The 3D-based scaling index algorithm: a new structure measure to analyze trabecular bone architecture in high-resolution MR images in vivo.

INTRODUCTION

The purpose of this study was to obtain different structure measures as the three-dimensional (3D)-based scaling index method (SIM) and standard two-dimensional (2D) bone histomorphometric parameters from high-resolution (HR) magnetic resonance (MR) images of the distal radius and to compare these parameters with bone mineral density (BMD) in their diagnostic performance to differentiate postmenopausal patients with and without vertebral fractures.

METHODS

Axial HR-MR images of the distal radius were obtained at 1.5 T in 40 postmenopausal women (17 with osteoporotic spine fractures and 23 controls). Trabecular microarchitecture analysis was performed using the new structure measure mP(alpha), derived from the SIM, as well as standard morphological 2D parameters. BMD of the spine was obtained using quantitative computed tomography (QCT). Receiver operating characteristic (ROC) analyses were used to determine diagnostic performance in differentiating both groups. Results were validated by bootstrapping techniques.

RESULTS

Significant differences between both patient groups were obtained using mP(alpha), 2D parameters, and spine BMD (p<0.05). In comparison with the 2D texture parameters [area under the curve (AUC) up to 0.67], diagnostic performance was significantly higher for mP(alpha)(AUC=0.85; p<0.05). There was a trend for a higher AUC value for mP(alpha) compared with BMD of the spine (AUC=0.71; p=0.81).

CONCLUSION

mP(alpha) yielded a robust measure of trabecular bone microarchitecture for HR-MR images of the radius, which significantly improved the diagnostic performance in differentiating postmenopausal women with and without osteoporotic spine fractures compared with standard 2D bone histomorphometric parameters. This 3D characterization of trabecular microarchitecture may provide a new approach to better assess the strength of human cancellous bone using HR-MR image data.