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Characterization of Rotating Gantry Micro-CT Configuration for the In Vivo Evaluation of Murine Trabecular Bone

Published online by Cambridge University Press:  30 May 2013

Luke Arentsen  and
Susanta Hui
Luke Arentsen
Affiliation:
Biophysical Science and Medical Physics, University of Minnesota, Minneapolis, MN 55455, USA
Susanta Hui*
Affiliation:
Biophysical Science and Medical Physics, University of Minnesota, Minneapolis, MN 55455, USA
*
*Corresponding author. E-mail: [email protected]
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Abstract

The objective of this study is to determine the optimal physical parameters of a rotating gantry micro-CT system to assess in vivo changes to the trabecular bone of mice. Magnification, binning, peak kilovoltage, beam filtration, and tissue thickness are examined on a commercially available micro-CT system. The X-ray source and detector geometry provides 1.3×, 1.8×, or 3.3× magnification. Binning is examined from no binning to 2 to 4. Energy is varied from 40 to 80 kVp in 10 kVp increments and filter thickness is increased from no filtration to 1.5 mmAl in 0.5 mmAl increments. Mice are imaged at different magnifications and binning combinations to evaluate changes to image quality and microstructure estimation. Increasing magnification from 1.3× to 3.3× and lowering binning from 4 to 1 varies the spatial resolution from 2.5 to 11.8 lp/mm. Increasing the beam energy or filtration thickness decreases Hounsfield unit (HU) estimation, with a maximum rate of change being −286 HU/kVp for 80 kVp. Images for murine trabecular bone are blurred at effective pixel sizes above 60 μm. By comparing resolution, signal-to-noise ratio, and radiation dose, we find that a 3.3× magnification, binning of 2.80 kVp beam with a 0.5 mmAl filter comprises the optimal parameters to evaluate murine trabecular bone for this rotating gantry micro-CT.

Keywords

rotating gantry micro-CTmouse trabecular bonemagnificationbinningfilter thicknesspeak energy
Type
Biological Applications
Information
Microscopy and Microanalysis , Volume 19 , Issue 4 , August 2013 , pp. 907 - 913
Copyright
Copyright © Microscopy Society of America 2013 

