Interpolation and Averaging of Multi-Compartment Model Images

  • Renaud Hédouin
  • Olivier Commowick
  • Aymeric Stamm
  • Christian Barillot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9350)


Multi-compartment diffusion models (MCM) are increasingly used to characterize the brain white matter microstructure from diffusion MRI. We address the problem of interpolation and averaging of MCM images as a simplification problem based on spectral clustering. As a core part of the framework, we propose novel solutions for the averaging of MCM compartments. Evaluation is performed both on synthetic and clinical data, demonstrating better performance for the “covariance analytic” averaging method. We then present an MCM template of normal controls constructed using the proposed interpolation.


Spectral Cluster White Matter Microstructure Spherical Harmonic Basis Human Brain Data Orientation Distribution Func 
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.


  1. 1.
    Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Log-Euclidean metrics for fast and simple calculus on diffusion tensors. MRM 56(2), 411–421 (2006)CrossRefGoogle Scholar
  2. 2.
    Barmpoutis, A., Vemuri, B.C., Forder, J.R.: Registration of high angular resolution diffusion MRI images using 4th order tensors. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 908–915. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Basser, P.J., Pierpaoli, C.: Microstructural and physiological features of tissues elucidated by quantitative diffusion-tensor MRI. Journal of Magnetic Resonance, Series B 111(3), 209–219 (1996)CrossRefGoogle Scholar
  4. 4.
    Ferizi, U., Schneider, T., et al.: A ranking of diffusion MRI compartment models with in vivo human brain data. MRM 72(6), 1785–1792 (2014)CrossRefGoogle Scholar
  5. 5.
    Geng, X., et al.: Diffusion MRI registration using orientation distribution functions. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds.) IPMI 2009. LNCS, vol. 5636, pp. 626–637. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Goh, A., Lenglet, C., Thompson, P.M., Vidal, R.: A nonparametric Riemannian framework for processing high angular resolution diffusion images and its applications to ODF-based morphometry. Neuroimage 56, 1181–1201 (2011)CrossRefGoogle Scholar
  7. 7.
    Guimond, A., Meunier, J., Thirion, J.P.: Average brain models: A convergence study. Computer Vision and Image Understanding 77(2), 192–210 (2000)CrossRefGoogle Scholar
  8. 8.
    McGraw, T., Vemuri, B.: Von mises-fisher mixture model of the diffusion ODF. In: IEEE ISBI, pp. 65–68 (2006)Google Scholar
  9. 9.
    Ng, A.Y., Jordan, M.I., Weiss, Y., et al.: On spectral clustering: Analysis and an algorithm. Advances in Neural Information Processing Systems 2, 849–856 (2002)Google Scholar
  10. 10.
    Ruiz-Alzola, J., Westin, C.F., et al.: Nonrigid registration of 3D tensor medical data. Medical Image Analysis 6(2), 143–161 (2002)CrossRefGoogle Scholar
  11. 11.
    Stamm, A., Pérez, P., Barillot, C.: A new multi-fiber model for low angular resolution diffusion mri. In: ISBI, pp. 936–939. IEEE (2012)Google Scholar
  12. 12.
    Suarez, R.O., Commowick, O., et al.: Automated delineation of white matter fiber tracts with a multiple region-of-interest approach. Neuroimage 59(4), 3690–3700 (2012)CrossRefGoogle Scholar
  13. 13.
    Taquet, M., Scherrer, B., Commowick, O., Peters, J., Sahin, M., Macq, B., Warfield, S.K.: Registration and analysis of white matter group differences with a multi-fiber model. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 313–320. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Renaud Hédouin
    • 1
  • Olivier Commowick
    • 1
  • Aymeric Stamm
    • 2
  • Christian Barillot
    • 1
  1. 1.VISAGES: INSERM U746, CNRS UMR6074, INRIAUniv. of Rennes IRennesFrance
  2. 2.CRL, Children’s Hospital Boston, Harvard Medical SchoolBostonUSA

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