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Inference of an Extended Short Fiber Bundle Atlas Using Sulcus-Based Constraints for a Diffeomorphic Inter-subject Alignment

  • Nicole Labra AvilaEmail author
  • Jessica Lebenberg
  • Denis Rivière
  • Guillaume Auzias
  • Clara Fischer
  • Fabrice Poupon
  • Pamela Guevara
  • Cyril Poupon
  • Jean-François Mangin
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

We present a new framework for the creation of an extended atlas of short fiber bundles between 20 and 80 mm length. This method uses a Diffeomorphic inter-subject alignment procedure including information of cortical foldings and forces the accurate match of the sulci that have to be circumvented by the U-bundles. Then, a clustering is performed to extract the most reproducible bundles across subjects. First results show an increased number of U-bundles consistently mapped in the general population compared with previous atlases created from the same database. Future analysis over this new extended Brain atlas may improve our understanding of the relationship between the folding pattern and the U-bundle variability. The ultimate aim will be the possibility to detect abnormal configurations induced by developmental issues.

Keywords

White matter U-fiber U-bundles Short fiber bundles dMRI Diffusion MRI Brain atlas Bundle atlas Diffeomorphic alignment Sulcus base alignment Cortical folding pattern Sulci 

References

  1. 1.
    Ashburner, J., et al.: A fast diffeomorphic image registration algorithm. Neuroimage 38(95), 113 (2007)Google Scholar
  2. 2.
    Assaf, Y., et al.: The CONNECT project: combining macro- and micro-structure. Neuroimage 80, 273–282 (2013)CrossRefGoogle Scholar
  3. 3.
    Auzias, G., et al.: Diffeomorphic brain registration under exhaustive sulcal constraints. IEEE Trans. Med. Imaging 30(6), 1214–1227 (2011)CrossRefGoogle Scholar
  4. 4.
    Brainvisa Homepage. http://brainvisa.info/web/index.html. Last accessed 6 July 2018
  5. 5.
    Cointepas, Y., et al.: BrainVISA: software platform for visualization and analysis of multi-modality brain data. In: OHBM. Presented at the OHBM, Brighton (2001)CrossRefGoogle Scholar
  6. 6.
    Dubois, J., et al.: Correction strategy for diffusion-weighted images corrupted with motion: application to the DTI evaluation of infants white matter. Magn. Reson. Imaging 32(8), 981–992 (2014)CrossRefGoogle Scholar
  7. 7.
    Duclap, D., et al.: Connectomist-2.0: a novel diffusion analysis toolbox for BrainVISA. In: 29th ESMRMB, Lisbonne, Portugal (2012)Google Scholar
  8. 8.
    Descoteaux, M., et al.: Regularized, fast and robust analytical Q-ball imaging. Magn. Reson. Med. 58, 497–510 (2007)CrossRefGoogle Scholar
  9. 9.
    Ester, M., et al.: A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining KDD 1996, Portland, Oregon, pp. 226–231 (1996)Google Scholar
  10. 10.
    Fischer, C., et al.: Morphologist 2012: the new morphological pipeline of BrainVISA. In: OHBM. Presented at the OHBM, Beijing, China (2012)Google Scholar
  11. 11.
    Guevara, P., et al.: Robust clustering of massive tractography datasets. Neuroimage 54(3), 1975–1993 (2011)CrossRefGoogle Scholar
  12. 12.
    Guevara, P., et al.: Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas. Neuroimage 61(4), 1083–1099 (2012)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Guevara, M., et al.: Reproducibility of superficial white matter tracts using diffusionweighted imaging tractography. Neuroimage 147, 703–725 (2017)CrossRefGoogle Scholar
  14. 14.
    Lebenberg, J., et al.: A framework based on sulcal constraints to align preterm, infant and adult human brain images acquired in vivo and post mortem (2018)Google Scholar
  15. 15.
    Mangin, J.-F., et al.: A framework to study the cortical folding patterns. Neuroimage 23(1), 129–138 (2004)CrossRefGoogle Scholar
  16. 16.
    Mazziotta, J., et al.: A probabilistic atlas and reference system for the human brain: international consortium for brain mapping (ICBM). Philos. Trans. R. Soc. Lond. Ser. B 356, 1293–1322 (2001)CrossRefGoogle Scholar
  17. 17.
    O’Donnell, L., et al.: Automatic tractography segmentation using a high-dimensional white matter atlas. IEEE Trans. Med. Imaging 26(11), 1562–1575 (2007)CrossRefGoogle Scholar
  18. 18.
    Roman, C., et al.: Clustering of whole brain white matter short association bundles using HARDI data. Front. Neuroinformatics 11, 73 (2017)Google Scholar
  19. 19.
    Zhang, F., et al.: Whole brain white matter connectivity analysis using machine learning: an application to autism. Neuroimage 172, 826–837 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nicole Labra Avila
    • 1
    • 2
    Email author
  • Jessica Lebenberg
    • 3
  • Denis Rivière
    • 1
  • Guillaume Auzias
    • 4
  • Clara Fischer
    • 1
  • Fabrice Poupon
    • 1
  • Pamela Guevara
    • 5
  • Cyril Poupon
    • 6
  • Jean-François Mangin
    • 1
  1. 1.UNATI, CEA/DRF/NeurospinGif-sur-YvetteFrance
  2. 2.Université Paris SudParis SaclayFrance
  3. 3.Université Paris Diderot, Sorbonne Paris CitéParisFrance
  4. 4.Institut de Neurosciences de la TimoneAix-Marseille UniversityMarseilleFrance
  5. 5.Universidad de ConcepciónConcepciónChile
  6. 6.UNIRS, CEA/DRF/NeurospinGif-sur-YvetteFrance

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