TY - GEN
T1 - Graph-constrained sparse construction of longitudinal diffusion-weighted infant atlases
AU - Kim, Jaeil
AU - Chen, Geng
AU - Lin, Weili
AU - Yap, Pew Thian
AU - Shen, Dinggang
N1 - Funding Information:
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.
Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - 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 l2,1 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.
AB - 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 l2,1 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.
UR - http://www.scopus.com/inward/record.url?scp=85029366095&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-66182-7_6
DO - 10.1007/978-3-319-66182-7_6
M3 - Conference contribution
AN - SCOPUS:85029366095
SN - 9783319661810
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 49
EP - 56
BT - Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
A2 - Descoteaux, Maxime
A2 - Duchesne, Simon
A2 - Franz, Alfred
A2 - Jannin, Pierre
A2 - Collins, D. Louis
A2 - Maier-Hein, Lena
PB - Springer Verlag
T2 - 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Y2 - 11 September 2017 through 13 September 2017
ER -