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.
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