Advertisement

Spatio-temporal Regularization for Longitudinal Registration to an Unbiased 3D Individual Template

  • Nicolas Guizard
  • Vladimir S. Fonov
  • Daniel García-Lorenzo
  • Bérengère Aubert-Broche
  • Simon F. Eskildsen
  • D. Louis Collins
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7570)

Abstract

Neurodegenerative diseases such as Alzheimer’s disease present subtle anatomical brain changes before the appearance of clinical symptoms. Large longitudinal brain imaging datasets are now accessible to investigate these structural changes over time. However, manual structure segmentation is long and tedious and although automatic methods exist, they are often performed in a cross-sectional manner where each visit is analysed independently. With such analysis methods, bias, error and longitudinal noise may be introduced. Noise due to MR scanners and other physiological effects may also introduce variability in the measurement. We propose to use 4D non-linear registration with spatio-temporal regularization to correct for longitudinal inconsistency in the context of structure segmentation. The major contribution of this article is the individual template creation with spatio-temporal regularization of the deformation fields for each subject. We validate our method with different sets of real MRI data and demonstrate that spatially local temporal regularization yields more consistent rates of change of global structures resulting in better statistical power for detecting significant changes occurring between populations.

Keywords

Longitudinal registration spatio-temporal consistency unbiased template creation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chard, D.T., Brex, P.A., Ciccarelli, O., Griffin, C.M., Parker, G.J., Dalton, C., Altmann, D.R., Thompson, A.J., Miller, D.H.: The longitudinal relation between brain lesion load and atrophy in multiple sclerosis: a 14 year follow up study. Journal of Neurology, Neurosurgery, and Psychiatry 74, 1551–1554 (2003)CrossRefGoogle Scholar
  2. 2.
    Burton, E., McKeith, I., Burn, D., Williams, D., O’Brien, J.: Cerebral atrophy in Parkinson’s disease with and without dementia: a comparison with Alzheimer’s disease, dementia with Lewy bodies and controls. Brain 127, 791–800 (2004)CrossRefGoogle Scholar
  3. 3.
    Ridha, B., Barnes, J., Bartlett, J., Godbolt, A., Pepple, T., Rossor, M., Fox, N.: Tracking atrophy progression in familial Alzheimer’s disease: a serial MRI study. Lancet Neurology 5, 828–834 (2006)CrossRefGoogle Scholar
  4. 4.
    Mueller, S., Weiner, M., Thal, L., Petersen, R., Jack, C., Jagust, W., Trojanowski, J., Toga, A., Beckett, L.: The Alzheimer’s disease neuroimaging initiative. Neuroimaging Clinics of North America 15, 869–877 (2005)CrossRefGoogle Scholar
  5. 5.
    Marcus, D., Fotenos, A., Csernansky, J., Morris, J., Buckner, R.: Open Access Series of Imaging Studies: Longitudinal MRI Data in Nondemented and Demented Older Adults. Journal of Cognitive Neuroscience 22, 2677–2684 (2009)CrossRefGoogle Scholar
  6. 6.
    Thompson, W., Holland, D.: Bias in tensor based morphometry Stat-ROI measures result in unrealistic power estimates. NeuroImage 57, 1–4 (2011)CrossRefGoogle Scholar
  7. 7.
    Caramanos, Z., Fonov, V., Francis, S., Narayanan, S., Pike, B., Collins, L., Arnold, D.: Gradient distortions in MRI: Characterizing and correcting for their effects on SIENA-generated measures of brain volume change. NeuroImage 49, 1601–1611 (2010)CrossRefGoogle Scholar
  8. 8.
    Westlye, L., Walhovd, K., Dale, A., Espeseth, T., Reinvang, I., Raz, N., Agartz, I., Greve, D., Fischl, B., Fjell, A.: Increased sensitivity to effects of normal aging and Alzheimer’s disease on cortical thickness by adjustment for local variability in gray/white contrast: A multi-sample MRI study. NeuroImage 47, 1545–1557 (2009)CrossRefGoogle Scholar
  9. 9.
    Reuter, M., Schmansky, N., Rosas, D., Fischl, B.: Within-subject template estimation for unbiased longitudinal image analysis. NeuroImage 61, 1402–1418 (2012)CrossRefGoogle Scholar
  10. 10.
    Davis, B.C., Fletcher, P.T., Bullitt, E., Joshi, S.: Population Shape Regression from Random Design Data. In: IEEE 11th International Conference on Computer Vision, ICCV, pp. 1–7 (2007)Google Scholar
