Annals of Biomedical Engineering

, Volume 34, Issue 10, pp 1587–1599 | Cite as

Evaluation of Three-dimensional Image Registration Methodologies for In Vivo Micro-computed Tomography

  • Steven K. BoydEmail author
  • Stephan Moser
  • Michael Kuhn
  • Robert J. Klinck
  • Peter L. Krauze
  • Ralph Müller
  • Jürg A. Gasser


The advent of in vivo micro-computed tomography (micro-CT) provides a novel approach to measure the temporal adaptation of bone micro-architecture within an individual. Spatial alignment in the scanner between serial scans is challenging, but three-dimensional image registration can be used to superimpose the resulting image data, thus ensuring consistent regions of interest (ROI) for analysis. There have been several approaches to image registration developed, yet little is known about their application to high resolution micro-CT data. The purpose of this study was to explore combinations of three image registration similarity measures and three image interpolators, in addition to multi-resolution registration configurations, for assessment of computational efficiency and accuracy on both in vitro and in vivo micro-CT data. Accuracy measures were assessed by comparison with a gold-standard reference transform based on attached fiducial markers. It was concluded that a mutual information registration similarity measure with a linear image interpolator, applied at steps of increasing image resolution, provided the best compromise between accurate and efficient results. In vivo registration of tibial bone microstructure measured in an ovariectomized rat model provided consistent ROI thus demonstrating the usefulness of three-dimensional image registration for in vivo experimental and clinical micro-CT research. It is a technique that is poised to become commonly utilized for analysis of micro-CT data to diagnose and monitor efficacy of therapy in bone diseases.


Image registration Micro-computed tomography Osteoporosis Rat models Tibial bone micro-structure 



The authors would like to acknowledge JM Fitzpatrick and D Hill for their discussions on how to assess image registration accuracy, and for providing the Matlab code to calculate TRE. This work benefited from the use of the Insight Segmentation and Registration Toolkit (ITK) and the Visualization Toolkit, which are open source software packages available at and


