Affine Coregistration of Diffusion Tensor Magnetic Resonance Images Using Mutual Information

  • Alexander Leemans
  • Jan Sijbers
  • Steve De Backer
  • Everhard Vandervliet
  • Paul M. Parizel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3708)


In this paper, we present an affine image coregistration technique for Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) data sets based on mutual information. The technique is based on a multi-channel approach where the diffusion weighted images are aligned according to the corresponding acquisition gradient directions. Also, in addition to the coregistration of the DT-MRI data sets, an appropriate reorientation of the diffusion tensor is worked out in order to remain consistent with the corresponding underlying anatomical structures. This reorientation strategy is determined from the spatial transformation while preserving the diffusion tensor shape. The method is fully automatic and has the advantage to be independent of the applied diffusion framework.


Mutual Information Fractional Anisotropy Reference Image Spatial Transformation Medical Image Registration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Brown, L.: A survey of image registration techniques. ACM Computing Surveys 24, 326–376 (1992)CrossRefGoogle Scholar
  2. 2.
    Maintz, J., Viergever, M.: A survey of medical image registration. Medical Image Analysis 2, 1–36 (1998)CrossRefGoogle Scholar
  3. 3.
    Zitová, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21, 977–1000 (2003)CrossRefGoogle Scholar
  4. 4.
    Basser, P.J., Mattiello, J., Le Bihan, D.: MR diffusion tensor spectroscopy and imaging. Biophys. J. 66, 259–267 (1994)CrossRefGoogle Scholar
  5. 5.
    Mori, S., van Zijl, P.: Fiber tracking: principles and strategies - a technical review. NMR biomed 15, 468–480 (2002)CrossRefGoogle Scholar
  6. 6.
    Basser, P., Jones, D.: Diffusion-tensor MRI: theory, experimental design and data analysis - a technical review. NMR biomed 15, 456–467 (2002)CrossRefGoogle Scholar
  7. 7.
    Alexander, D., Gee, J.: Elastic matching of diffusion tensor images. Computer Vision and Image Understanding 77, 233–250 (2000)CrossRefGoogle Scholar
  8. 8.
    Ruiz-Alzola, J., Westin, C.F., Warfield, S., Alberola, C., Maier, S., Kikinis, R.: Nonrigid registration of 3D tensor medical data. Medical Image Analysis 6, 143–161 (2002)CrossRefGoogle Scholar
  9. 9.
    Ruiz-Alzola, J., Westin, C.F., Warfield, S., Nabavi, A., Kikinis, R.: Nonrigid registration of 3D scalar, vector and tensor medical data. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 541–550. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  10. 10.
    Park, H.J., Kubicki, M., Shenton, M., Guimond, A., McCarley, R., Maier, S., Kikinis, R., Jolesz, F., Westin, C.F.: Spatial normalization of diffusion tensor MRI using multiple channels. NeuroImage 20, 1995–2009 (2003)CrossRefGoogle Scholar
  11. 11.
    Guimond, A., Guttmann, C.: Deformable registration of DT-MRI data based on transformation invariant tensor characteristics. In: IEEE International Symposium on Biomedical Imaging (ISBI 2002), Washington, DC (2002)Google Scholar
  12. 12.
    Xue, D., Mori, S., Shen, D., van Zijl, P., Davatzikos, C.: Spatial normalization of diffusion tensor fields. Magn Reson Med 50, 175–182 (2003)CrossRefGoogle Scholar
  13. 13.
    Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Trans. Medical Imaging 16, 187–198 (1997)CrossRefGoogle Scholar
  14. 14.
    Alexander, D., Pierpaoli, C., Basser, P., Gee, J.: Spatial transformations of diffusion tensor magnetic resonance images. IEEE Trans. Med. Imag. 20, 1131–1139 (2001)CrossRefGoogle Scholar
  15. 15.
    Viola, P., Wells, W.M.: Multi-modal volume registration by maximization of mutual information. Med. Image. Anal. 1, 35–51 (1996)CrossRefGoogle Scholar
  16. 16.
    Collingnon, A., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P., Marchal, G.: Automated multimodality medical image registration using information theory. In: Insana, M.F., Leahy, R.M. (eds.) IPMI 2001. LNCS, vol. 2082, pp. 92–105. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  17. 17.
    Leemans, A., Sijbers, J., Verhoye, M., Van der Linden, A., Van Dyck, D.: Mathematical framework for simulating diffusion tensor MR neural fiber bundles. Magn. Reson. Med. 53, 944–953 (2005)CrossRefGoogle Scholar
  18. 18.
    Basser, P.J., Pierpaoli, C.: Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn. Reson. B. 111, 209–219 (1996)CrossRefGoogle Scholar
  19. 19.
    Leemans, A., Sijbers, J., Parizel, P.: A graphical toolbox for exploratory diffusion tensor imaging and fiber tractography. In: Section for Magnetic Resonance Technologists (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Alexander Leemans
    • 1
  • Jan Sijbers
    • 1
  • Steve De Backer
    • 1
  • Everhard Vandervliet
    • 2
  • Paul M. Parizel
    • 2
  1. 1.Vision Lab (Department of Physics)University of AntwerpAntwerpBelgium
  2. 2.Department of Radiology and Medical ImagingUniversity Hospital AntwerpEdegemBelgium

Personalised recommendations