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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.

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 72mg/cm(3). 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.

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.

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.

Purpose: Dual-energy x-ray absorptiometry (DXA) is used in clinical routine to provide a two-dimensional (2D) analysis of the bone mineral density (BMD). 3D reconstruction methods from 2D DXA images could improve the BMD analysis. To find the optimal configuration that should be used in clinical routine, this paper relies on a 3D reconstruction method from DXA images to compare the accuracy that can be obtained from one single-view and from multiview DXA images (two to four projections). Methods: The 3D reconstruction method uses a statistical model and a nonrigid registration technique to recover in 3D the shape and the BMD distribution of the proximal femur. The accuracy was evaluatedin vivo by comparing 3D reconstructions obtained from simulated DXA images of 30 patients (using between one and four DXA views) with quantitative computed tomographyreconstructions. Results: This comparison showed that the use of one single DXA provides accurate 3D reconstructions (mean shape accuracy of 1.0 mm and BMD distribution errors of 7.0%). Among the multiview configurations, the use of two views (0° and 45°) was the best compromise, increasing the accuracy of pose (mean accuracy of 0.7°/1.2°/0.9° against 1.0°/3.5°/3.3° for the single view), reducing slightly the BMD errors (5.7%) while maintaining the same shape accuracy. Conclusions: The use of two views constitutes an interesting configuration when multiview DXA devices are available in clinical routine. However, the use of only one single view remains an accurate solution to recover the shape and the BMD distribution in 3D, with the advantage of a higher potential for clinical translation.

Current vertebral fracture prevention measures use Dual-energy X-ray Absorptiometry (DXA) to quantify the density of the vertebrae and subsequently determine the risk of fracture. This modality however only provides information about the projected Bone Mineral Density (BMD) while the shape and spatial distribution of the bone determines the strength of the vertebrae. Quantitative Computed Tomography (QCT) allows for the measurement of the vertebral dimensions and volumetric densities, which have been shown to be able to determine the fracture risk more reliably than DXA. However, for the high cost and high radiation dose, QCT is not used in clinical routine for fracture risk assessment. In this work, we therefore propose a method to reconstruct the 3D shape and density volume of lumbar vertebrae from an anteroposterior (AP) and lateral DXA image used in clinical routine. The method is evaluated for the L2, L3 and L4 vertebra. Of these vertebrae a statistical model of the vertebral shape and density distribution is first constructed from a large dataset of QCT scans. All three models are then simultaneously registered onto both AP and lateral DXA image. The shape and volumetric BMD at several regions of the reconstructed vertebrae is then evaluated with respect to the ground truth QCT volumes. For the L2, L3 and L4 vertebrae respectively the shape was reconstructed with a mean (2RMS) point-to-surface distance of 1.00 (2.64) mm, 0.93(2.52) mm and 1.34(3.72) mm and a strong correlation (r > 0.82) was found between the trabecular volumetric BMD extracted from the reconstructions and from the same subject QCT scans. These results indicate that the proposed method is able to accurately reconstruct the 3D shape and density volume of the lumbar vertebrae from AP and lateral DXA, which can potentially improve the fracture risk estimation accuracy with respect to the currently used DXA derived areal BMD measurements.

Femoral strength is estimated in clinical routine from 2D Dual-energy X-rays Absorptiometry (DXA). In this study, a new pipeline for femoral strength prediction from DXA images is presented, using a 3D reconstruction method of the shape and the Bone Mineral Density (BMD) distribution and a regression analysis based on partial least squares. A database of 90 proximal femoral cadaveric specimens, that were previously imaged and tested to measure their fracture load, was used to develop and to validate the method. The proposed pipeline resulted in a correlation coefficient of 0.85 between predicted and measured fracture load, while a regression using 2D BMD measurements from DXA resulted in a correlation coefficient of 0.77. With an improved femoral strength prediction from DXA images, this method opens interesting translational perspectives in clinics for a better diagnosis of osteoporosis and fracture risk prediction.