  11. 11.
    Hart, G., Shi, Y., Zhu, H., Sanchez, M., Styner, M., Niethammer, M.: DTI Longitudinal Atlas Construction as an Average of Growth Models (2010)Google Scholar
  12. 12.
    Prastawa, M., Awate, S., Gerig, G.: Building spatiotemporal anatomical models using joint 4-D segmentation, registration, and subject-specific atlas estimation. In: 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 49–56. IEEE (2012)Google Scholar
  13. 13.
    Durrleman, S., Pennec, X., Trouvé, A., Gerig, G., Ayache, N.: Spatiotemporal Atlas Estimation for Developmental Delay Detection in Longitudinal Datasets. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part I. LNCS, vol. 5761, pp. 297–304. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Lorenzi, M., Ayache, N., Frisoni, G., Pennec, X.: 4D registration of serial brain’s MR images: a robust measure of changes applied to Alzheimer’s disease. In: Medical Image Computing and Computer-Assisted Intervention, MICCAI 13, Patiotemporal Image Analysis for Longitudinal and Time-Series Image Data (STIA) Workshop (2010)Google Scholar
  15. 15.
    Thompson, P., Toga, A.: A framework for computational anatomy. Computing and Visualization in Science 5, 13–34 (2002)zbMATHCrossRefGoogle Scholar
  16. 16.
    Collins, L., Evans, A.C.: ANIMAL: Validation and Applications of Non-Linear Registration-Based Segmentation. International Journal of Pattern Recognition and Artificial Intelligence 11, 1271–1294 (1997)CrossRefGoogle Scholar
  17. 17.
    Ashburner, J., Hutton, C., Frackowiak, R., Johnsrude, I., Price, C., Friston, K.: Identifying global anatomical differences: Deformation-based morphometry. Human Brain Mapping 6, 348–357 (1998)CrossRefGoogle Scholar
  18. 18.
    Guimond, A., Meunier, J., Thirion, J.-P.: Automatic Computation of Average Brain Models, p. 631 (1998)Google Scholar
  19. 19.
    Fonov, V., Evans, A., Botteron, K., Almli, R., McKinstry, R., Collins, L.: Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54, 313–327 (2011)CrossRefGoogle Scholar
  20. 20.
    Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging 17, 87–97 (1998)CrossRefGoogle Scholar
  21. 21.
    Eskildsen, S., Coupé, P., Fonov, V., Manjón, J., Leung, K., Guizard, N., Wassef, S., Østergaard, L., Collins, L.: BEaST: Brain extraction based on nonlocal segmentation technique. NeuroImage 59, 2362–2373 (2012)CrossRefGoogle Scholar
  22. 22.
    Nyúl, L., Udupa, J.: On standardizing the MR image intensity scale. Magnetic Resonance in Medicine 42, 1072–1081 (1999)CrossRefGoogle Scholar
  23. 23.
    Collins, D.L., Neelin, P., Peters, T.M., Evans, A.C.: Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Journal of Computer Assisted Tomography 18, 192–205 (1994)CrossRefGoogle Scholar
  24. 24.
    Miller, M., Banerjee, A., Christensen, G., Joshi, S., Khaneja, N., Grenander, U., Matejic, L.: Statistical methods in computational anatomy. Statistical Methods in Medical Research 6, 267–299 (1997)CrossRefGoogle Scholar
  25. 25.
    Nestor, S., Rupsingh, R., Borrie, M., Smith, M., Accomazzi, V., Wells, J., Fogarty, J., Bartha, R.: The Alzheimer’s Disease Neuroimaging, I.: Ventricular enlargement as a possible measure of Alzheimer’s disease progression validated using the Alzheimer’s disease neuroimaging initiative database. Brain 131, 2443–2454 (2008)CrossRefGoogle Scholar
  26. 26.
    Apostolova, L., Green, A., Babakchanian, S., Hwang, K., Chou, Y.-Y., Toga, A., Thompson, P.: Hippocampal Atrophy and Ventricular Enlargement in Normal Aging, Mild Cognitive Impairment (MCI), and Alzheimer Disease. Alzheimer Disease & Associated Disorders 26, 17–27 (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nicolas Guizard
    • 1
  • Vladimir S. Fonov
    • 1
  • Daniel García-Lorenzo
    • 1
    • 3
  • Bérengère Aubert-Broche
    • 1
  • Simon F. Eskildsen
    • 1
    • 2
  • D. Louis Collins
    • 1
  1. 1.Montreal Neurological InstituteMcGill UniversityCanada
  2. 2.Center of Functionally Integrative NeuroscienceAarhus UniversityDenmark
  3. 3.CENIR - ICM, Pitié SalpétrièreParisFrance

Personalised recommendations