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Graph-Constrained Sparse Construction of Longitudinal Diffusion-Weighted Infant Atlases

  • Jaeil Kim
  • Geng Chen
  • Weili Lin
  • Pew-Thian Yap
  • Dinggang ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

Constructing longitudinal diffusion-weighted atlases of infant brains poses additional challenges due to the small brain size and the dynamic changes in the early developing brains. In this paper, we introduce a novel framework for constructing longitudinally-consistent diffusion-weighted infant atlases with improved preservation of structural details and diffusion characteristics. In particular, instead of smoothing diffusion signals by simple averaging, our approach fuses the diffusion-weighted images in a patch-wise manner using sparse representation with a graph constraint that encourages spatiotemporal consistency. Diffusion-weighted atlases across time points are jointly constructed for patches that are correlated in time and space. Compared with existing methods, including the one using sparse representation with \(l_{2,1}\) regularization, our approach generates longitudinal infant atlases with much richer and more consistent features of the developing infant brain, as shown by the experimental results.

Notes

Acknowledgments

This work was supported in part by an NIH grants (1U01MH110274, NS093842, and EB022880) and the efforts of the UNC/UMN Baby Connectome Project Consortium.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jaeil Kim
    • 1
  • Geng Chen
    • 1
  • Weili Lin
    • 1
  • Pew-Thian Yap
    • 1
  • Dinggang Shen
    • 1
    Email author
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUS

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