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Automatic Population HARDI White Matter Tract Clustering by Label Fusion of Multiple Tract Atlases

  • Yan Jin
  • Yonggang Shi
  • Liang Zhan
  • Junning Li
  • Greig I. de Zubicaray
  • Katie L. McMahon
  • Nicholas G. Martin
  • Margaret J. Wright
  • Paul M. Thompson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7509)

Abstract

Automatic labeling of white matter fibres in diffusion-weighted brain MRI is vital for comparing brain integrity and connectivity across populations, but is challenging. Whole brain tractography generates a vast set of fibres throughout the brain, but it is hard to cluster them into anatomically meaningful tracts, due to wide individual variations in the trajectory and shape of white matter pathways. We propose a novel automatic tract labeling algorithm that fuses information from tractography and multiple hand-labeled fibre tract atlases. As streamline tractography can generate a large number of false positive fibres, we developed a top-down approach to extract tracts consistent with known anatomy, based on a distance metric to multiple hand-labeled atlases. Clustering results from different atlases were fused, using a multi-stage fusion scheme. Our “label fusion” method reliably extracted the major tracts from 105-gradient HARDI scans of 100 young normal adults.

Keywords

Fractional Anisotropy White Matter Tract Diffusion Tensor Magnetic Resonance Imaging Fractional Anisotropy Image Label Fusion 
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.

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References

  1. 1.
    Tuch, D.S.: Q-Ball Imaging. Magn. Reson. Med. 52, 1358–1372 (2004)CrossRefGoogle Scholar
  2. 2.
    Mori, S., Crain, B.J., Chacko, V.P., van Zijl, P.C.: Three-dimensional Tracking of Axonal Projections in the Brain by Magnetic Resonance Imaging. Ann. Neurol. 45, 265–269 (1999)CrossRefGoogle Scholar
  3. 3.
    Wakana, S., Caprihan, A., Panzenboeck, M.M., Fallon, J.H., Perry, M., Gollub, R.L., Hua, K., Zhang, J., Jiang, H., Dubey, P., Blitz, A., van Zijl, P., Mori, S.: Reproducibility of Quantitative Tractography Methods Applied to Cerebral White Matter. NeuroImage 36, 630–644 (2007)CrossRefGoogle Scholar
  4. 4.
    O’Donnell, L.J., Westin, C.F.: Automatic Tractography Segmentation using a High-dimensional White Matter Atlas. IEEE Trans. Med. Imag. 26, 1562–1575 (2007)CrossRefGoogle Scholar
  5. 5.
    Maddah, M., Zöllei, L., Grimson, W.E., Westin, C.F., Wells, W.M.: A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis. In: 5th IEEE ISBI, pp. 105–108 (2008)Google Scholar
  6. 6.
    Wassermann, D., Bloy, L., Kanterakis, E., Verma, R., Deriche, R.: Unsupervised White Matter Fibre Clustering and Tract Probability Map Generation. NeuroImage 51, 228–241 (2010)CrossRefGoogle Scholar
  7. 7.
    Artaechevarria, X., Munoz-Barrutia, A., Ortiz-de-Solorzano, C.: Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data. IEEE Trans. Med. Imag. 28, 1266–1277 (2009)CrossRefGoogle Scholar
  8. 8.
    Sabuncu, M., Yeo, B.T., Van Leemput, K., Fischl, B., Golland, P.: A Generative Model for Image Segmentation Based on Label Fusion. IEEE Trans. Med. Imag. 29, 1714–1729 (2010)CrossRefGoogle Scholar
  9. 9.
    Oishi, K., et al.: Atlas-based Whole Brain White Matter Analysis using Large Deformation Diffeomorphic Metric Mapping. NeuroImage 46, 486–499 (2009)CrossRefGoogle Scholar
  10. 10.
    Descoteaux, M., Faria, A., Jiang, H., Li, X., Akhter, K., Zhang, J., Hsu, J.T., Miller, M.I., van Zijl, P.C., Albert, M., Lyketsos, C.G., Woods, R., Toga, A.W., Pike, G.B., Rosa-Neto, P., Evans, A., Mazziotta, J., Mori, S.: Regularized, Fast and Robust Analytical Q-ball Imaging. Magn. Reson. Med. 58, 497–510 (2007)CrossRefGoogle Scholar
  11. 11.
    Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A Repro-ducible Evaluation of ANTs Similarity Metric Performance in Brain Image Registration. NeuroImage 54, 2033–2044 (2011)CrossRefGoogle Scholar
  12. 12.
    Zhang, Y., Zhang, J., Oishi, K., Faria, A.V., Jiang, H., Li, X., Akhter, K., Rosa-Neto, P., Pike, G.B., Evans, A., Toga, A.W., Woods, R., Mazziotta, J.C., Miller, M.I., van Zijl, P.C., Mori, S.: Atlas-Guided Tract Reconstruction for Automated and Comprehensive Examination of the White Matter Anatomy. NeuroImage 52, 1289–1301 (2010)CrossRefGoogle Scholar
  13. 13.
    Guevara, P., Duclap, D., Poupon, C., Marrakchi-Kacem, L., Fillard, P., Le Bihan, D., Leboyer, M., Houenou, J., Mangin, J.F.: Automatic Fibre Bundle Segmentation in Massive Tractography Datasets using a Multi-subject Bundle Atlas. In: 14th MICCAI Workshop on CDMRI (2011)Google Scholar
  14. 14.
    Jin, Y., Shi, Y., Jahanshad, N., Aganj, I., Sapiro, G., Toga, A.W., Thompson, P.M.: 3D Elastic Registration Improves HARDI-derived Fibre Alignment and Automated Tract Clustering. In: 8th IEEE ISBI, pp. 822–826 (2011)Google Scholar
  15. 15.
    Rohlfing, T., Brandt, R., Menzel, R., Maurer Jr., C.R.: Evaluation of Atlas Selection Strate-gies for Atlas-based Imaging Segmentation with Application to Confocal Microscopy Images of Bee Brains. NeuroImage 21, 1428–1442 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yan Jin
    • 1
  • Yonggang Shi
    • 1
  • Liang Zhan
    • 1
  • Junning Li
    • 1
  • Greig I. de Zubicaray
    • 2
  • Katie L. McMahon
    • 2
  • Nicholas G. Martin
    • 3
  • Margaret J. Wright
    • 3
  • Paul M. Thompson
    • 1
  1. 1.Laboratory of Neuro Imaging, Department of Neurology, David Geffen School of MedicineUniversity of California, Los AngelesLos AngelesUSA
  2. 2.University of QueenslandLuciaAustralia
  3. 3.Queensland Institute of Medical ResearchHerstonAustralia

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