Huber Markus B, Carballido-Gamio Julio, Bauer Jan S, Baum Thomas, Eckstein Felix, Lochmüller Eva M, Majumdar Sharmila, Link Thomas M
Musculoskeletal and Quantitative Imaging Research, Department of Radiology, University of California, San Francisco, 185 Berry St, Suite 350, San Francisco, CA 94107, USA.
Radiology. 2008 May;247(2):472-81. doi: 10.1148/radiol.2472070982.
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 compare that correlation with those observed at dual-energy x-ray absorptiometry (DXA) measurement of BMD.
The study was compliant with institutional and legislative requirements; donors had dedicated their body for education and research before death. Multidetector CT and DXA scans were acquired in 178 proximal femur specimens harvested from human cadavers (91 women, 87 men; mean age at death, 79 years +/- 10.2; range, 52-100 years). An automated VOI-fitting algorithm was 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 mechanical test. Quantitative CT BMD, quantitative CT pixel distribution parameters, DXA BMD, and FL were correlated at multiple regression analysis.
Mean precision errors in quantitative CT BMD measurements at segmentation with repositioning were 0.56%, 2.26%, and 0.61% for the head, neck, and trochanter, respectively. For the head, neck, and trochanter, respectively, r values were 0.77, 0.53, and 0.59 for the correlation between quantitative CT BMD and FL and 0.74, 0.55, and 0.65 for the correlation between quantitative CT BMC and FL (P < .001). Values ranged from 0.77 to 0.80 for correlations between DXA BMD and FL and from 0.73 to 0.82 for correlations between DXA BMC and FL (P < .001). In a multiple regression model that included quantitative CT pixel distributions, adjusted multivariate correlation coefficient values for correlations with FL increased to up to 0.88.
Regional BMD of the proximal femur can be determined in vitro from quantitative CT data with high precision by using an automated VOI-fitting algorithm. The best multiple regression model for predicting FL included DXA BMD and regional quantitative CT BMD measurements.
前瞻性评估一种用于近端股骨标本定量计算机断层扫描（CT）的自动感兴趣区（VOI）拟合算法，将骨密度（BMD）与体外生物力学测定的骨强度相关联，并将该相关性与双能X线吸收法（DXA）测量BMD时观察到的相关性进行比较。
本研究符合机构和法律要求；捐赠者在生前已将遗体奉献用于教育和研究。对从人类尸体获取的178个近端股骨标本（91名女性，87名男性；死亡时平均年龄79岁±10.2；范围52 - 100岁）进行多排CT和DXA扫描。使用自动VOI拟合算法根据CT结果和像素分布参数计算头部、颈部和大转子的BMD和骨矿物质含量（BMC）。通过力学测试确定股骨破坏载荷（FL）。在多元回归分析中对定量CT BMD、定量CT像素分布参数、DXA BMD和FL进行相关性分析。
重新定位分割时定量CT BMD测量的平均精度误差，头部、颈部和大转子分别为0.56%、2.26%和0.61%。对于头部、颈部和大转子，定量CT BMD与FL之间的相关性r值分别为0.77、0.53和0.59，定量CT BMC与FL之间的相关性r值分别为0.74、0.55和0.65（P <.001）。DXA BMD与FL之间的相关性值范围为0.77至0.80，DXA BMC与FL之间的相关性值范围为0.73至0.82（P <.001）。在包含定量CT像素分布的多元回归模型中，与FL相关性的调整多元相关系数值增加至高达0.88。
使用自动VOI拟合算法可在体外从定量CT数据高精度地确定近端股骨的区域BMD。预测FL的最佳多元回归模型包括DXA BMD和区域定量CT BMD测量值。
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