3D-DXA Based Finite Element Modelling for Femur Strength Prediction: Evaluation Against QCT.

Osteoporosis is characterised by the loss of bone density resulting in an increased risk of fragility fractures. The clinical gold standard for diagnosing osteoporosis is based on the areal bone mineral density (aBMD) used as a surrogate for bone strength, in combination with clinical risk factors. Finite element (FE) analyses based on quantitative computed tomography (QCT) have been shown to estimate bone strength better than aBMD. However, their application in the osteoporosis clinics is limited due to exposure of patients to increased X-rays radiation dose. Statistical modelling methods (3D-DXA) enabling the estimation of 3D femur shape and volumetric bone density from dual energy X-ray absorptiometry (DXA) scan have been shown to improve osteoporosis management. The current study used 3D-DXA based FE analyses to estimate femur strength from the routine clinical DXA scans and compared its results against 151 QCT based FE analyses, in a clinical cohort of 157 subjects. The linear regression between the femur strength predicted by QCT-FE and 3D-DXA-FE models correlated highly (coefficient of determination R2 = 0.86) with a root mean square error (RMSE) of 397 N. In conclusion, the current study presented a 3D-DXA-FE modelling tool providing accurate femur strength estimates noninvasively, compared to QCT-FE models.

3D Analysis of Cortical and Trabecular Bone From Hip DXA:Precision and Trend Assessment Interval in PostmenopausalWomen.

The 3D distribution of the cortical and trabecular bone mass is a critical component in determining the resistance of a bone to fracture that is not assessed in standard dual-energy X-ray absorptiometry (DXA) exams. In this work, we assessed in vivo short-term precision of measurements provided by 3D modeling techniques from DXA scans and trend assessment intervals (TAIs) in postmenopausal women. Subjects included to study precision errors were scanned twice, with repositioning for duplicate hip scans, using either a Lunar iDXA scanner (GE Healthcare, Madison, WI) or a Discovery W scanner (Hologic, Inc., Waltham, MA). Postmenopausal women having baseline and 18-mo follow-up visit were scanned using a Lunar iDXA device to assess TAIs. TAIs indicate what time intervals are required to allow accurate assessment of response to treatment or progression of disease. The 3D-SHAPER software (Galgo Medical, Barcelona, Spain) was used to derive 3D measurements from hip DXA scans. Least significant changes were 10.39 and 8.72 mg/cm3 for integral volumetric bone mineral density (BMD), 9.64 and 9.59 mg/cm3 for trabecular volumetric BMD, and 6.25 and 5.99 mg/cm2 for cortical surface BMD, using the Lunar iDXA and Discovery W scanners, respectively. TAIs in postmenopausal women were 2.9 yr (integral volumetric BMD), 2.6 yr (trabecular volumetric BMD), and 3.5 yr (cortical surface BMD), using the Lunar iDXA scanner. As a comparison, TAIs for areal BMD were 2.8 yr at neck and 2.7 yr at total femur. Least significant changes of measurements provided by 3D modeling techniques from DXA were assessed. TAIs in postmenopausal women were similar to those measured for areal BMD measurements. DXA-derived 3D measurements could potentially provide additional indicators to improve patient monitoring in clinical practices.

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.

Technical Note: Cortical thickness and density estimation from clinical CT using a prior thickness-density relationship.

PURPOSE: Cortical thickness and density are critical components in determining the strength of bony structures. Computed tomography (CT) is one possible modality for analyzing the cortex in 3D. In this paper, a model-based approach for measuring the cortical bone thickness and density from clinical CT images is proposed. METHODS: Density variations across the cortex were modeled as a function of the cortical thickness and density, location of the cortex, density of surrounding tissues, and imaging blur. High resolution micro-CT data of cadaver proximal femurs were analyzed to determine a relationship between cortical thickness and density. This thickness-density relationship was used as prior information to be incorporated in the model to obtain accurate measurements of cortical thickness and density from clinical CT volumes. The method was validated using micro-CT scans of 23 cadaver proximal femurs. Simulated clinical CT images with different voxel sizes were generated from the micro-CT data. Cortical thickness and density were estimated from the simulated images using the proposed method and compared with measurements obtained using the micro-CT images to evaluate the effect of voxel size on the accuracy of the method. Then, 19 of the 23 specimens were imaged using a clinical CT scanner. Cortical thickness and density were estimated from the clinical CT images using the proposed method and compared with the micro-CT measurements. Finally, a case-control study including 20 patients with osteoporosis and 20 age-matched controls with normal bone density was performed to evaluate the proposed method in a clinical context. RESULTS: Cortical thickness (density) estimation errors were 0.07 ± 0.19 mm (-18 ± 92 mg/cm(3)) using the simulated clinical CT volumes with the smallest voxel size (0.33 × 0.33 × 0.5 mm(3)), and 0.10 ± 0.24 mm (-10 ± 115 mg/cm(3)) using the volumes with the largest voxel size (1.0 × 1.0 × 3.0 mm(3)). A trend for the cortical thickness and density estimation errors to increase with voxel size was observed and was more pronounced for thin cortices. Using clinical CT data for 19 of the 23 samples, mean errors of 0.18 ± 0.24 mm for the cortical thickness and 15 ± 106 mg/cm(3) for the density were found. The case-control study showed that osteoporotic patients had a thinner cortex and a lower cortical density, with average differences of -0.8 mm and -58.6 mg/cm(3) at the proximal femur in comparison with age-matched controls (p-value < 0.001). CONCLUSIONS: This method might be a promising approach for the quantification of cortical bone thickness and density using clinical routine imaging techniques. Future work will concentrate on investigating how this approach can improve the estimation of mechanical strength of bony structures, the prevention of fracture, and the management of osteoporosis.

Technical note: comparison between single and multiview simulated DXA configurations for reconstructing the 3D shape and bone mineral density distribution of the proximal femur.

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 evaluated in vivo by comparing 3D reconstructions obtained from simulated DXA images of 30 patients (using between one and four DXA views) with quantitative computed tomography reconstructions. 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.

A statistical model of shape and bone mineral density distribution of the proximal femur for fracture risk assessment.

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 Dual-energy 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.