Advertisement

Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms

  • Pietro Gori
  • Olivier Colliot
  • Linda Marrakchi-Kacem
  • Yulia Worbe
  • Alexandre Routier
  • Cyril Poupon
  • Andreas Hartmann
  • Nicholas Ayache
  • Stanley Durrleman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9123)

Abstract

This work proposes an atlas construction method to jointly analyse the relative position and shape of fiber tracts and gray matter structures. It is based on a double diffeomorphism which is a composition of two diffeomorphisms. The first diffeomorphism acts only on the white matter keeping fixed the gray matter of the atlas. The resulting white matter, together with the gray matter, are then deformed by the second diffeomorphism. The two diffeomorphisms are related and jointly optimised. In this way, the first diffeomorphisms explain the variability in structural connectivity within the population, namely both changes in the connected areas of the gray matter and in the geometry of the pathway of the tracts. The second diffeomorphisms put into correspondence the homologous anatomical structures across subjects. Fiber bundles are approximated with weighted prototypes using the metric of weighted currents. The atlas, the covariance matrix of deformation parameters and the noise variance of each structure are automatically estimated using a Bayesian approach. This method is applied to patients with Tourette syndrome and controls showing a variability in the structural connectivity of the left cortico-putamen circuit.

Keywords

White Matter Gray Matter Fiber Bundle Cortical Surface Structural Connectivity 
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.

Notes

Acknowledgements

The research leading to these results has received funding from the program “Investissements d’avenir” ANR-10-IAIHU-06.

References

  1. 1.
    Allassonnière, S., Amit, Y., Trouvé, A.: Toward a coherent statistical framework for dense deformable template estimation. J. Roy. Statist. Soc. B 69(1), 3–29 (2007)CrossRefGoogle Scholar
  2. 2.
    Gori, P., Colliot, O., Worbe, Y., Marrakchi-Kacem, L., Lecomte, S., Poupon, C., Hartmann, A., Ayache, N., Durrleman, S.: Bayesian atlas estimation for the variability analysis of shape complexes. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol. 8149, pp. 267–274. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  3. 3.
    Worbe, Y., Marrakchi-Kacem, L., Lecomte, S., Valabregue, R., Poupon, F., Guevara, P., Tucholka, A., Mangin, J.F., Vidailhet, M., Lehericy, S., Hartmann, A., Poupon, C.: Altered structural connectivity of cortico-striato-pallido-thalamic networks in Gilles de la Tourette syndrome. Brain 138, 472–482 (2014). doi: 10.1093/brain/awu311 CrossRefGoogle Scholar
  4. 4.
    Gori, P., Colliot, O., Marrakchi-Kacem, L., Worbe, Y., De Vico Fallani, F., Chavez, M., Lecomte, S., Poupon, C., Hartmann, A., Ayache, N., Durrleman, S.: A prototype representation to approximate white matter bundles with weighted currents. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part III. LNCS, vol. 8675, pp. 289–296. Springer, Heidelberg (2014) Google Scholar
  5. 5.
    Durrleman, S., Prastawa, M., Charon, N., Korenberg, J.R., Joshi, S., Gerig, G., Trouvé, A.: Morphometry of anatomical shape complexes with dense deformations and sparse parameters. NeuroImage 101, 35–49 (2014). doi: 10.1016/j.neuroimage.2014.06.043 CrossRefGoogle Scholar
  6. 6.
    Chen, T., Rangarajan, A., Eisenschenk, S.J., Vemuri, B.C.: Construction of a neuroanatomical shape complex atlas from 3D MRI brain structures. NeuroImage 60(3), 1778–1787 (2012)CrossRefGoogle Scholar
  7. 7.
    Gorczowski, K., Styner, M., Jeong, J.Y., Marron, J.S., Piven, J., Hazlett, H.C., Pizer, S.M., Gerig, G.: Multi-object analysis of volume, pose, and shape using statistical discrimination. IEEE Trans. Pattern Anal. Mach. Intell. 32(4), 652–661 (2010)CrossRefGoogle Scholar
  8. 8.
    Qiu, A., Miller, M.I.: Multi-structure network shape analysis via normal surface momentum maps. NeuroImage 42(4), 1430–1438 (2008)CrossRefGoogle Scholar
  9. 9.
    Ma, J., Miller, M.I., Younes, L.: A bayesian generative model for surface template estimation. Int. J. Biomed Imaging. 16, 1–14 (2010). doi: 10.1155/2010/974957 CrossRefGoogle Scholar
  10. 10.
    Davies, R.H., Twining, C.J., Cootes, T.F., Taylor, C.J.: Building 3-D statistical shape models by direct optimization. IEEE. Trans. Med. Imag. 29(4), 961–981 (2010)CrossRefGoogle Scholar
  11. 11.
    Hufnagel, H., Pennec, X., Ehrhardt, J., Ayache, N., Handels, H.: Computation of a probabilistic statistical shape model in a Maximum-a-posteriori framework. Methods. Inf. Med. 48(4), 314–319 (2009)CrossRefGoogle Scholar
  12. 12.
    Niethammer, M., Reuter, M., Wolter, F.-E., Bouix, S., Peinecke, N., Koo, M.-S., Shenton, M.E.: Global medical shape analysis using the laplace-beltrami spectrum. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 850–857. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  13. 13.
    Joshi, S.H., Cabeen, R.P., Joshi, A.A., Sun, B., Dinov, I., Narr, K.L., Toga, A.W., Woods, R.P.: Diffeomorphic sulcal shape analysis on the cortex. IEEE Trans. Med. Imaging 31(6), 1195–1212 (2012)CrossRefGoogle Scholar
  14. 14.
    Durrleman, S., Fillard, P., Pennec, X., Trouvé, A., Ayache, N.: Registration, atlas estimation and variability analysis of white matter fiber bundles modeled as currents. Neuroimage 55(3), 1073–1090 (2011)CrossRefGoogle Scholar
  15. 15.
    O’Donnell, L.J., Westin, C.F., Golby, A.J.: Tract-based morphometry for white matter group analysis. Neuroimage 45(3), 832–844 (2009)CrossRefGoogle Scholar
  16. 16.
    Wassermann, D., Rathi, Y., Bouix, S., Kubicki, M., Kikinis, R., Shenton, M., Westin, C.-F.: White matter bundle registration and population analysis based on gaussian processes. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 320–332. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  17. 17.
    Zhang, M., Singh, N., Fletcher, P.T.: Bayesian estimation of regularization and atlas building in diffeomorphic image registration. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 37–48. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  18. 18.
    Marsland, S., McLachlan, R.: A hamiltonian particle method for diffeomorphic image registration. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 396–407. Springer, Heidelberg (2007) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Pietro Gori
    • 1
  • Olivier Colliot
    • 1
    • 2
  • Linda Marrakchi-Kacem
    • 1
    • 3
  • Yulia Worbe
    • 1
  • Alexandre Routier
    • 1
  • Cyril Poupon
    • 3
  • Andreas Hartmann
    • 1
  • Nicholas Ayache
    • 4
  • Stanley Durrleman
    • 1
  1. 1.Inria Paris-RocquencourtSorbonne UniversitésParisFrance
  2. 2.Departments of Neurology and NeuroradiologyAP-HP, Pitié-Salpêtrière HospitalParisFrance
  3. 3.Neurospin, CEAGif-sur-yvetteFrance
  4. 4.Asclepios Project-team, Inria Sophia AntipolisSophia AntipolisFrance

Personalised recommendations