  1. 1.
    Boone J. M., Velazquez O., Cherry S. R., (2004) Small-animal X-ray dose from micro-CT Mol. Imaging 3:149–158PubMedCrossRefGoogle Scholar
  2. 2.
    Boutroy S., Bouxsein M. L., Munoz F., Delmas P. D., (2005) In vivo assessment of trabecular bone microarchitecture by high-resolution peripheral quantitative computed tomography J. Clin. Endocrinol. Metab. 90:6508–6515PubMedCrossRefGoogle Scholar
  3. 3.
    Boyd, S. K., P. Davison, R. Müller, and J. A. Gasser. Monitoring individual morphological changes over time in ovariectomized rats by in vivo micro-computed tomography. Bone, Epub 2006 Jun 4, 2006. doi:10.1016/j.bone.2006.04.017Google Scholar
  4. 4.
    Boyd, S. K., M. Kuhn, S. Moser, P. Krauze, R.J. Klinck, C. Mattmann, A. Kuhn, and R. Müller. Three-dimensional image registration for longitudinal site-specific measure of bone adaptation. Proc. Eur. Soc. Biomech.514, 2004, ‘s-Hertogenbosch, NetherlandsGoogle Scholar
  5. 5.
    Boyd S. K., Mattmann C., Kuhn A., Müller R., Gasser J. A., (2004) A novel approach for monitoring and predicting bone microstructure in osteoporosis J. Bone Miner. Res. 19:S236–237Google Scholar
  6. 6.
    Brandt R., Rohlfing T., Rybak J., Krofczik S., Maye A., Westerhoff M., Hege H. C., Menzel R., (2005) Three-dimensional average-shape atlas of the honeybee brain and its applications J. Comp. Neurol. 492:1–19PubMedCrossRefGoogle Scholar
  7. 7.
    David V., Laroche N., Boudignon B., Lafage-Proust M. H., Alexandre C., Rüegsegger P., Vico L., (2003) Noninvasive in vivo monitoring of bone architecture alterations in hindlimb-unloaded female rats using novel three-dimensional microcomputed tomography J. Bone Miner. Res. 18:1622–1631PubMedCrossRefGoogle Scholar
  8. 8.
    Fitzpatrick J. M. (2001) Detecting failure, assessing success. In: Hajnal J. V., Hill D. L. G., Hawkes D. J. (eds) Medical Image Registration. CRC Press, Boca Raton, pp. 117–139Google Scholar
  9. 9.
    Fitzpatrick J. M., West J. B., Maurer C. R. Jr. (1998) Predicting error in rigid-body point-based registration IEEE Trans. Med. Imaging 17:694–702PubMedCrossRefGoogle Scholar
  10. 10.
    Ford N. L., Thornton M. M., Holdsworth D. W., (2003) Fundamental image quality limits for microcomputed tomography in small animals Med. Phys. 30:2869–2877PubMedCrossRefGoogle Scholar
  11. 11.
    Hajnal J. V., Saeed N., Soar E. J., Oatridge A., Young I. R., Bydder G. M., (1995) A registration and interpolation procedure for subvoxel matching of serially acquired MR images J. Comput. Assist. Tomogr. 19:289–296PubMedCrossRefGoogle Scholar
  12. 12.
    Hildebrand T., Rüegsegger P., (1997a) A new method for the model-independent assessment of thickness in three-dimensional images J. Microsc. 185:67–75CrossRefGoogle Scholar
  13. 13.
    Hildebrand T., Rüegsegger P., (1997b) Quantification of bone microarchitecture with the structure model index Comput. Methods Biomech. Biomed. Eng. 1:15–23Google Scholar
  14. 14.
    Hill D. L. G., Batchelor P., (2001) Registration methodology: concepts and algorithms. In: Hajnal J. V., Hill D. L. G., Hawkes D. J. (eds) Medical Image Registration. CRC Press, Boca Raton, FL, pp. 39–70Google Scholar
  15. 15.
    Hill D. L. G., Maurer C. R., Studholme C., Fitzpatrick J. M., Hawkes D. J., (1998) Correcting scaling errors in tomographic images using a nine degree of freedom registration algorithm J. Comput. Assist. Tomogr. 22:317–323PubMedCrossRefGoogle Scholar
  16. 16.
    Holden M., Hill D. L., Denton E. R., Jarosz J. M., Cox T. C., Rohlfing T., Goodey J., Hawkes D. J., (2000) Voxel similarity measures for 3-D serial MR brain image registration IEEE Trans. Med. Imaging 19:94–102PubMedCrossRefGoogle Scholar
  17. 17.
    Hollister S. J., Fyhrie D. P., Jepsen K. J., Goldstein S. A., (1991) Application of homogenization theory to the study of trabecular bone mechanics J. Biomech. 24:825–839PubMedCrossRefGoogle Scholar
  18. 18.
    Khosla S., Riggs B. L., Atkinson E. J., Oberg A. L., McDaniel L. J., Holets M., Peterson J. M., Melton L. J. 3rd (2006) Effects of sex and age on bone microstructure at the ultradistal radius: a population-based noninvasive in vivo assessment J. Bone Miner. Res. 21:124–131PubMedCrossRefGoogle Scholar
  19. 19.
    Kuhn, A., C. Mattmann, J. A. Gasser, R. Müller, and S. K. Boyd. Site specific micro-architectural bone changes with in vivo computed tomography. 16th International Bone Densitometry Meeting, 84, Annecy, France, 2004Google Scholar
  20. 20.
    Laib A., Kumer J. L., Majumdar S., Lane N. E., (2001) The temporal changes of trabecular architecture in ovariectomized rats assessed by MicroCT Osteoporos. Int. 12:936–941PubMedCrossRefGoogle Scholar
  21. 21.
    Lane N. E., Haupt D., Kimmel D. B., Modin G., Kinney J. H., (1999) Early estrogen replacement therapy reverses the rapid loss of trabecular bone volume and prevents further deterioration of connectivity in the rat J. Bone Miner. Res. 14:206–214PubMedCrossRefGoogle Scholar
