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Bundle-Specific Tractography

  • Francois Rheault
  • Etienne St-Onge
  • Jasmeen Sidhu
  • Quentin Chenot
  • Laurent Petit
  • Maxime Descoteaux
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

Tractography allows the investigation of white matter fascicles. However, it requires a large amount of streamlines to be generated to cover the full spatial extent of desired bundles. In this work, a bundle-specific tractography algorithm was developed to increase reproducibility and sensitivity of white matter fascicle virtual dissection, thus avoiding the computation of a full brain tractography. Using fascicle priors from manually segmented bundles templates or atlases, we propose a novel local orientation enhancement methodology that overcomes reconstruction difficulties in crossing regions. To reduce unnecessary computation, tractography seeding and tracking were restricted to specific locales within the brain. These additions yield better spatial coverage, increasing the quality of the fanning in crossing regions, helping to accurately represent fascicle shape. In this work, tractography methods were analyzed and compared using a single bundle of interest, the corticospinal tract.

References

  1. 1.
    Assaf, Y., Pasternak, O.: Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review. J. Mol. Neurosci. 34(1), 51–61 (2008). https://doi.org/10.1007/s12031-007-0029-0 CrossRefGoogle Scholar
  2. 2.
    Avants, B., Epstein, C., Grossman, M., Gee, J.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)CrossRefGoogle Scholar
  3. 3.
    Behrens, T.E., Berg, H.J., Jbabdi, S., Rushworth, M., Woolrich, M.: Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? NeuroImage 34(1), 144–155 (2007)CrossRefGoogle Scholar
  4. 4.
    Catani, M., De Schotten, M.T.: A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex 44(8), 1105–1132 (2008)CrossRefGoogle Scholar
  5. 5.
    Catani, M., Howard, R.J., Pajevic, S., Jones, D.K.: Virtual in vivo interactive dissection of white matter fasciculi in the human brain. NeuroImage 17, 77–94 (2002)CrossRefGoogle Scholar
  6. 6.
    Chamberland, M., Whittingstall, K., Fortin, D., Mathieu, D., Descoteaux, M.: Real-time multi-peak tractography for instantaneous connectivity display. Front. Neuroinform. 8, 59 (2014)CrossRefGoogle Scholar
  7. 7.
    Chamberland, M., Scherrer, B., Prabhu, S.P., Madsen, J., Fortin, D., Whittingstall, K., Descoteaux, M., Warfield, S.K.: Active delineation of Meyer’s loop using oriented priors through magnetic tractography (magnet). Hum. Brain Mapp. 38(1), 509–527 (2017)CrossRefGoogle Scholar
  8. 8.
    Cousineau, M., Jodoin, P.M., Garyfallidis, E., Côté, M.A., Morency, F.C., Rozanski, V., Grand’Maison, M., Bedell, B.J., Descoteaux, M.: A test–retest study on parkinson’s ppmi dataset yields statistically significant white matter fascicles. NeuroImage: Clinical 16(Suppl. C), 222–233 (2017). http://www.sciencedirect.com/science/article/pii/S2213158217301869
  9. 9.
    Dayan, M., Monohan, E., Pandya, S., Kuceyeski, A., Nguyen, T.D., Raj, A., Gauthier, S.A.: Profilometry: a new statistical framework for the characterization of white matter pathways, with application to multiple sclerosis. Hum. Brain Mapp. 37(3), 989–1004 (2015).  https://doi.org/10.1002/hbm.23082 CrossRefGoogle Scholar
  10. 10.
    Descoteaux, M., Deriche, R., Knosche, T.R., Anwander, A.: Deterministic and probabilistic tractography based on complex fibre orientation distributions. IEEE Trans. Med. Imaging 28(2), 269–286 (2009)CrossRefGoogle Scholar
