Literature DB >> 20950723

In vivo micro-computed tomography allows direct three-dimensional quantification of both bone formation and bone resorption parameters using time-lapsed imaging.

Friederike A Schulte1, Floor M Lambers, Gisela Kuhn, Ralph Müller.   

Abstract

Bone is a living tissue able to adapt its structure to external influences such as altered mechanical loading. This adaptation process is governed by two distinct cell types: bone-forming cells called osteoblasts and bone-resorbing cells called osteoclasts. It is therefore of particular interest to have quantitative access to the outcomes of bone formation and resorption separately. This article presents a non-invasive three-dimensional technique to directly extract bone formation and resorption parameters from time-lapsed in vivo micro-computed tomography scans. This includes parameters such as Mineralizing Surface (MS), Mineral Apposition Rate (MAR), and Bone Formation Rate (BFR), which were defined in accordance to the current nomenclature of dynamic histomorphometry. Due to the time-lapsed and non-destructive nature of in vivo micro-computed tomography, not only formation but also resorption can now be assessed quantitatively and time-dependent parameters Eroded Surface (ES) as well as newly defined indices Mineral Resorption Rate (MRR) and Bone Resorption Rate (BRR) are introduced. For validation purposes, dynamic formation parameters were compared to the traditional quantitative measures of dynamic histomorphometry, where MAR correlated with R = 0.68 and MS with R = 0.78 (p < 0.05). Reproducibility was assessed in 8 samples that were scanned 5 times and errors ranged from 0.9% (MRR) to 6.6% (BRR). Furthermore, the new parameters were applied to a murine in vivo loading model. A comparison of directly extracted parameters between formation and resorption within each animal revealed that in the control group, i.e., during normal remodeling, MAR was significantly lower than MRR (p < 0.01), whereas MS compared to ES was significantly higher (p < 0.0001). This implies that normal remodeling seems to take place by many small formation packets and few but large resorption volumes. After 4 weeks of mechanical loading, newly extracted trabecular BFR and MS were significantly higher (p < 0.01) in the loading compared to the control group. At the same time, ES was significantly decreased (p < 0.01). This indicates that modeling induced by mechanical loading takes place primarily by increased area, not width of formation packets. With these results, we conclude that the non-invasive direct technique is well suited to extract dynamic bone morphometry parameters and eventually gain more insight into the processes of bone adaptation not only for formation but also resorption.
Copyright © 2010 Elsevier Inc. All rights reserved.

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Year:  2010        PMID: 20950723     DOI: 10.1016/j.bone.2010.10.007

Source DB:  PubMed          Journal:  Bone        ISSN: 1873-2763            Impact factor:   4.398


  47 in total

1.  Cylinders or walls? A new computational model to estimate the MR transverse relaxation rate dependence on trabecular bone architecture.

Authors:  Bernd Müller-Bierl; Olivia Louis; Yves Fierens; Nico Buls; Robert Luypaert; Johan de Mey
Journal:  MAGMA       Date:  2013-09-06       Impact factor: 2.310

Review 2.  Microarchitectural changes in the aging skeleton.

Authors:  Yankel Gabet; Itai Bab
Journal:  Curr Osteoporos Rep       Date:  2011-12       Impact factor: 5.096

Review 3.  In vivo Visualisation and Quantification of Bone Resorption and Bone Formation from Time-Lapse Imaging.

Authors:  Patrik Christen; Ralph Müller
Journal:  Curr Osteoporos Rep       Date:  2017-08       Impact factor: 5.096

4.  The influence of curvature on three-dimensional mineralized matrix formation under static and perfused conditions: an in vitro bioreactor model.

Authors:  Jolanda R Vetsch; Ralph Müller; Sandra Hofmann
Journal:  J R Soc Interface       Date:  2016-10       Impact factor: 4.118

5.  Mechanical regulation of bone formation and resorption around implants in a mouse model of osteopenic bone.

Authors:  Zihui Li; Duncan Betts; Gisela Kuhn; Michael Schirmer; Ralph Müller; Davide Ruffoni
Journal:  J R Soc Interface       Date:  2019-03-29       Impact factor: 4.118

6.  Quantitative analysis of bone and soft tissue by micro-computed tomography: applications to ex vivo and in vivo studies.

Authors:  Graeme M Campbell; Antonia Sophocleous
Journal:  Bonekey Rep       Date:  2014-08-20

7.  Time course of rapid bone loss and cortical porosity formation observed by longitudinal μCT in a rat model of CKD.

Authors:  Erin M B McNerny; Dorothy T Buening; Mohammad W Aref; Neal X Chen; Sharon M Moe; Matthew R Allen
Journal:  Bone       Date:  2019-05-03       Impact factor: 4.398

Review 8.  The use of bone mineral density measured by dual energy X-ray absorptiometry (DXA) and peripheral quantitative computed microtomography in chronic kidney disease.

Authors:  Martin Jannot; Fabrice Mac-Way; Vanessa Lapierre; Marie-Helene Lafage-Proust
Journal:  J Nephrol       Date:  2017-09-12       Impact factor: 3.902

9.  Quantification of skeletal growth, modeling, and remodeling by in vivo micro computed tomography.

Authors:  Allison R Altman; Wei-Ju Tseng; Chantal M J de Bakker; Abhishek Chandra; Shenghui Lan; Beom Kang Huh; Shiming Luo; Mary B Leonard; Ling Qin; X Sherry Liu
Journal:  Bone       Date:  2015-08-06       Impact factor: 4.398

Review 10.  Studying osteocytes within their environment.

Authors:  Duncan J Webster; Philipp Schneider; Sarah L Dallas; Ralph Müller
Journal:  Bone       Date:  2013-01-11       Impact factor: 4.398

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