This work presents a statistical model of both the shape and Bone Mineral Density (BMD) distribution of the proximal femur for fracture risk assessment. The shape and density model was built from a dataset of Quantitative Computed Tomography scans of fracture patients and a control group. Principal Component Analysis and Horn's parallel analysis were used to reduce the dimensionality of the shape and density model to the main modes of variation. The input data was then used to analyze the model parameters for the optimal separation between the fracture and control group. Feature selection using the Fisher criterion determined the parameters with the best class separation, which were used in Fisher Linear Discriminant Analysis to find the direction in the parameter space that best separates the fracture and control group. This resulted in a Fisher criterion value of 6.70, while analyzing the Dualenergy X-ray Absorptiometry derived femur neck areal BMD of the same subjects resulted in a Fisher criterion value of 0.98. This indicates that a fracture risk estimation approach based on the presented model might improve upon the current standard clinical practice.

The diagnosis of osteoporosis and the prevention of femur fractures is a major challenge for our society. However, the diagnosis performed in clinical routine from Dual Energy X-ray Absorptiometry (DXA) images is limited. This paper proposes a 3D reconstruction method of both the shape and the Bone Mineral Density (BMD) distribution of the proximal femur from routinely used DXA images. The reconstruction accuracy that can be obtained from single-view and multi-view DXA devices was assessed. This evaluation, from 20 bone specimens and simulated DXA images, highlighted a mean shape accuracy of 1.3mm and a BMD accuracy of 4.4% from a single-view DXA image. A multi-view configuration with 2 views (frontal-sagittal) appeared as a good compromise (mean shape accuracy of 0.9mm and BMD accuracy of 3.2%). We are currently using this method for in vivo clinical studies in order to improve the diagnosis of osteoporosis and the prevention of femur fractures.

Comunicació presentada al congrés: Medical Imaging 2010: Image Processing (SPIE) celebrat del 13 al 18 de febrer de 2010 a San Diego, California. Area Bone Mineral Density (aBMD) measured by Dual-energy X-ray Absorptiometry (DXA) is an established criterion in the evaluation of hip fracture risk. The evaluation from these planar images, however, is limited to 2D while it has been shown that proper 3D assessment of both the shape and the Bone Mineral Density (BMD) distribution improves the fracture risk estimation. In this work we present a method to reconstruct both the 3D bone shape and 3D BMD distribution of the proximal femur from a single DXA image. A statistical model of shape and a separate statistical model of the BMD distribution were automatically constructed from a set of Quantitative Computed Tomography (QCT) scans. The reconstruction method incorporates a fully automatic intensity based 3D-2D registration process, maximizing the similarity between the DXA and a digitally reconstructed radiograph of the combined model. For the construction of the models, an in vitro dataset of QCT scans of 60 anatomical specimens was used. To evaluate the reconstruction accuracy, experiments were performed on simulated DXA images from the QCT scans of 30 anatomical specimens. Comparisons between the reconstructions and the same subject QCT scans showed a mean shape accuracy of 1.2mm, and a mean density error of 81mg/cm3. The results show that this method is capable of accurately reconstructing both the 3D shape and 3D BMD distribution of the proximal femur from DXA images used in clinical routine, potentially improving the diagnosis of osteoporosis and fracture risk assessments at a low radiation dose and low cost. This research has been financially supported by the 3D-FemOs project (3-Dimensional Femur Reconstruction for Osteoporotic Fracture Risk Assessment) with a grant awarded by ACC1Ó Valtec (Tipus 1). In addition this work has been supported by a grant from the Instituto de Salud Carlos III (FI09/00757) and by a grant from the \Deutsche Forschungsgemeinschaft" (LO 730 / 3-1). The authors acknowledge Benedikt Schuler (Institute for Biomedical Image Analysis, UMIT, Hall in Tirol, Austria) and Dr. E. M. Lochmüuller (Universitäts-Frauenklinik der LMU, Mnchen, Germany) for the data collection.