Improving Registration Using Multi-channel Diffeomorphic Demons Combined with Certainty Maps

  • Daniel Forsberg
  • Yogesh Rathi
  • Sylvain Bouix
  • Demian Wassermann
  • Hans Knutsson
  • Carl-Fredrik Westin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7012)


The number of available imaging modalities increases both in clinical practice and in clinical studies. Even though data from multiple modalities might be available, image registration is typically only performed using data from a single modality. In this paper, we propose using certainty maps together with multi-channel diffeomorphic demons in order to improve both accuracy and robustness when performing image registration. The proposed method is evaluated using DTI data, multiple region overlap measures and a fiber bundle similarity metric.


Fractional Anisotropy Image Registration Demon Algorithm Womens Hospital 144x144 Encode 
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  1. 1.
    Avants, B., Duda, J., Zhang, H., Gee, J.: Multivariate normalization with symmetric diffeomorphisms for multivariate studies. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 359–366. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Forsberg, D., Andersson, M., Knutsson, H.: Adaptive anisotropic regularization of deformation fields for non-rigid registration using the morphon framework. In: ICASSP (2010)Google Scholar
  3. 3.
    Guimond, A., Guttmann, C., Warfield, S., Westin, C.F.: Deformable registration of DT-MRI data based on transformation invariant tensor characteristics. In: ISBI (2002)Google Scholar
  4. 4.
    Holden, M.: A Review of Geometric Transformations for Nonrigid Body Registration. IEEE Transactions on Medical Imaging (2008)Google Scholar
  5. 5.
    Knutsson, H., Westin, C.F.: Normalized and Differential Convolution: Methods for Interpolation and Filtering of Incomplete and Uncertain Data. In: CVPR (1993)Google Scholar
  6. 6.
    Kybic, J.: Bootstrap resampling for image registration uncertainty estimation without ground truth. IEEE Transactions on Image Processing (2010)Google Scholar
  7. 7.
    Li, Y., Verma, R.: Multichannel Image Registration by Feature-Based Information Fusion. IEEE Transactions on Medical Imaging (2011)Google Scholar
  8. 8.
    Park, H.J., Kubicki, M., Shenton, M.E., Guimond, A., McCarley, R.W., Maier, S.E., Kikinis, R., Jolesz, F.A., Westin, C.F.: Spatial Normalization of Diffusion Tensor MRI Using Multiple Channels. Neuroimage (2003)Google Scholar
  9. 9.
    Peyrat, J.-M., Delingette, H., Sermesant, M., Pennec, X., Xu, C., Ayache, N.: Registration of 4D time-series of cardiac images with multichannel diffeomorphic demons. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 972–979. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Risholm, P., Pieper, S., Samset, E., Wells III, W.M.: Summarizing and visualizing uncertainty in non-rigid registration. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 554–561. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Rohde, G.K., Pajevic, S., Pierpaoli, C., Basser, P.J.: A comprehensive approach for multi-channel image registration. In: Gee, J.C., Maintz, J.B.A., Vannier, M.W. (eds.) WBIR 2003. LNCS, vol. 2717, pp. 214–223. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  12. 12.
    Studholme, C.: Incorporating DTI data as a constraint in deformation tensor morphometry between T1 MR images. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 223–232. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Thirion, J.P.: Fast Non-Rigid Matching of 3D Medical Images. Research Report RR-2547, INRIA (1995)Google Scholar
  14. 14.
    Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Non-parametric diffeomorphic image registration with the demons algorithm. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 319–326. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Wang, Y., Gupta, A., Liu, Z., Zhang, H., Escolar, M., Gilmore, J., Gouttard, S., Fillard, P., Maltbie, E., Gerig, G., Styner, M.: Dti registration in atlas based fiber analysis of infantile krabbe disease. Neuroimage (2011)Google Scholar
  16. 16.
    Wassermann, D., Bloy, L., Kanterakis, E., Verma, R., Deriche, R.: Unsupervised white matter fiber clustering and tract probability map generation: Applications of a Gaussian process framework for white matter fibers. NeuroImage (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Daniel Forsberg
    • 1
    • 2
    • 3
  • Yogesh Rathi
    • 4
  • Sylvain Bouix
    • 4
  • Demian Wassermann
    • 4
  • Hans Knutsson
    • 2
    • 3
  • Carl-Fredrik Westin
    • 5
  1. 1.Sectra ImtecLinköpingSweden
  2. 2.Department of Biomedical EngineeringLinköping UniversitySweden
  3. 3.Center for Medical Image Science and Visualization (CMIV)Linköping UniversitySweden
  4. 4.Psychiatry Neuroimaging Laboratory, Brigham and Womens HospitalHarvard Medical SchoolBostonUSA
  5. 5.Laboratory of Mathematics in Imaging, Brigham and Womens HospitalHarvard Medical SchoolBostonUSA

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