Large Deformation Diffeomorphic Registration of Diffusion-Weighted Images with Explicit Orientation Optimization

  • Pei Zhang
  • Marc Niethammer
  • Dinggang Shen
  • Pew-Thian Yap
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)


We seek to compute a diffeomorphic map between a pair of diffusion-weighted images under large deformation. Unlike existing techniques, our method allows any diffusion model to be fitted after registration for subsequent multifaceted analysis. This is achieved by directly aligning the diffusion-weighted images using a large deformation diffeomorphic registration framework formulated from an optimal control perspective. Our algorithm seeks the optimal coordinate mapping by simultaneously considering structural alignment, local fiber reorientation, and deformation regularization. Our algorithm also incorporates a multi-kernel strategy to concurrently register anatomical structures of different scales. We demonstrate the efficacy of our approach using in vivo data and report on detailed qualitative and quantitative results in comparison with several different registration strategies.


Orientation Distribution Function Spatial Alignment Shooting Algorithm High Angular Resolution Diffusion Imaging Anisotropy Image 
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.
    Ashburner, J., Friston, K.J.: Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation. NeuroImage 55(3), 954–967 (2011)CrossRefGoogle Scholar
  2. 2.
    Beg, M.F., Miller, M.I., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. International Journal of Computer Vision 61(2), 139–157 (2005)CrossRefGoogle Scholar
  3. 3.
    Cao, Y., Miller, M.I., Winslow, R.L., Younes, L.: Large deformation diffeomorphic metric mapping of fiber orientations. In: Proceedings of IEEE Conference on International Conference on Computer Vision, pp. 1379–1386 (2005)Google Scholar
  4. 4.
    Dhollander, T., Veraart, J., Van Hecke, W., Maes, F., Sunaert, S., Sijbers, J., Suetens, P.: Feasibility and advantages of diffusion weighted imaging atlas construction in Q-space. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 166–173. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Du, J., Goh, A., Qiu, A.: Diffeomorphic metric mapping of high angular resolution diffusion imaging based on Riemannian structure of orientation distribution functions. IEEE Transactions on Medical Imaging 31(5), 1021–1033 (2012)CrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Glaunès, J., Qiu, A., Miller, M., Younes, L.: Large deformation diffeomorphic metric curve mapping. International Journal of Computer Vision 80(3), 317–336 (2008)CrossRefGoogle Scholar
  8. 8.
    Hong, X., Arlinghaus, L., Anderson, A.: Spatial normalization of the fiber orientation distribution based on high angular resolution diffusion imaging data. Magnetic Resonance in Medicine 61(6), 1520–1527 (2009)CrossRefGoogle Scholar
  9. 9.
    Raffelt, D., Tournier, J.D., Fripp, J., Crozier, S., Connelly, A., Salvado, O.: Symmetric diffeomorphic registration of fibre orientation distributions. NeuroImage 56(3), 1171–1180 (2011)CrossRefGoogle Scholar
  10. 10.
    Risser, L., Vialard, F.X., Wolz, R., Murgasova, M., Holm, D.D., Rueckert, D.: Simultaneous multi-scale registration using large deformation diffeomorphic metric mapping. IEEE Transactions on Medical Imaging 30(10), 1746–1759 (2011)CrossRefGoogle Scholar
  11. 11.
    Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: Efficient non-parametric image registration. NeuroImage 45(1), S61–S72 (2009)Google Scholar
  12. 12.
    Yap, P.T., Chen, Y., An, H., Yang, Y., Gilmore, J.H., Lin, W., Shen, D.: SPHERE: Spherical harmonic elastic registration of HARDI data. NeuroImage 55(2), 545–556 (2011)CrossRefGoogle Scholar
  13. 13.
    Yap, P.T., Shen, D.: Spatial transformation of DWI data using non-negative sparse representation. IEEE Transactions on Medical Imaging 31(11), 2035–2049 (2012)CrossRefGoogle Scholar
  14. 14.
    Yeo, B.T.T., Vercauteren, T., Fillard, P., Peyrat, J.M., Pennec, X., Golland, P., Ayache, N., Clatz, O.: DT-REFinD: Diffusion tensor registration with exact finite-strain differential. IEEE Transactions on Medical Imaging 28(12), 1914–1928 (2009)CrossRefGoogle Scholar
  15. 15.
    Zhang, H., Yushkevich, P.A., Alexander, D.C., Gee, J.C.: Deformable registration of diffusion tensor MR images with explicit orientation optimization. Medical Image Analysis 10(5), 764–785 (2006)CrossRefGoogle Scholar
  16. 16.
    Zhang, P., Niethammer, M., Shen, D., Yap, P.-T.: Large deformation diffeomorphic registration of diffusion-weighted images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 171–178. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Pei Zhang
    • 1
  • Marc Niethammer
    • 2
  • Dinggang Shen
    • 1
  • Pew-Thian Yap
    • 1
  1. 1.Department of Radiology, Biomedical Research Imaging Center (BRIC)The University of North Carolina at Chapel HillUSA
  2. 2.Department of Computer Science, Biomedical Research Imaging Center (BRIC)The University of North Carolina at Chapel HillUSA

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