Development of a protocol to quantify local bone adaptation over space and time: Quantification of reproducibility

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• 作者：Yongtao Lu^a ; ^b ; ^{yongtaolu@dlut.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; ^{yongtaolu@hotmail.com" class="auth_mail" title="E-mail the corresponding author} ; Maya Boudiffa^c ; Enrico Dall&rsquo ; Ara^c ; Ilaria Bellantuono^c ; Marco Viceconti^b
• 关键词：In vivo micro-CT ; Local bone adaptation ; Mouse tibia ; Space and time
• 刊名：Journal of Biomechanics
• 出版年：2016
• 出版时间：5 July 2016
• 年：2016
• 卷：49
• 期：10
• 页码：2095-2099
• 全文大小：2505 K

文摘

In vivo micro-computed tomography (µCT) scanning of small rodents is a powerful method for longitudinal monitoring of bone adaptation. However, the life-time bone growth in small rodents makes it a challenge to quantify local bone adaptation. Therefore, the aim of this study was to develop a protocol, which can take into account large bone growth, to quantify local bone adaptations over space and time. The entire right tibiae of eight 14-week-old C57BL/6J female mice were consecutively scanned four times in an in vivo µCT scanner using a nominal isotropic image voxel size of 10.4 µm. The repeated scan image datasets were aligned to the corresponding baseline (first) scan image dataset using rigid registration. 80% of tibia length (starting from the endpoint of the proximal growth plate) was selected as the volume of interest and partitioned into 40 regions along the tibial long axis (10 divisions) and in the cross-section (4 sectors). The bone mineral content (BMC) was used to quantify bone adaptation and was calculated in each region. All local BMCs have precision errors (PE_%CV) of less than 3.5% (24 out of 40 regions have PE_%CV of less than 2%), least significant changes (LSCs) of less than 3.8%, and 38 out of 40 regions have intraclass correlation coefficients (ICCs) of over 0.8. The proposed protocol allows to quantify local bone adaptations over an entire tibia in longitudinal studies, with a high reproducibility, an essential requirement to reduce the number of animals to achieve the necessary statistical power.