femoral heads of 14 cases.

RESULTS

Hip fracture patients had distinct patterns of focal osteoporosis that determined fracture type, and CBM measures classified fracture type better than aBMD parameters. CBM measures however improved only minimally on aBMD for predicting any hip fracture and depended on the inclusion of trabecular bone measures alongside cortical regions. Focal osteoporosis was confirmed on biopsy as reduced sub-cortical trabecular bone volume.

CONCLUSION

Using 3D imaging methods and targeted bone biopsy, we discovered focal osteoporosis affecting trabecular and cortical bone of the proximal femur, among men and women with hip fracture.

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.

Dual-Energy X-Ray Absorptiometry for Measurement of Phalangeal Bone Mineral Density on a Slot-Scanning Digital Radiography System

OBJECTIVE

In this paper, we assess the feasibility of using two detectors in a slot-scanning digital radiography system to acquire images for measuring bone mineral density (BMD) of the middle phalanx of the middle finger using dual-energy X-ray absorptiometry (DXA).

METHODS

Simulations were used to evaluate the spectral separation of the low- and high-energy spectra and detective quantum efficiency was used for assessing image quality. Scan parameters were chosen to optimize spectral separation, image quality, and radiation dose. We introduce the measurement of volumetric BMD (vBMD) using basis material decomposition. We assess the accuracy of our methods by comparing measurements taken using bone images against reference data derived from subsequent incineration of the bones. In vivo scans were conducted to evaluate the system precision (repeatability) and agreement with a clinical densitometer.

RESULTS

Average errors for bone mineral content (BMC), areal BMD (aBMD), and vBMD were 4.85%, 5.49%, and 12.77%, respectively. Our system had good agreement with a clinical densitometer based on concordance correlation coefficient values of 0.92 and 0.98 for aBMD and BMC, respectively. Precision studies yielded coefficient of variation (CV) values of 1.35% for aBMD, 1.48% for BMC, and 1.80% for vBMD. The CV values of all measurements were within 2%, indicating that the methods have clinically acceptable precision.

CONCLUSION

We conclude that our techniques yield bone measurements with high accuracy, clinically acceptable precision, and good agreement with a clinical densitometer.

SIGNIFICANCE

We have shown the clinical potential of phalangeal DXA measurements of aBMD and vBMD on a slot-scanning digital radiography system.

Patient-specific bone modeling and analysis: the role of integration and automation in clinical adoption

Patient-specific analysis of bones is considered an important tool for diagnosis and treatment of skeletal diseases and for clinical research aimed at understanding the etiology of skeletal diseases and the effects of different types of treatment on their progress. In this article, we discuss how integration of several important components enables accurate and cost-effective patient-specific bone analysis, focusing primarily on patient-specific finite element (FE) modeling of bones. First, the different components are briefly reviewed. Then, two important aspects of patient-specific FE modeling, namely integration of modeling components and automation of modeling approaches, are discussed. We conclude with a section on validation of patient-specific modeling results, possible applications of patient-specific modeling procedures, current limitations of the modeling approaches, and possible areas for future research.

Prediction of incident hip fracture with the estimated femoral strength by finite element analysis of DXA Scans in the study of osteoporotic fractures