  22. 22.
    Lehmann L. A., Gonner C., Spitzer K., (1999) Survey: interpolation methods in medical image processing IEEE Trans. Med. Imaging 18:1049–1075PubMedCrossRefGoogle Scholar
  23. 23.
    Lorensen W. E., Cline H. E., (1987) Marching cubes: a high resolution 3D surface construction algorithm Comput. Graphics 21:163–169Google Scholar
  24. 24.
    Maes F., Collignon A., Vandermeulen D., Marchal G., Suetens P., (1997) Multimodality image registration by maximization of mutual information IEEE Trans. Med. Imaging 16:187–198PubMedCrossRefGoogle Scholar
  25. 25.
    Maes F., Vandermeulen D., Suetens P., (1999) Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information Med. Image Anal. 3:373–386PubMedCrossRefGoogle Scholar
  26. 26.
    Mattes, D., D. R. Haynor, H. Vesselle, T. K. Lewellen, and W. Eubank. “Non-rigid multi-modality image registration.” In: (ed) Medical Imaging 2001: Image Processing. 2001, pp. 1609–1620Google Scholar
  27. 27.
    Mattes D., Haynor D. R., Vesselle H., Lewellen T. K., Eubank W., (2003) PET-CT image registration in the chest using free-form deformations IEEE Trans. Med. Imaging 22:120–128PubMedCrossRefGoogle Scholar
  28. 28.
    Odgaard A., Gundersen H. J., (1993) Quantification of connectivity in cancellous bone, with special emphasis on 3-D reconstructions Bone 14:173–182PubMedCrossRefGoogle Scholar
  29. 29.
    Pluim J. P., Maintz J. B. A., Viergever M. A., (2000) Interpolation artefacts in mutual information-based image registration CVIU 77:211–232Google Scholar
  30. 30.
    Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B.P. “Numerical recipes in C: the art of scientific computing.” In: edited by Cambridge: Cambridge University Press, 1992, pp. 105–128Google Scholar
  31. 31.
    Ritman E. L. (2004) Micro-computed tomography-current status and developments Annu. Rev. Biomed. Eng. 6:185–208PubMedCrossRefGoogle Scholar
  32. 32.
    Rohlfing, T. “Incremental method for computing the intersection of discretely sampled m-dimensional images with n-dimensional boundaries.” In: Sonka M., Fitzpatrick J. M. (eds) Medical Imaging: Image Processing. 2003, pp. 1346–1354Google Scholar
  33. 33.
    Rüegsegger P., Koller B., Müller R., (1996) A microtomographic system for the nondestructive evaluation of bone architecture Calcif. Tissue Int. 58:24–29PubMedCrossRefGoogle Scholar
  34. 34.
    Söderkvist I., Wedin P. A., (1993) Determining the movements of the skeleton using well-configured markers J. Biomech. 26:1473–1477PubMedCrossRefGoogle Scholar
  35. 35.
    Sonka, M. and J. M. Fitzpatrick. Handbook of medical imaging-volume 2. medical image processing and analysis. SPIE Press, BellinghamGoogle Scholar
  36. 36.
    Studholme C., Hill D. L., Hawkes D. J., (1997) Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures Med. Phys. 24:25–35PubMedCrossRefGoogle Scholar
  37. 37.
    Van Rietbergen B., Weinans H., Huiskes R., Odgaard A., (1995) A new method to determine trabecular bone elastic properties and loading using micromechanical finite-element models J. Biomech. 28:69–81PubMedCrossRefGoogle Scholar
  38. 38.
    Viola P., Wells W. M., (1997) Alignment by maximization of mutual information Int. J. Comput. Vision 24:137–154CrossRefGoogle Scholar
  39. 39.
    Waarsing J. H., Day J. S., van der Linden J. C., Ederveen A. G., Spanjers C., De Clerck N., Sasov A., Verhaar J. A., Weinans H., (2004) Detecting and tracking local changes in the tibiae of individual rats: a novel method to analyse longitudinal in vivo micro-CT data Bone 34:163–169PubMedCrossRefGoogle Scholar
  40. 40.
    Wells W. M., Viola P., Atsumi H., Nakajima S., Kikinis R., (1996) Multi-modal volume registration by maximization of mutual information Med. Image Anal. 1:35–51PubMedCrossRefGoogle Scholar
  41. 41.
    Wronski T. J., Cintron M., Dann L. M., (1988) Temporal relationship between bone loss and increased bone turnover in ovariectomized rats Calcif. Tissue Int. 43:179–183PubMedGoogle Scholar
  42. 42.
    Yoo T. S., Ackerman M. J., Lorensen W. E., Schroeder W., Chalana V., Aylward S., Metaxes D., Whitaker R., (2002) “Engineering and algorithm design for an image processing API: a technical report on ITK – The Insight Toolkit.” In: Westwood J. (ed) Proc of Medicine Meets Virtual Reality. IOS Press, Amsterdam, pp. 586–592Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Steven K. Boyd
    • 1
    Email author
  • Stephan Moser
    • 1
    • 2
  • Michael Kuhn
    • 1
    • 2
  • Robert J. Klinck
    • 1
  • Peter L. Krauze
    • 1
  • Ralph Müller
    • 2
  • Jürg A. Gasser
    • 3
  1. 1.Department of Mechanical and Manufacturing Engineering, Schulich School of EngineeringUniversity of CalgaryCalgaryCanada
  2. 2.Institute for Biomedical EngineeringUniversity and ETH ZürichZürichSwitzerland
  3. 3.Novartis Institutes for Biomedical ResearchBaselSwitzerland

Personalised recommendations