Proximal Femur Specimens: Automated 3D Trabecular Bone Mineral Density Analysis at Multidetector CT--Correlation with Biomechanical Strength Measurement

doi:10.1148/radiol.2472070982

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Musculoskeletal Imaging

Proximal Femur Specimens: Automated 3D Trabecular Bone Mineral Density Analysis at Multidetector CT—Correlation with Biomechanical Strength Measurement¹

Markus B. Huber, MSc, Julio Carballido-Gamio, PhD, Jan S. Bauer, MD, Thomas Baum, Felix Eckstein, MD, Eva M. Lochmüller, MD, Sharmila Majumdar, PhD, and Thomas M. Link, MD

¹ From Musculoskeletal and Quantitative Imaging Research, Department of Radiology, University of California, San Francisco, 185 Berry St, Suite 350, San Francisco, CA 94107 (M.B.H., J.C., S.M., T.M.L.); Institut für Röntgendiagnostik, Technische Universität München, Munich, Germany (J.S.B., T.B.); Institute of Anatomy and Musculoskeletal Research, Paracelsus Private Medical University Salzburg, Salzburg, Austria (F.E.); and First University Hospital of Gynecology, Ludwig-Maximilians-Universität, Munich, Germany (E.M.L.). Received June 6, 2007; revision requested August 6; revision received September 6; final version accepted October 4. Supported by AO Foundation grant "Fracture Fixation in Osteoporotic Bone," Project X-ray and CT-Analysis, and German Research Society Foundation grant (DFG) LO 730/3-1. Address correspondence to M.B.H. (e-mail: mbh{at}radiology.ucsf.edu).

Purpose: To prospectively evaluate an automated volume of interest(VOI)-fitting algorithm for quantitative computed tomography (CT)of proximal femur specimens, correlate bone mineral density (BMD)with biomechanically determined bone strength in vitro, and comparethat correlation with those observed at dual-energy x-ray absorptiometry(DXA) measurement of BMD.

Materials and Methods: The study was compliant with institutionaland legislative requirements; donors had dedicated their body foreducation and research before death. Multidetector CT and DXA scanswere acquired in 178 proximal femur specimens harvested from humancadavers (91 women, 87 men; mean age at death, 79 years ±10.2; range, 52–100 years). An automated VOI-fitting algorithmwas used to calculate BMD and bone mineral content (BMC) in the head,neck, and trochanter from CT findings and pixel distribution parameters.The femur failure load (FL) was determined by using a mechanicaltest. Quantitative CT BMD, quantitative CT pixel distribution parameters,DXA BMD, and FL were correlated at multiple regression analysis.

Results: Mean precision errors in quantitative CT BMD measurementsat segmentation with repositioning were 0.56%, 2.26%, and 0.61% forthe head, neck, and trochanter, respectively. For the head, neck,and trochanter, respectively, r values were 0.77, 0.53, and 0.59for the correlation between quantitative CT BMD and FL and 0.74,0.55, and 0.65 for the correlation between quantitative CT BMC andFL (P < .001). Values ranged from 0.77 to 0.80 for correlationsbetween DXA BMD and FL and from 0.73 to 0.82 for correlations betweenDXA BMC and FL (P < .001). In a multiple regression model thatincluded quantitative CT pixel distributions, adjusted multivariatecorrelation coefficient values for correlations with FL increasedto up to 0.88.

Conclusion: Regional BMD of the proximal femur can be determinedin vitro from quantitative CT data with high precision by using anautomated VOI-fitting algorithm. The best multiple regression modelfor predicting FL included DXA BMD and regional quantitative CT BMDmeasurements.