A bone fractures only when loaded beyond its strength. The purpose of this study was to determine the association of femoral strength, as estimated by finite element (FE) analysis of dual-energy X-ray absorptiometry (DXA) scans, with incident hip fracture in comparison to hip bone mineral density (BMD), Fracture Risk Assessment Tool (FRAX), and hip structure analysis (HSA) variables. This prospective case-cohort study included a random sample of 1941 women and 668 incident hip fracture cases (295 in the random sample) during a mean ± SD follow-up of 12.8 ± 5.7 years from the Study of Osteoporotic Fractures (n = 7860 community-dwelling women ≥67 years of age). We analyzed the baseline DXA scans (Hologic 1000) of the hip using a validated plane-stress, linear-elastic finite element (FE) model of the proximal femur and estimated the femoral strength during a simulated sideways fall. Cox regression accounting for the case-cohort design assessed the association of estimated femoral strength with hip fracture. The age-body mass index (BMI)-adjusted hazard ratio (HR) per SD decrease for estimated strength (2.21; 95% CI, 1.95-2.50) was greater than that for total hip (TH) BMD (1.86; 95% CI, 1.67-2.08; p < 0.05), FN BMD (2.04; 95% CI, 1.79-2.32; p > 0.05), FRAX scores (range, 1.32-1.68; p < 0.0005), and many HSA variables (range, 1.13-2.43; p < 0.005), and the association was still significant (p < 0.05) after further adjustment for hip BMD or FRAX scores. The association of estimated strength with incident hip fracture was strong (Harrell's C index 0.770), significantly better than TH BMD (0.759; p < 0.05) and FRAX scores (0.711-0.743; p < 0.0001), but not FN BMD (0.762; p > 0.05). Similar findings were obtained for intracapsular and extracapsular fractures. In conclusion, the estimated femoral strength from FE analysis of DXA scans is an independent predictor and performs at least as well as FN BMD in predicting incident hip fracture in postmenopausal women.

Statistical shape and appearance models in osteoporosis

Statistical models (SMs) of shape (SSM) and appearance (SAM) have been acquiring popularity in medical image analysis since they were introduced in the early 1990s. They have been primarily used for segmentation, but they are also a powerful tool for 3D reconstruction and classification. All these tasks may be required in the osteoporosis domain, where fracture detection and risk estimation are key to reducing the mortality and/or morbidity of this bone disease. In this article, we review the different applications of SSMs and SAMs in the context of osteoporosis, and it concludes with a discussion of their advantages and disadvantages for this application.

Statistical shape and appearance models of bones

When applied to bones, statistical shape models (SSM) and statistical appearance models (SAM) respectively describe the mean shape and mean density distribution of bones within a certain population as well as the main modes of variations of shape and density distribution from their mean values. The availability of this quantitative information regarding the detailed anatomy of bones provides new opportunities for diagnosis, evaluation, and treatment of skeletal diseases. The potential of SSM and SAM has been recently recognized within the bone research community. For example, these models have been applied for studying the effects of bone shape on the etiology of osteoarthritis, improving the accuracy of clinical osteoporotic fracture prediction techniques, design of orthopedic implants, and surgery planning. This paper reviews the main concepts, methods, and applications of SSM and SAM as applied to bone.

Fast generation of virtual X-ray images for reconstruction of 3D anatomy

We propose a novel GPU-based approach to render virtual X-ray projections of deformable tetrahedral meshes. These meshes represent the shape and the internal density distribution of a particular anatomical structure and are derived from statistical shape and intensity models (SSIMs). We apply our method to improve the geometric reconstruction of 3D anatomy (e.g. pelvic bone) from 2D X-ray images. For that purpose, shape and density of a tetrahedral mesh are varied and virtual X-ray projections are generated within an optimization process until the similarity between the computed virtual X-ray and the respective anatomy depicted in a given clinical X-ray is maximized. The OpenGL implementation presented in this work deforms and projects tetrahedral meshes of high resolution (200.000+ tetrahedra) at interactive rates. It generates virtual X-rays that accurately depict the density distribution of an anatomy of interest. Compared to existing methods that accumulate X-ray attenuation in deformable meshes, our novel approach significantly boosts the deformation/projection performance. The proposed projection algorithm scales better with respect to mesh resolution and complexity of the density distribution, and the combined deformation and projection on the GPU scales better with respect to the number of deformation parameters. The gain in performance allows for a larger number of cycles in the optimization process. Consequently, it reduces the risk of being stuck in a local optimum. We believe that our approach will improve treatments in orthopedics, where 3D anatomical information is essential.

