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A New Framework for Analyzing Structural Volume Changes of Longitudinal Brain MRI Data

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

Abstract

Cross-sectional analysis of longitudinal MRI data might be sub-optimal as each dataset is analyzed independently. In this study, we evaluate how much variability can be reduced by analyzing structural volume changes of longitudinal data using longitudinal analysis. We propose a two-part pipeline that consists of longitudinal registration and longitudinal classification. The longitudinal registration step includes the creation of subject-specific linear and non-linear templates that are then registered to a population template. The longitudinal classification is composed of a 4D EM algorithm, using a priori classes computed by averaging the tissue classes of all time points obtained cross-sectionally.

To study the impact of these two steps, we apply the framework completely (called LL method: Longitudinal registration and Longitudinal classification) and partially (LC method: Longitudinal registration and Cross-sectional classification) and compare these to a standard cross-sectional framework (CC method: Cross-sectional registration and Cross-sectional classification).

The three methods are applied to (1) a scan-rescan database to analyze the reliability and to (2) the NIH pediatric population to compare the GM and WM growth trajectories, evaluated with a linear mixed-model. The LL method, and the LC method to a lesser extent, significantly reduce the variability in the measurements in the scan-rescan study and give the best fitted GM and WM growth models with the NIH pediatric database. The results confirm that both steps of the longitudinal framework reduce the variability and improve the accuracy compared to the cross-sectional framework, with longitudinal classification yielding the greatest impact.

Keywords

White Matter Grey Matter Bayesian Information Criterion White Matter Volume Stereotaxic Space 
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.

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References

  1. 1.
    Cocosco, C.A., Zijdenbos, A., Evans, A.C.: A fully automatic and robust brain MRI tissue classification method. Med. Image Anal. 7(4), 513–527 (2003)CrossRefGoogle Scholar
  2. 2.
    Collins, D.L., Evans, A.C.: ANIMAL: validation and applications of non-linear registration-based segmentation. Int. J. Pattern R. 11, 1271–1294 (1997)CrossRefGoogle Scholar
  3. 3.
    Collins, D.L., Neelin, P., Peters, T.M., Evans, A.C.: Automatic 3D inter-subject registration of MR volumetric data in standardized Talairach space. J. Comput. Assist. Tomo. 18, 192–205 (1994)CrossRefGoogle Scholar
  4. 4.
    Collins, D.L., Zijdenbos, A.P., Baaré, W.F.C., Evans, A.C.: ANIMAL+INSECT: Improved Cortical Structure Segmentation. In: Kuba, A., Sámal, M., Todd-Pokropek, A. (eds.) IPMI 1999. LNCS, vol. 1613, pp. 210–223. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  5. 5.
    Coupe, P., Manjon, J.V., Gedamu, E., Arnold, D., Robles, M., Collins, D.L.: Robust Rician Noise Estimation for MR Images. Med. Image Anal. 14(4), 483–493 (2010)CrossRefGoogle Scholar
  6. 6.
    Coupe, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., Barillot, C.: An optimized blockwise nonlocal means denoising filter for 3-d magnetic resonance images. IEEE Trans. Med. Imaging 27(4), 425–441 (2008)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Eskildsen, S.F., Coup, P., Fonov, V., Manjn, J.V., Leung, K.K., Guizard, N., Wassef, S.N., Ostergaard, L.R., Collins, D.L.: BEaST: Brain extraction based on nonlocal segmentation technique. Neuroimage 59(3), 2362–2373 (2012)CrossRefGoogle Scholar
  9. 9.
    AC Evans and Brain Development Cooperative Group. The NIH MRI Study of Normal Brain Development 30(1), 184–202 (2006)Google Scholar
  10. 10.
    Fonov, V., Evans, A., Botteron, K., McKinstry, R., Collins, D.: Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 54(1), 313–327 (2011)CrossRefGoogle Scholar
  11. 11.
    Garcia-Lorenzo, D., Prima, S., Arnold, D.L., Collins, D.L., Barillot, C.: Trimmed-likelihood estimation for focal lesions and tissue segmentation in multisequence MRI for multiple sclerosis. IEEE Trans. Med. Imaging 30(8), 1455–1467 (2011)CrossRefGoogle Scholar
  12. 12.
    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: STIA, MICCAI (2010)Google Scholar
  13. 13.
    Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., R Core Team: nlme: Linear and nonlinear mixed effects models. R package version 3.1-104 (2012)Google Scholar
  14. 14.
    Reuter, M., Schmansky, N., Rosas, H., Fischl, B.: Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61(4), 1402–1418 (2012)CrossRefGoogle Scholar
  15. 15.
    Shen, D., Davatzikos, C.: Measuring temporal morphological changes robustly in brain MR images via 4-D template warping. Neuroimage 21(4), 1508–1517 (2004)CrossRefGoogle Scholar
  16. 16.
    Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 17(1), 87–97 (1998)CrossRefGoogle Scholar
  17. 17.
    Wolz, R., Heckemann, R.A., Aljabar, P., Hajnal, J.V., Hammers, A., Lotjonen, J., Rueckert, D.: Measurement of hippocampal atrophy using 4D graph-cut segmentation: Application to ADNI. Neuroimage 52(1), 109–118 (2010)CrossRefGoogle Scholar
  18. 18.
    Xue, Z., Shen, D., Davatzikos, C.: CLASSIC: consistent longitudinal alignment and segmentation for serial image computing. Neuroimage 30(2), 388–399 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bérengère Aubert-Broche
    • 1
  • Vladimir S. Fonov
    • 1
  • Daniel García-Lorenzo
    • 1
    • 2
  • Abderazzak Mouiha
    • 1
  • Nicolas Guizard
    • 1
  • Pierrick Coupé
    • 1
    • 3
  • Simon F. Eskildsen
    • 1
    • 4
  • D. Louis Collins
    • 1
  1. 1.Montreal Neurological InstituteMcGill UniversityCanada
  2. 2.ICM, UPMC/INSERM UMR975 CNRS UMR7225Hôpital de la SalpêtrièreParisFrance
  3. 3.Laboratoire Bordelais de Recherche en InformatiqueUnité Mixte de Recherche CNRS (UMR 5800)BordeauxFrance
  4. 4.Aarhus UniversityDenmark

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