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Registration and Analysis of White Matter Group Differences with a Multi-fiber Model

  • Maxime Taquet
  • Benoît Scherrer
  • Olivier Commowick
  • Jurriaan Peters
  • Mustafa Sahin
  • Benoît Macq
  • Simon K. Warfield
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7512)

Abstract

Diffusion magnetic resonance imaging has been used extensively to probe the white matter in vivo. Typically, the raw diffusion images are used to reconstruct a diffusion tensor image (DTI). The incapacity of DTI to represent crossing fibers leaded to the development of more sophisticated diffusion models. Among them, multi-fiber models represent each fiber bundle independently, allowing the direct extraction of diffusion features for population analysis. However, no method exists to properly register multi-fiber models, seriously limiting their use in group comparisons. This paper presents a registration and atlas construction method for multi-fiber models. The validity of the registration is demonstrated on a dataset of 45 subjects, including both healthy and unhealthy subjects. Morphometry analysis and tract-based statistics are then carried out, proving that multi-fiber models registration is better at detecting white matter local differences than single tensor registration.

Keywords

Diffusion Imaging Multi-Fiber Models Registration White Matter 

References

  1. 1.
    Zhang, H., Avants, B., Yushkevich, P., Woo, J., Wang, S., McCluskey, L., Elman, L., Melhem, E., Gee, J.: High-dimensional spatial normalization of diffusion tensor images improves the detection of white matter differences: an example study using amyotrophic lateral sclerosis. IEEE TMI 26(11), 1585–1597 (2007)Google Scholar
  2. 2.
    Minati, L., Weglarz, W.: Physical foundations, models, and methods of diffusion magnetic resonance imaging of the brain: A review. Concepts in Magnetic Resonance Part A 30(5), 278–307 (2007)CrossRefGoogle Scholar
  3. 3.
    Barmpoutis, A., Vemuri, B.C., Forder, J.R.: Registration of High Angular Resolution Diffusion MRI Images Using 4th Order Tensors. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 908–915. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Yap, P., Chen, Y., An, H., Yang, Y., Gilmore, J., Lin, W., Shen, D.: Sphere: Spherical harmonic elastic registration of hardi data. NeuroImage 55(2), 545–556 (2011)CrossRefGoogle Scholar
  5. 5.
    Bergmann, O., Kindlmann, G., Peled, S., Westin, C.: Two-tensor fiber tractography. In: IEEE International Symposium on Biomedical Imaging, pp. 796–799 (2007)Google Scholar
  6. 6.
    Assaf, Y., Basser, P.: Composite hindered and restricted model of diffusion (charmed) mr imaging of the human brain. Neuroimage 27(1), 48–58 (2005)CrossRefGoogle Scholar
  7. 7.
    Taquet, M., Scherrer, B., Benjamin, C., Prabhu, S., Macq, B., Warfield, S.: Interpolating multi-fiber models by gaussian mixture simplification. In: IEEE International Symposium on Biomedical Imaging (2012)Google Scholar
  8. 8.
    Banerjee, A., Merugu, S., Dhillon, I., Ghosh, J.: Clustering with bregman divergences. The Journal of Machine Learning Research 6, 1705–1749 (2005)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Commowick, O., Arsigny, V., Isambert, A., Costa, J., Dhermain, F., Bidault, F., Bondiau, P., Ayache, N., Malandain, G.: An efficient locally affine framework for the smooth registration of anatomical structures. MedIA 12(4), 427–441 (2008)Google Scholar
  10. 10.
    Ruiz-Alzola, J., Westin, C., Warfield, S., Alberola, C., Maier, S., Kikinis, R.: Nonrigid registration of 3d tensor medical data. MedIA 6(2), 143–161 (2002)Google Scholar
  11. 11.
    Yeo, B., Vercauteren, T., Fillard, P., Peyrat, J., Pennec, X., Golland, P., Ayache, N., Clatz, O.: Dt-refind: Diffusion tensor registration with exact finite-strain differential. IEEE Trans. on Medical Imaging 28(12), 1914–1928 (2009)CrossRefGoogle Scholar
  12. 12.
    Taquet, M., Macq, B., Warfield, S.: A generalized correlation coefficient: Application to dti and multi-fiber dti. In: IEEE MMBIA, pp. 9–14 (2012)Google Scholar
  13. 13.
    Scherrer, B., Warfield, S.: Toward an accurate multi-fiber assessment strategy for clinical practice. In: IEEE International Symposium on Biomedical Imaging, pp. 2140–2143 (2011)Google Scholar
  14. 14.
    Guimond, A., Meunier, J., Thirion, J.: Average brain models: A convergence study. Computer Vision and Image Understanding 77(2), 192–210 (2000)CrossRefGoogle Scholar
  15. 15.
    Ashburner, J., Hutton, C., Frackowiak, R., Johnsrude, I., Price, C., Friston, K.: Identifying global anatomical differences: deformation-based morphometry. Human Brain Mapping 6(5-6), 348–357 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Maxime Taquet
    • 1
    • 2
  • Benoît Scherrer
    • 1
  • Olivier Commowick
    • 3
  • Jurriaan Peters
    • 1
    • 4
  • Mustafa Sahin
    • 4
  • Benoît Macq
    • 2
  • Simon K. Warfield
    • 1
  1. 1.Computational Radiology LaboratoryChildren’s Hospital BostonHarvardUSA
  2. 2.ICTEAM InstituteUniversité Catholique de LouvainLouvain-La-NeuveBelgium
  3. 3.VisAGeS U746 Unit/ProjectINRIA, INSERMRennesFrance
  4. 4.Department of NeurologyChildren’s Hospital BostonHarvardUSA

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