Detection of DTI White Matter Abnormalities in Multiple Sclerosis Patients

  • Olivier Commowick
  • Pierre Fillard
  • Olivier Clatz
  • Simon K. Warfield
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5241)


The emergence of new modalities such as Diffusion Tensor Imaging (DTI) is of great interest for the characterization and the temporal study of Multiple Sclerosis (MS). DTI indeed gives information on water diffusion within tissues and could therefore reveal alterations in white matter fibers before being visible in conventional MRI. However, recent studies generally rely on scalar measures derived from the tensors such as FA or MD instead of using the full tensor itself. Therefore, a certain amount of information is left unused.

In this article, we present a framework to study the benefits of using the whole diffusion tensor information to detect statistically significant differences between each individual MS patient and a database of control subjects. This framework, based on the comparison of the MS patient DTI and a mean DTI atlas built from the control subjects, allows us to look for differences both in normally appearing white matter but also in and around the lesions of each patient. We present a study on a database of 11 MS patients, showing the ability of the DTI to detect not only significant differences on the lesions but also in regions around them, enabling an early detection of an extension of the MS disease.


Multiple Sclerosis Fractional Anisotropy Multiple Sclerosis Patient Multiple Sclerosis Lesion Nonrigid 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.


  1. 1.
    Ge, Y.: Multiple sclerosis: The role of MR imaging. American Journal of Neuroradiology 27, 1165–1176 (2006)Google Scholar
  2. 2.
    Basser, P., Mattiello, J., Bihan, D.L.: MR diffusion tensor spectroscopy and imaging. Biophysical Journal 66, 259–267 (1994)CrossRefGoogle Scholar
  3. 3.
    Filippi, M., Cercignani, M., Inglese, M., Comi, M.H.G.: Diffusion tensor magnetic resonance imaging in multiple sclerosis. Neurology 56, 304–311 (2001)Google Scholar
  4. 4.
    Whitcher, B., Wisco, J.J., Hadjikhani, N., Tuch, D.S.: Statistical group comparison of diffusion tensors via multivariate hypothesis testing. Magnetic Resonance in Medicine (57), 1065–1074 (2007)Google Scholar
  5. 5.
    Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Log-Euclidean metrics for fast and simple calculus on diffusion tensors. Magnetic Resonance in Medicine 56(2), 411–421 (2006)CrossRefGoogle Scholar
  6. 6.
    Lepore, N., Brun, C.A., Chiang, M.C., Chou, Y.Y., Lopez, O.L., Aizenstein, H.J., Toga, A.W., Becker, J.T., Thompson, P.M.: Multivariate statistics of the jacobian matrices in tensor based morphometry and their application to HIV/AIDS. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 191–198. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Guimond, A., Meunier, J., Thirion, J.P.: Average brain models: A convergence study. Computer Vision and Image Understanding 77(2), 192–210 (2000)CrossRefGoogle Scholar
  8. 8.
    Commowick, O., Malandain, G.: Evaluation of atlas construction strategies in the context of radiotherapy planning. In: Proc. of the SA2PM Workshop, Copenhagen. Held in conjunction with MICCAI 2006 (October 2006)Google Scholar
  9. 9.
    Arsigny, V., Commowick, O., Pennec, X., Ayache, N.: A Log-Euclidean framework for statistics on diffeomorphisms. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 924–931. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Ruiz-Alzola, J., Westin, C.F., Warfield, S.K., Alberola, C., Maier, S., Kikinis, R.: Nonrigid registration of 3D tensor medical data. MedIA 6(2), 143–161 (2002)Google Scholar
  11. 11.
    Alexander, D., Pierpaoli, C., Basser, P., Gee, J.: Spatial transformations of diffusion tensor magnetic resonance images. IEE TMI 20(11), 1131–1139 (2001)Google Scholar
  12. 12.
    Rueckert, D., Sonoda, L.L., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Transactions on Medical Imaging 18(8), 712–721 (1999)CrossRefGoogle Scholar
  13. 13.
    Stefanescu, R., Commowick, O., Malandain, G., Bondiau, P.Y., Ayache, N., Pennec, X.: Non-rigid atlas to subject registration with pathologies for conformal brain radiotherapy. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 704–711. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Goodlett, C., Davis, B., Jean, R., Gilmore, J.H., Gerig, G.: Improved correspondence for DTI population studies via unbiased atlas building. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 260–267. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Warfield, S.K., Zou, K.H., Wells, W.M.: Validation of image segmentation by estimating rater bias and variance. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 839–847. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Olivier Commowick
    • 1
  • Pierre Fillard
    • 2
  • Olivier Clatz
    • 2
  • Simon K. Warfield
    • 1
  1. 1.Computational Radiology Laboratory, Department of Radiology, Children’s Hospital BostonUSA
  2. 2.INRIA Sophia Antipolis - Asclepios TeamSophia Antipolis CedexFrance

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