A Framework for Creating Population Specific Multimodal Brain Atlas Using Clinical T1 and Diffusion Tensor Images

  • Vikash Gupta
  • Grégoire Malandain
  • Nicholas Ayache
  • Xavier Pennec
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)


Spatial normalization is one of the most important steps in population based statistical analysis of brain images. This involves normalizing all the brain images to a pre-defined template or a population specific template. With multiple emerging imaging modalities, it is quintessential to develop a method for building a joint template that is a statistical representation of the given population across different modalities. It is possible to create different population specific templates in different modalities using existing methods. However, they do not give an opportunity for voxelwise comparison of different modalities. A multimodal brain template with probabilistic region of interest (ROI) definitions will give opportunity for multivariate statistical frameworks for better understanding of brain diseases. In this paper, we propose a methodology for developing such a multimodal brain atlas using the anatomical T1 images and the diffusion tensor images (DTI), along with an automated workflow to probabilistically define the different white matter regions on the population specific multimodal template. The method will be useful to carry out ROI based statistics across different modalities even in the absence of expert segmentation. We show the effectiveness of such a template using voxelwise multivariate statistical analysis on population based group studies on HIV/AIDS patients.


  1. 1.
    Thompson, P.M., Toga, A.W.: Detection, visualization and animation of abnormal anatomic structure with a deformable probabilistic brain atlas based on random vector field transformations. Med. Image Anal. 1(4), 271–294 (1997)CrossRefGoogle Scholar
  2. 2.
    Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23, S151–S160 (2004)CrossRefGoogle Scholar
  3. 3.
    Toga, A.W., Thompson, P.M., et al.: Towards multimodal atlases of the human brain. Nat. Rev. Neurosci. 7(12), 952–966 (2006)CrossRefGoogle Scholar
  4. 4.
    Mori, S., et al.: Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage 40(2), 570–582 (2008)CrossRefGoogle Scholar
  5. 5.
    Guimond, A., Meunier, J., Thirion, J.P.: Average brain models: a convergence study. Comput. Vis. Image Underst. 77(2), 192–210 (2000)CrossRefGoogle Scholar
  6. 6.
    Commowick, O., Malandain, G.: Efficient selection of the most similar image in a database for critical structures segmentation. In: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007, pp. 203–210. Springer, Berlin (2007)Google Scholar
  7. 7.
    Tustison, N.J., Avants, B.B., et al.: N4ITK: improved N3 bias correction. Trans. Med. Imaging 29(6), 1310–1320 (2010)CrossRefGoogle Scholar
  8. 8.
    Lorenzi, M., et al.: LCC-Demons: a robust and accurate symmetric diffeomorphic registration algorithm. NeuroImage 81, 470–483 (2013)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Fillard, P., Pennec, X., et al.: Clinical DT-MRI estimation, smoothing, and fiber tracking with log-Euclidean metrics. IEEE Trans. Med. Imaging 26(11), 1472–1482 (2007)CrossRefGoogle Scholar
  10. 10.
    Arsigny, V., Commowick, O., Pennec, X., Ayache, N.: A log-Euclidean framework for statistics on diffeomorphisms. In: MICCAI 2006, pp. 924–931. Springer, Berlin (2006)Google Scholar
  11. 11.
    Baringhaus, L., Franz, C.: On a new multivariate two-sample test. J. Multivar. Anal. 88(1), 190–206 (2004)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Vikash Gupta
    • 1
  • Grégoire Malandain
    • 1
  • Nicholas Ayache
    • 1
  • Xavier Pennec
    • 1
  1. 1.INRIA Sophia Antipolis - ASCLEPIOS ProjectSophia Antipolis CedexFrance

Personalised recommendations