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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
Collins, D.L., Evans, A.C.: ANIMAL: validation and applications of non-linear registration-based segmentation. Int. J. Pattern R. 11, 1271–1294 (1997)
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)
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)
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)
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)
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)
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)
AC Evans and Brain Development Cooperative Group. The NIH MRI Study of Normal Brain Development 30(1), 184–202 (2006)
Fonov, V., Evans, A., Botteron, K., McKinstry, R., Collins, D.: Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 54(1), 313–327 (2011)
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)
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)
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)
Reuter, M., Schmansky, N., Rosas, H., Fischl, B.: Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61(4), 1402–1418 (2012)
Shen, D., Davatzikos, C.: Measuring temporal morphological changes robustly in brain MR images via 4-D template warping. Neuroimage 21(4), 1508–1517 (2004)
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)
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)
Xue, Z., Shen, D., Davatzikos, C.: CLASSIC: consistent longitudinal alignment and segmentation for serial image computing. Neuroimage 30(2), 388–399 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aubert-Broche, B. et al. (2012). A New Framework for Analyzing Structural Volume Changes of Longitudinal Brain MRI Data. In: Durrleman, S., Fletcher, T., Gerig, G., Niethammer, M. (eds) Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data. STIA 2012. Lecture Notes in Computer Science, vol 7570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33555-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-33555-6_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33554-9
Online ISBN: 978-3-642-33555-6
eBook Packages: Computer ScienceComputer Science (R0)