The effect of femur positioning on dual-energy X-ray absorptiometry (DXA) measures and statistical shape and appearance modeling (SSAM) fracture risk assessments.

The diagnosis of osteoporosis using Dual-energy X-ray Absorptiometry (DXA) relies on accurate hip scans, whereby variability in measurements may be introduced by altered patient positioning, as could occur with repeated scans over time. The goal herein was to test how altered postures affect diagnostic metrics (i.e., standard clinical metrics and a newer image processing tool) for femur positioning. A device was built to support cadaveric femurs and adjust their orientation in 3° increments in flexion and internal/external rotation. Seven isolated femurs were scanned in six flexion postures (0° (neutral) to 15° of flexion) and eleven rotational postures (15° external to 15° internal rotation) while collecting standard clinical DXA-based measures for each scan. The fracture risk tool was applied to each scan to calculate fracture risk. Two separate one-way repeated measures ANOVAs (α = 0.05) were performed on the DXA-based measures and fracture risk prediction output. Flexion had a significant effect on T-score, Bone Mineral Density (BMD), and Bone Mineral Content (BMC), but not area, at angles greater than 12°. Internal and external rotation did not have a significant effect on any clinical metric. Fracture risk (as assessed by the image processing tool) was not affected by either rotation mode. Overall, this suggests clinicians can adjust patient posture to accommodate discomfort if deviations are less than 12 degrees, and the greatest care should be taken in flexion. Furthermore, the tool is relatively insensitive to postural adjustments, and as such may be a good option for tracking risk over repeated patient scans.

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.

Comparing the fracture limits of the proximal femur under impact and quasi-static conditions in simulation of a sideways fall.

Sideways falls onto the hip are responsible for a great number of fractures in older adults. One of the possible ways to prevent these fractures is through early identification of people at greatest risk so that preventive measures can be properly implemented. Many numerical techniques that are designed to predict the femur fracture risk are validated through performing quasi-static (QS) mechanical tests on isolated cadaveric femurs, whereas the real hip fracture is a result of an impact (IM) incident. The goal of this study was to compare the fracture limits of the proximal femur under IM and QS conditions in the simulation of a sideways fall to identify any possible relationship between them. Eight pairs of fresh frozen cadaveric femurs were divided into two groups of QS and IM (left and right randomized). All femurs were scanned with a Hologic DXA scanner and then cut and potted in a cylindrical tube. To measure the stiffness in two conditions of the single-leg stance (SLS) and sideways fall (SWF), non-destructive tests at a QS displacement rate were performed on the two groups. For the destructive tests, the QS group was tested in SWF configuration with the rate of 0.017 mm/s using a material testing machine, and the IM group was tested in the same configuration inside a pneumatic IM device with the projectile target displacement rate of 3 m/s. One of the IM specimens was excluded due to multiple strikes. The result of this study showed that there were no significant differences in the SLS and SWF stiffnesses between the two groups (P = 0.15 and P = 0.64, respectively). The destructive test results indicated that there was a significant difference in the fracture loads of the two groups (P < 0.00001) with the impact ones being higher; however, they were moderately correlated (R2 = 0.45). Also, the comparison of the fracture location showed a qualitatively good agreement between the two groups. Using the relationship developed herein, results from another study were extrapolated with errors of less than 12%, showing that meaningful predictions for the impact scenario can be made based on the quasi-static tests. The result of this study suggests that there is a potential to replace IM tests with QS displacement rate tests, and this will provide important information that can be used for future studies evaluating clinical factors related to fracture risk.

Enhancing hip fracture risk prediction by statistical modeling and texture analysis on DXA images.

Each year in the US more than 300,000 older adults suffer from hip fractures. While protective measures exist, identification of those at greatest risk by DXA scanning has proved inadequate. This study proposed a new technique to enhance hip fracture risk prediction by accounting for many contributing factors to the strength of the proximal femur. Twenty-two isolated cadaveric femurs were DXA scanned, 16 of which had been mechanically tested to failure. A function consisting of the calculated modes from the statistical shape and appearance modeling (to consider the shape and BMD distribution), homogeneity index (representing trabecular quality), BMD, age and sex of the donor was created in a training set and used to predict the fracture load in a test group. To classify patients as "high risk" or "low risk", fracture load thresholds were investigated. Hip fracture load estimation was significantly enhanced using the new technique in comparison to using t-score or BMD alone (average R² of 0.68, 0.32, and 0.50, respectively) (P < 0.05). Using a fracture cut-off of 3400 N correctly predicted risk in 94% of specimens, a substantial improvement over t-score classification (38%). Ultimately, by identifying patients at high risk more accurately, devastating hip fractures can be prevented through applying protective measures.