Longitudinal Brain MRI Analysis with Uncertain Registration

  • Ivor J. A. Simpson
  • MarkW. Woolrich
  • Adrian R. Groves
  • Julia A. Schnabel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)


In this paper we propose a novel approach for incorporating measures of spatial uncertainty, which are derived from non-rigid registration, into spatially normalised statistics. Current approaches to spatially normalised statistical analysis use point-estimates of the registration parameters. This is limiting as the registration will rarely be completely accurate, and therefore data smoothing is often used to compensate for the uncertainty of the mapping. We derive localised measurements of spatial uncertainty from a probabilistic registration framework, which provides a principled approach to image smoothing. We evaluate our method using longitudinal deformation features from a set of MR brain images acquired from the Alzheimer’s Disease Neuroimaging Initiative. These images are spatially normalised using our probabilistic registration algorithm. The spatially normalised longitudinal features are adaptively smoothed according to the registration uncertainty. The proposed adaptive smoothing shows improved classification results, (84% correct Alzheimer’s Disease vs. controls), over either not smoothing (79.6%), or using a Gaussian filter with σ = 2mm (78.8%).


Mild Cognitive Impairment Gaussian Smoothing Spatial Uncertainty Adaptive Smoothing Variational Free Energy 
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.
    Allassonniére, S., Amit, Y., Trouvè, A.: Toward a coherent statistical framework for dense deformable template estimation. Journal of the Royal Statistical Society, Series B 69(2) (2007)Google Scholar
  2. 2.
    Ashburner, J.: A fast diffeomorphic image registration algorithm. Neuroimage 38(1), 95–113 (2007)CrossRefGoogle Scholar
  3. 3.
    Attias, H.: A variational Bayesian framework for graphical models. In: Leen, T., Dietterich, T., Tresp, V. (eds.) NIPS 2000, vol. 12, pp. 209–215. MIT Press, Cambridge (2000)Google Scholar
  4. 4.
    Groves, A.R., Beckmann, C.F., Smith, S.M., Woolrich, M.W.: Linked independent component analysis for multimodal data fusion. NeuroImage 54(3), 2198–2217 (2011)CrossRefGoogle Scholar
  5. 5.
    Klein, A., Andersson, J., Ardekani, B., Ashburner, J., Avants, B., Chiang, M., Christensen, G., Collins, D., Gee, J., Hellier, P., Song, J., Jenkinson, M., Lepage, C., Rueckert, D., Thompson, P., Vercauteren, T., Woods, R., Mann, J., Parsey, R.: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46(3), 786–802 (2009)Google Scholar
  6. 6.
    Leow, A., Yanovsky, I., Parikshak, N., Hua, X., Lee, S., Toga, A., Jack Jr., C., Bernstein, M., Britson, P., Gunter, J., Ward, C., Borowski, B., Shaw, L., Trojanowski, J., Fleisher, A., Harvey, D., Kornak, J., Schuff, N., Alexander, G., Weiner, M., Thompson, P.: Alzheimer’s disease neuroimaging initiative: a one-year follow up study using tensor-based morphometry correlating degenerative rates, biomarkers and cognition. Neuroimage 45(3), 645–655 (2009)Google Scholar
  7. 7.
    Mueller, S., Weiner, M., Thal, L., Petersen, R., Jack, C., Jagust, W., Trojanowski, J., Toga, A., Beckett, L.: Alzheimer’s Disease Neuroimaging Initiative. Advances in Alzheimer’s and Parkinson’s Disease, 183–189 (2008)Google Scholar
  8. 8.
    Risholm, P., Pieper, S., Samset, E., Wells, W.: Summarizing and visualizing uncertainty in non-rigid registration. In: Jiang, T., Navab, N., Pluim, J., Viergever, M. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 554–561. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Scahill, R., Schott, J., Stevens, J., Rossor, M., Fox, N.: Mapping the evolution of regional atrophy in Alzheimer’s disease: unbiased analysis of fluid-registered serial MRI. Proceedings of the National Academy of Sciences of the United States of America 99(7), 4703 (2002)Google Scholar
  10. 10.
    Simpson, I., Woolrich, M., Schnabel, J.: Probabilistic segmentation propagation. In: Medical Image Understanding and Analysis 2011 (2011)Google Scholar
  11. 11.
    Smith, S., Zhang, Y., Jenkinson, M., Chen, J., Matthews, P., Federico, A., De Stefano, N.: Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 17(1), 479–489 (2002)CrossRefGoogle Scholar
  12. 12.
    Szeliski, R.: Bayesian modeling of uncertainty in low-level vision. International Journal of Computer Vision 5(3), 271–301 (1990)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ivor J. A. Simpson
    • 1
    • 2
  • MarkW. Woolrich
    • 2
    • 3
  • Adrian R. Groves
    • 2
  • Julia A. Schnabel
    • 1
  1. 1.Institute of Biomedical EngineeringUniversity of OxfordOxfordUK
  2. 2.Oxford Centre for Functional MRI of the BrainUniversity of OxfordOxfordUK
  3. 3.Oxford Centre for Human Brain ActivityUniversity of OxfordOxfordUK

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