Abstract

The contrast provided by diffusion MRI has been exploited repeatedly for in vivo segmentations of thalamic nuclei. This paper systematically investigates the benefits of high-angular resolution (HARDI) data for this purpose. An empirical analysis of clustering stability reveals a clear advantage of acquiring HARDI data at b = 1000 s/mm2. However, based on stability arguments, as well as further visual and statistical evidence and theoretical insights about the impact of parameters, HARDI models such as the q-ball do not exhibit clear benefits over the standard diffusion tensor for thalamus segmentation at this b value.

Keywords

Principal Direction Thalamic Nucleus Rand Index Sobolev Norm Adjust Rand Index 
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.

References

  1. 1.
    Wiegell, M.R., Tuch, D., Larsson, H.B., Wedeen, V.J.: Automatic segmentation of thalamic nuclei from diffusion tensor magnetic resonance imaging. NeuroImage 19, 391–401 (2003)CrossRefGoogle Scholar
  2. 2.
    Ziyan, U., Tuch, D., Westin, C.-F.: Segmentation of thalamic nuclei from DTI using spectral clustering. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 807–814. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Duan, Y., Li, X., Xi, Y.: Thalamus segmentation from diffusion tensor magnetic resonance imaging. Int. J. Biomed. Imaging 2007 (2007)Google Scholar
  4. 4.
    Jonasson, L., Hagmann, P., Pollo, C., Bresson, X., Wilson, C.R., Meuli, R., Thiran, J.P.: A level set method for segmentation of the thalamus and its nuclei in DT-MRI. Signal Processing 87(2), 309–321 (2007)CrossRefMATHGoogle Scholar
  5. 5.
    Ziyan, U., Westin, C.-F.: Joint segmentation of thalamic nuclei from a population of diffusion tensor MR images. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 279–286. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Grassi, A., Cammoun, L., Pollo, C., Hagmann, P., Meuli, R., Thiran, J.P.: Thalamic nuclei clustering on high angular resolution diffusion images. In: Proc. Int. Soc. Magn. Reson. Med., p. 1777 (2008)Google Scholar
  7. 7.
    Brunenberg, E., Duits, R., ter Haar Romeny, B., Platel, B.: A sobolev norm based distance measure for HARDI clustering. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 175–182. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R.: Regularized, fast, and robust analytical Q-Ball imaging. Magn. Reson. Med. 58, 497–510 (2007)CrossRefGoogle Scholar
  9. 9.
    Tuch, D.S.: Q-Ball imaging. Magn. Reson. Med. 52, 1358–1372 (2004)CrossRefGoogle Scholar
  10. 10.
    Tournier, J.D., Calamante, F., Gadian, D.G., Connelly, A.: Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage 23, 1176–1185 (2004)CrossRefGoogle Scholar
  11. 11.
    Alexander, D.C., Barker, G.J., Arridge, S.R.: Detection and modeling of non-gaussian apparent diffusion coefficient profiles in human brain data. Magn. Reson. Med. 48, 331–340 (2002)CrossRefGoogle Scholar
  12. 12.
    Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2, 193–218 (1985)CrossRefMATHGoogle Scholar
  13. 13.
    Jones, D.K., Horsfield, M.A., Simmons, A.: Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn. Reson. Med. 42, 515–525 (1999)CrossRefGoogle Scholar
  14. 14.
    Behrens, T.E.J., Johansen-Berg, H., Jbabdi, S., Rushworth, M.F.S., Woolrich, M.W.: Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage 34, 144–155 (2007)CrossRefGoogle Scholar
  15. 15.
    Behrens, T.E.J., Johansen-Berg, H., Woolrich, M.W., Smith, S.M., Wheeler-Kingshott, C.A.M., Boulby, P.A., Barker, G.J., Sillery, E.L., Sheehan, K., Ciccarelli, O., Thompson, A.J., Brady, J.M., Matthews, P.M.: Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat. Neurosci. 6(7), 750–757 (2003)CrossRefGoogle Scholar
  16. 16.
    Johansen-Berg, H., Behrens, T.E.J., Sillery, E., Ciccarelli, O., Thompson, A.J., Smith, S.M., Matthews, P.M.: Functional-anatomical validation and individual variation of diffusion tractography-based segmentation of the human thalamus. Cereb. Cortex 15(1), 31–39 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Thomas Schultz
    • 1
  1. 1.Computation InstituteUniversity of ChicagoChicagoUSA

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