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References

Bouxsein, M.L., Boyd, S.K., Christiansen, B.A., Guldberg, R.E., Jepsen, K.J. & Muller, R. (2010). Guidelines for assessment of bone microstructure in rodents using micro-computed tomography. J Bone Miner Res 25(7), 1468–1486.CrossRefGoogle ScholarPubMed
Cao, G., Lee, Y.Z., Peng, R., Liu, Z., Rajaram, R., Calderon-Colon, X., An, L., Wang, P., Phan, T., Sultana, S., Lalush, D.S., Lu, J.P. & Zhou, O. (2009). A dynamic micro-CT scanner based on a carbon nanotube field emission X-ray source. Phys Med Biol 54(8), 2323–2340.Google Scholar
Du, L.Y., Umoh, J., Nikolov, H.N., Pollmann, S.I., Lee, T.Y. & Holdsworth, D.W. (2007). A quality assurance phantom for the performance evaluation of volumetric micro-CT systems. Phys Med Biol 52(23), 7087–7108.CrossRefGoogle ScholarPubMed
Duerinckx, A.J. & Macovski, A. (1978). Polychromatic streak artifacts in computed tomography images. J Comput Assist Tomogr 2(4), 481–487.Google Scholar
Ford, N.L., Thornton, M.M. & Holdsworth, D.W. (2003). Fundamental image quality limits for microcomputed tomography in small animals. Med Phys 30(11), 2869–2877.CrossRefGoogle ScholarPubMed
Goodsitt, M. (1994). The composition of bone marrow for a dual-energy quantitative computed tomography technique. Invest Radiol 29(7), 695–704.CrossRefGoogle ScholarPubMed
Holdsworth, D.W. & Thornton, M.M. (2002). Micro-CT in small animal and specimen imaging. Trends Biotechnol 20(8), S34–S39.Google Scholar
Hui, S.K., Fairchild, G.R., Kidder, L.S., Sharma, M., Bhattacharya, M., Jackson, S., Le, C. & Yee, D. (2012). Skeletal remodeling following clinically relevant radiation-induced bone damage treated with zoledronic acid. Calcif Tissue Int 90(1), 40–49.Google Scholar
Jiang, Y., Zhao, J., Liao, E.Y., Dai, R.C., Wu, X.P. & Genant, H.K. (2005). Application of micro-CT assessment of 3-D bone microstructure in preclinical and clinical studies. J Bone Miner Metab 23(Suppl), 122–131.Google Scholar
Khodaverdi, M. (2005). Investigation of different microCT scanner configurations by GEANT4 simulations. IEEE Trans Nucl Sci 52(1), 188–192.Google Scholar
Kinney, J.H., Lane, N.E. & Haupt, D.L. (1995). In vivo, three-dimensional microscopy of trabecular bone. J Bone Miner Res 10(2), 264–270.CrossRefGoogle ScholarPubMed
Laperre, K., Depypere, M., van Gastel, N., Torrekens, S., Moermans, K., Bogaerts, R., Maes, F. & Carmeliet, G. (2011). Development of micro-CT protocols for in vivo follow-up of mouse bone architecture without major radiation side effects. Bone 49(4), 613–622.Google Scholar
Lenox, M. (2008). Transmission measurements using iterative conebeam reconstruction on the Inveon DPET. In IEEE Nuclear Science Symposium Conference Record, Dresden, Germany, October 19–25, 2008, pp. 4711–4717.Google Scholar
Magota, K. (2011). Performance characterization of the Inveon preclinical small-animal PET/SPECT/CT system for multimodality imaging. Eur J Nucl Med Mol Imaging 38, 742–752.Google Scholar
Meganck, J.A., Kozloff, K.M., Thornton, M.M., Broski, S.M. & Goldstein, S.A. (2009). Beam hardening artifacts in micro-computed tomography scanning can be reduced by X-ray beam filtration and the resulting images can be used to accurately measure BMD. Bone 45(6), 1104–1116.Google Scholar
Moya, U. & Brandan, M. (2010). Parameterization of X-ray spectra appropriate for microCT scanners. Nucl Instr Meth Phys Res: Part A 613, 152–155.Google Scholar
Mulder, L., Koolstra, J.H. & van Eijden, T.M. (2004). Accuracy of microCT in the quantitative determination of the degree and distribution of mineralization in developing bone. Acta Radio 7, 769–777.Google Scholar
Peyrin, F., Salome, M., Cloetens, P., Laval-Jeantet, A.M., Ritman, E. & Ruegsegger, P. (1998). Micro-CT examinations of trabecular bone samples at different resolutions: 14, 7 and 2 micron level. Technol Health Care 6(5-6), 391–401.Google Scholar
Rasband, W.S. (1997–2012). ImageJ. Bethesda, MD: U.S. National Institutes of Health. Available at http://imagej.nih.gov/ij/.Google Scholar
Ritman, E.L. (2002). Molecular imaging in small animals—Roles for micro-CT. J Cell Biochem 39, 116–124.Google Scholar
Rossmann, K. (1969). Point spread-function, line spread-function, and modulation transfer function. Tools for the study of imaging systems. Radiology 93(2), 257–272.Google Scholar
Shrimpton, P.C., Jones, D.G. & Wall, B.F. (1988). The influence of tube filtration and potential on patient dose during X-ray examinations. Phys Med Biol 33(10), 1205–1212.Google Scholar
Spowage, A.C., Shacklock, A.P., Malcolm, A.A., May, S.L., Tong, L. & Kennedy, A.R. (2006). Development of characterisation methodologies for macroporous materials. J Porous Mater 13(3-4), 431–438.Google Scholar
Storer, J. (1967). Relation of lifespan to brain weight, body weight, and metabolic rate among inbred mouse strains. Exp Gerontol 2(3), 173–182.Google Scholar
Thurner, P., Beckmann, F. & Muller, B. (2004). An optimization procedure for spatial and density resolution in hard X-ray micro-computed tomography. Nucl Instrum Meth Phys Res: Part B 225, 599–603.Google Scholar