• Osteoporos Int · Dec 2009

    Automated simulation of areal bone mineral density assessment in the distal radius from high-resolution peripheral quantitative computed tomography.

    • A J Burghardt, G J Kazakia, T M Link, and S Majumdar.
    • Musculoskeletal Quantitative Imaging Research Group, Department of Radiology and Biomedical Imaging, University of California, San Francisco, QB3 Building, Suite 203, 1700 4th St, San Francisco, CA 94158, USA. andrew.burghardt@radiology.ucsf.edu
    • Osteoporos Int. 2009 Dec 1; 20 (12): 2017-24.

    SummaryAn automated image processing method is presented for simulating areal bone mineral density measures using high-resolution peripheral quantitative computed tomography (HR-pQCT) in the ultra-distal radius. The accuracy of the method is validated against clinical dual X-ray absorptiometry (DXA). This technique represents a useful reference to gauge the utility of novel 3D quantification methods applied to HR-pQCT in multi-center clinical studies and potentially negates the need for separate forearm DXA measurements.IntroductionOsteoporotic status is primarily assessed by measuring areal bone mineral density (aBMD) using 2D dual X-ray absorptiometry (DXA). However, this technique does not sufficiently explain bone strength and fracture risk. High-resolution peripheral quantitative computed tomography (HR-pQCT) has been introduced as a method to quantify 3D bone microstructure and biomechanics. In this study, an automated method is proposed to simulate aBMD measures from HR-pQCT distal radius images.MethodsA total of 117 subject scans were retrospectively analyzed from two clinical bone quality studies. The distal radius was imaged by HR-pQCT and DXA on one of two devices (Hologic or Lunar). Areal BMD was calculated by simulation from HR-pQCT images (aBMD(sim)) and by standard DXA analysis (aBMD(dxa)).ResultsThe reproducibility of the simulation technique was 1.1% (root mean-squared coefficient of variation). HR-pQCT-based aBMD(sim) correlated strongly to aBMD(dxa) (Hologic: R (2) = 0.82, Lunar: R (2) = 0.87), though aBMD(sim) underestimated aBMD(dxa) for both DXA devices (p < 0.0001). Finally, aBMD(sim) predicted aBMD at the proximal femur and lumbar spine with equal power compared to aBMD(dxa).ConclusionThe results demonstrate that aBMD can be simulated from HR-pQCT images of the distal radius. This approach has the potential to serve as a surrogate forearm aBMD measure for clinical HR-pQCT studies when axial bone mineral density values are not required.

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