Evaluation of automated statistical shape model based knee kinematics from biplane fluoroscopy

State-of-the-art fluoroscopic knee kinematic analysis methods require the patient-specific bone shapes segmented from CT or MRI. Substituting the patient-specific bone shapes with personalizable models, such as statistical shape models (SSM), could eliminate the CT/MRI acquisitions, and thereby decrease costs and radiation dose (when eliminating CT). SSM based kinematics, however, have not yet been evaluated on clinically relevant joint motion parameters. Therefore, in this work the applicability of SSMs for computing knee kinematics from biplane fluoroscopic sequences was explored. Kinematic precision with an edge based automated bone tracking method using SSMs was evaluated on 6 cadaveric and 10 in-vivo fluoroscopic sequences. The SSMs of the femur and the tibia-fibula were created using 61 training datasets. Kinematic precision was determined for medial-lateral tibial shift, anterior-posterior tibial drawer, joint distraction-contraction, flexion, tibial rotation and adduction. The relationship between kinematic precision and bone shape accuracy was also investigated. The SSM based kinematics resulted in sub-millimeter (0.48-0.81mm) and approximately 1° (0.69-0.99°) median precision on the cadaveric knees compared to bone-marker-based kinematics. The precision on the in-vivo datasets was comparable to that of the cadaveric sequences when evaluated with a semi-automatic reference method. These results are promising, though further work is necessary to reach the accuracy of CT-based kinematics. We also demonstrated that a better shape reconstruction accuracy does not automatically imply a better kinematic precision. This result suggests that the ability of accurately fitting the edges in the fluoroscopic sequences has a larger role in determining the kinematic precision than that of the overall 3D shape accuracy.

Accounting for patient variability in finite element analysis of the intact and implanted hip and knee: a review

It is becoming increasingly difficult to differentiate the performance of new joint replacement designs using available preclinical test methods. Finite element analysis is commonly used and the majority of published studies are performed on representative anatomy, assuming optimal implant placement, subjected to idealised loading conditions. There are significant differences between patients and accounting for this variability will lead to better assessment of the risk of failure. This review paper provides a comprehensive overview of the techniques available to account for patient variability. There is a brief overview of patient-specific model generation techniques, followed by a review of multisubject patient-specific studies performed on the intact and implanted femur and tibia. In particular, the challenges and limitations of manually generating models for such studies are discussed. To efficiently account for patient variability, the application of statistical shape and intensity models (SSIM) are being developed. Such models have the potential to synthetically generate thousands of representative models generated from a much smaller training set. Combined with the automation of the prosthesis implantation process, SSIM provides a potentially powerful tool for assessing the next generation of implant designs. The potential application of SSIM are discussed along with their limitations.

Hip fracture discrimination from dual-energy X-ray absorptiometry by statistical model registration

Although the areal Bone Mineral Density (BMD) measurements from dual-energy X-ray absorptiometry (DXA) are able to discriminate between hip fracture cases and controls, the femoral strength is largely determined by the 3D bone structure. In a previous work a statistical model was presented which parameterizes the 3D shape and BMD distribution of the proximal femur. In this study the parameter values resulting from the registration of the model onto DXA images are evaluated for their hip fracture discrimination ability with respect to regular DXA derived areal BMD measurements. The statistical model was constructed from a large database of QCT scans of females with an average age of 67.8 ± 17.0 years. This model was subsequently registered onto the DXA images of a fracture and control group. The fracture group consisted of 175 female patients with an average age of 66.4 ± 9.9 years who suffered a fracture on the contra lateral femur. The control group consisted of 175 female subjects with an average age of 65.3 ± 10.0 years and no fracture history. The discrimination ability of the resulting model parameter values, as well as the areal BMD measurements extracted from the DXA images were evaluated using a logistic regression analysis. The area under the receiver operating curve (AUC) of the combined model parameters and areal BMD values was 0.840 (95% CI 0.799-0.881), whilst using only the areal BMD values resulted in an AUC of 0.802 (95% CI 0.757-0.848). These results indicate that the discrimination ability of the areal BMD values is improved by supplementing them with the model parameter values, which give a more complete representation of the subject specific shape and internal bone distribution. Thus, the presented method potentially allows for an improved hip fracture risk estimation whilst maintaining DXA as the current standard modality.