Abstract
Building of atlases plays a crucial role in the analysis of brain images. In scenarios where early growth, aging or disease trajectories are of key importance, longitudinal atlases become necessary as references, most often created from cross-sectional data. New opportunities will be offered by creating longitudinal brain atlases from longitudinal subject-specific image data, where explicit modeling of subject’s variability in slope and intercept leads to a more robust estimation of average trajectories but also to estimates of confidence bounds. This work focuses on a framework to build a continuous 4D atlas from longitudinal high angular resolution diffusion images (HARDI) where, unlike atlases of derived scalar diffusion indices such as FA, statistics on dODFs is preserved. Multi-scalar images obtained from DW images are used for geometric alignment, and linear mixed-effects modeling from longitudinal diffusion orientation distribution functions (dODF) leads to estimation of continuous dODF changes. The proposed method is applied to a longitudinal dataset of HARDI images from healthy developing infants in the age range of 3 to 36 months. Verification of mixed-effects modeling is obtained by voxel-wise goodness of fit calculations. To demonstrate the potential of our method, we display changes of longitudinal atlas using dODF and derived generalized fractional anisotropy (GFA) of dODF. We also investigate white matter maturation patterns in genu, body, and splenium of the corpus callosum. The framework can be used to build an average dODF atlas from HARDI data and to derive subject-specific and population-based longitudinal change trajectories.
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 subscriptionsReferences
Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ants similarity metric performance in brain image registration. Neuroimage 54(3), 2033–2044 (2011)
Bouix, S., Rathi, Y., Sabuncu, M.: Building an average population hardi atlas. In: MICCAI Workshop on Computational Diffusion MRI (2010)
Descoteaux, M., Angelino, E., Fitzgibbons, S., Deriche, R.: Regularized, fast, and robust analytical q-ball imaging. Magn. Reson. Med. Official J. Inter. Soc. MR Med. 58(3), 497–510 (2007)
Du, J., Goh, A., Kushnarev, S., Qiu, A.: Geodesic regression on orientation distribution functions with its application to an aging study. NeuroImage 87, 416–426 (2014)
Elhabian, S., Vachet, C., Piven, J., Styner, M., Gerig, G.: Compressive sensing based q-space resampling for handling fast bulk motion in hardi acquisitions. In: 2016 IEEE 13th ISBI. IEEE (2016)
Geng, X., Gouttard, S., Sharma, A., Gu, H., Styner, M., Lin, W., Gerig, G., Gilmore, J.H.: Quantitative tract-based white matter development from birth to age 2 years. Neuroimage 61(3), 542–557 (2012)
Goodlett, C.B., Fletcher, P.T., Gilmore, J.H., Gerig, G.: Group analysis of dti fiber tract statistics with application to neurodevelopment. Neuroimage 45(1), S133–S142 (2009)
Kim, J., Chen, G., Lin, W., Yap, P.T., Shen, D.: Graph-constrained sparse construction of longitudinal diffusion-weighted infant atlases. In: MICCAI. Springer, Berlin (2017)
Pietsch, M., Christiaens, D., Hutter, J., Cordero-Grande, L., Price, A.N., Hughes, E., Edwards, A.D., Hajnal, J.V., Counsell, S.J., Tournier, J.D.: A framework for multi-component analysis of diffusion mri data over the neonatal period. NeuroImage (2018)
Sadeghi, N., Prastawa, M., Fletcher, P.T., Vachet, C., Wang, B., Gilmore, J., Gerig, G.: Multivariate modeling of longitudinal mri in early brain development with confidence measures. In: 2013 IEEE 10th ISBI. IEEE (2013)
Tuch, D.S.: Q-ball imaging. Magn. Reson. Med. 52(6), 1358–1372 (2004)
Yang, Z., Chen, G., Shen, D., Yap, P.T.: Robust fusion of diffusion mri data for template construction. Scientific reports 7(1), 12950 (2017)
Yap, P.T., Shen, D.: Spatial transformation of dwi data using non-negative sparse representation. IEEE Trans. Med. Imaging 31(11), 2035–2049 (2012)
Yeh, F.C., Tseng, W.Y.I.: Ntu-90: a high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction. Neuroimage 58(1), 91–99 (2011)
Zhang, H., Yushkevich, P.A., Rueckert, D., Gee, J.C.: Unbiased white matter atlas construction using diffusion tensor images. In: MICCAI. Springer, Berlin (2007)
Acknowledgments
This work was supported by the NIH grants R01-HD055741-12, 1R01HD089390-01A1, 1R01DA038215-01A1 and 1R01HD08812501A1. We are thankful for research discussions with Dr. Ragini Verma.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
The authors declare that there are no conflicts or commercial interest related to this article.
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kim, H., Styner, M., Piven, J., Gerig, G. (2020). A Framework to Construct a Longitudinal DW-MRI Infant Atlas Based on Mixed Effects Modeling of dODF Coefficients. In: Bonet-Carne, E., Hutter, J., Palombo, M., Pizzolato, M., Sepehrband, F., Zhang, F. (eds) Computational Diffusion MRI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-52893-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-52893-5_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-52892-8
Online ISBN: 978-3-030-52893-5
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)