  11. 11.
    Desikan, R.S., Ségonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., Buckner, R.L., Dale, A.M., Maguire, R.P., Hyman, B.T., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3), 968–980 (2006)CrossRefGoogle Scholar
  12. 12.
    Dhollander, T., Emsell, L., Hecke, W.V., Maes, F., Sunaert, S., Suetens, P.: Track orientation density imaging (TODI) and track orientation distribution (TOD) based tractography. NeuroImage 94, 312–336 (2014)CrossRefGoogle Scholar
  13. 13.
    Dubois, J., Dehaene-Lambertz, G., Perrin, M., Mangin, J., Cointepas, Y., Duchesnay, E., Bihan, D.L., Hertz-Pannier, L.: Asynchrony of the early maturation of white matter bundles in healthy infants: quantitative landmarks revealed noninvasively by diffusion tensor imaging. Hum. Brain Mapp. 29(1), 14–27 (2008).  https://doi.org/10.1002/hbm.20363 CrossRefGoogle Scholar
  14. 14.
    Girard, G., Descoteaux, M.: Anatomical tissue probability priors for tractography. In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI’12)-Computational Diffusion MRI Workshop, pp. 174–185 (2012)Google Scholar
  15. 15.
    Girard, G., Descoteaux, M.: Towards quantitative connectivity analysis: reducing tractography biases. NeuroImage 98(1), 266–278 (2014)CrossRefGoogle Scholar
  16. 16.
    Jbabdi, S., Johansen-Berg, H.: Tractography: where do we go from here? Brain connect. 1(3), 169–183 (2011)CrossRefGoogle Scholar
  17. 17.
    Mazoyer, B., Mellet, E., Perchey, G., Zago, L., Crivello, F., Jobard, G., Delcroix, N., Vigneau, M., Leroux, G., Petit, L., Joliot, M., Tzourio-Mazoyer, N.: BIL&GIN: a neuroimaging, cognitive, behavioral, and genetic database for the study of human brain lateralization. NeuroImage 124 Part B, 1225–1231 (2016)Google Scholar
  18. 18.
    Smith, R.E., Tournier, J.D., Calamante, F., Connelly, A.: Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. NeuroImage 62(3), 1924–1938 (2012)CrossRefGoogle Scholar
  19. 19.
    Takemura, H., Caiafa, C.F., Wandell, B.A., Pestilli, F.: Ensemble tractography. PLOS Comput. Biol. 12(2), 1–22 (2016)CrossRefGoogle Scholar
  20. 20.
    Tournier, J.D., Cho, K.H., Calamante, F., Yeh, C.H., Connelly, A., Lin, C.P.: Resolving crossing fibres using constrained spherical deconvolution: Validation using DWI phantom data. In: Proceedings of the International Society of Magnetic Resonance in Medicine, Berlin, p. 902 (2007)Google Scholar
  21. 21.
    Tournier, J.D., Calamante, F., Connelly, A.: MRtrix: diffusion tractography in crossing fiber regions. Int. J. Imaging Syst. Technol. 22(1), 53–66 (2012)CrossRefGoogle Scholar
  22. 22.
    Wang, R., Benner, T., Sorensen, A., Wedeen., V.: Diffusion toolkit: a software package for diffusion imaging data processing and tractography. In: International Symposium on Magnetic Resonance in Medicine (ISMRM’07), p. 3720 (2007)Google Scholar
  23. 23.
    Wassermann, D., Makris, N., Rathi, Y., Shenton, M., Kikinis, R., Kubicki, M., Westin, C.F.: The white matter query language: a novel approach for describing human white matter anatomy. Brain Struct. Funct. 221(9), 4705–4721 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Francois Rheault
    • 1
  • Etienne St-Onge
    • 1
  • Jasmeen Sidhu
    • 1
  • Quentin Chenot
    • 2
  • Laurent Petit
    • 2
  • Maxime Descoteaux
    • 1
  1. 1.Sherbrooke Connectivity Imaging Laboratory (SCIL)Université de SherbrookeSherbrookeCanada
  2. 2.Groupe d’Imagerie Neurofonctionnelle, IMN, CNRS, CEAUniversité de BordeauxBordeauxFrance

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