Construction of a 4D Brain Atlas and Growth Model Using Diffeomorphic Registration

  • Andreas Schuh
  • Maria Murgasova
  • Antonios Makropoulos
  • Christian Ledig
  • Serena J. Counsell
  • Jo V. Hajnal
  • Paul Aljabar
  • Daniel Rueckert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8682)


Atlases of the human brain have numerous applications in neurological imaging such as the analysis of brain growth. Publicly available atlases of the developing brain have previously been constructed using the arithmetic mean of free-form deformations which were obtained by asymmetric pairwise registration of brain images. Most of these atlases represent cross-sections of the growth process only. In this work, we use the Log-Euclidean mean of inverse consistent transformations which belong to the one-parameter subgroup of diffeomorphisms, as it more naturally represents average morphology. During the registration, similarity is evaluated symmetrically for the images to be aligned. As both images are equally affected by the deformation and interpolation, asymmetric bias is reduced. We further propose to represent longitudinal change by exploiting the numerous transformations computed during the atlas construction in order to derive a deformation model of mean growth. Based on brain images of 118 neonates, we constructed an atlas which describes the dynamics of early development through mean images at weekly intervals and a continuous spatio-temporal deformation. The evolution of brain volumes calculated on preterm neonates is in agreement with recently published findings based on measures of cortical folding of fetuses at the equivalent age range.


Brain Growth Neonatal Brain Template Image Early Brain Development Cortical Grey Matter Volume 
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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  1. 1.
    Aljabar, P., Bhatia, K.K., Hajnal, J.V., Boardman, J.P., Srinivasan, L., Rutherford, M.A., Dyet, L.E., Edwards, A.D., Rueckert, D.: Analysis of growth in the developing brain using non-rigid registration. In: ISBI, pp. 201–204 (2006)Google Scholar
  2. 2.
    Arsigny, V., Commowick, O., Pennec, X., Ayache, N.: A log-euclidean framework for statistics on diffeomorphisms. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 924–931. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: 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
  4. 4.
    Bossa, M., Hernandez, M., Olmos, S.: Contributions to 3D diffeomorphic atlas estimation: application to brain images. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 667–674. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Cheng, S.H.U.N., Higham, N.J., Kenney, C.S., Laub, A.J.: Approximating the logarithm of a matrix to specified accuracy. SIAM J. Matrix Anal. Appl. 22(4), 1112–1125 (1999)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Davis, B.C., Fletcher, P.T., Bullitt, E., Joshi, S.: Population shape regression from random design data. In: ICCV, pp. 1–7 (2007)Google Scholar
  7. 7.
    Habas, P.A., Kim, K., Corbett-Detig, J.M., Rousseau, F., Glenn, O.A., Barkovich, A.J., Studholme, C.: A spatiotemporal atlas of MR intensity, tissue probability and shape of the fetal brain with application to segmentation. NeuroImage 53(2), 460–470 (2010)CrossRefGoogle Scholar
  8. 8.
    Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23(suppl. 1), S151–S160 (2004)CrossRefGoogle Scholar
  9. 9.
    Kuklisova-Murgasova, M., Aljabar, P., Srinivasan, L., Counsell, S.J., Doria, V., Serag, A., Gousias, I.S., Boardman, J.P., Rutherford, M.A., Edwards, A.D., Hajnal, J.V., Rueckert, D.: A dynamic 4D probabilistic atlas of the developing brain. NeuroImage 54(4), 2750–2763 (2011)CrossRefGoogle Scholar
  10. 10.
    Ledig, C., Wright, R., Serag, A., Aljabar, P., Rueckert, D.: Neonatal brain segmentation using second order neighborhood information. In: Workshop on Perinatal and Paediatric Imaging: PaPI, MICCAI, pp. 33–40 (2012)Google Scholar
  11. 11.
    Lorenzi, M., Ayache, N., Frisoni, G.B., Pennec, X.: LCC-Demons: a robust and accurate symmetric diffeomorphic registration algorithm. NeuroImage 81, 470–483 (2013)CrossRefGoogle Scholar
  12. 12.
    Modat, M., Cardoso, M.J., Daga, P., Cash, D., Fox, N.C., Ourselin, S.: Inverse-consistent symmetric free form deformation. In: Dawant, B.M., Christensen, G.E., Fitzpatrick, J.M., Rueckert, D. (eds.) WBIR 2012. LNCS, vol. 7359, pp. 79–88. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Modat, M., Daga, P., Cardoso, M.J., Ourselin, S., Ridgway, G.R., Ashburner, J.: Parametric non-rigid registration using a stationary velocity field. In: MMBIA, pp. 145–150 (2012)Google Scholar
  14. 14.
    Modat, M., Ridgway, G.R., Taylor, Z.A., Hawkes, D.J., Fox, N.C., Ourselin, S.: A parallel-friendly normalised mutual information gradient for free-form registration. In: SPIE (2009)Google Scholar
  15. 15.
    Niethammer, M., Huang, Y., Vialard, F.-X.: Geodesic regression for image time-series. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 655–662. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Mutual-information-based registration of medical images: a survey. IEEE TMI 22(8), 986–1004 (2003)Google Scholar
  17. 17.
    Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)CrossRefGoogle Scholar
  18. 18.
    Serag, A., Aljabar, P., Ball, G., Counsell, S.J., Boardman, J.P., Rutherford, M.A., Edwards, A.D., Hajnal, J.V., Rueckert, D.: Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression. NeuroImage 59(3), 2255–2265 (2012)CrossRefGoogle Scholar
  19. 19.
    Singh, N., Hinkle, J., Joshi, S., Fletcher, P.T.: A vector momenta formulation of diffeomorphisms for improved geodesic regression and atlas construction. In: ISBI, pp. 1219–1222 (2013)Google Scholar
  20. 20.
    Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Symmetric log-domain diffeomorphic registration: a demons-based approach. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 754–761. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  21. 21.
    Wright, R., Kyriakopoulou, V., Ledig, C., Rutherford, M.A., Hajnal, J.V., Rueckert, D., Aljabar, P.: Automatic quantification of normal cortical folding patterns from fetal brain MRI. NeuroImage 91, 21–32 (2014)CrossRefGoogle Scholar
  22. 22.
    Yushkevich, P.A., Avants, B.B., Das, S.R., Pluta, J., Altinay, M., Craige, C.: Bias in estimation of hippocampal atrophy using deformation-based morphometry arises from asymmetric global normalization: an illustration in ADNI 3T MRI data. NeuroImage 50(2), 434–445 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Andreas Schuh
    • 1
  • Maria Murgasova
    • 2
  • Antonios Makropoulos
    • 1
  • Christian Ledig
    • 1
  • Serena J. Counsell
    • 2
  • Jo V. Hajnal
    • 2
  • Paul Aljabar
    • 2
  • Daniel Rueckert
    • 1
  1. 1.Imperial College LondonLondonUK
  2. 2.King’s College LondonLondonUK

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