Consistent Spatial-Temporal Longitudinal Atlas Construction for Developing Infant Brains
- Authors
- Zhang, Yuyao; Shi, Feng; Wu, Guorong; Wang, Li; Yap, Pew-Thian; Shen, Dinggang
- Issue Date
- 12월-2016
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Magnetic resonance imaging (MRI); Brain; atlases; Image enhancement (noise and artifact reduction); Infant; Neonate; Spatial-temporal consistency; Longitudinal atlas; Sparse representation; Wavelet domain; Frequency subbands
- Citation
- IEEE TRANSACTIONS ON MEDICAL IMAGING, v.35, no.12, pp.2568 - 2577
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON MEDICAL IMAGING
- Volume
- 35
- Number
- 12
- Start Page
- 2568
- End Page
- 2577
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/86641
- DOI
- 10.1109/TMI.2016.2587628
- ISSN
- 0278-0062
- Abstract
- Brain atlases are an essential component in understanding the dynamic cerebral development, especially for the early postnatal period. However, longitudinal atlases are rare for infants, and the existing ones are generally limited by their fuzzy appearance. Moreover, since longitudinal atlas construction is typically performed independently over time, the constructed atlases often fail to preserve temporal consistency. This problem is further aggravated for infant images since they typically have low spatial resolution and insufficient tissue contrast. In this paper, we propose a novel framework for consistent spatial-temporal construction of longitudinal atlases for developing infant brain MR images. Specifically, for preserving structural details, the atlas construction is performed in spatial-temporal wavelet domain simultaneously. This is achieved by a patch-based combination of results from each frequency subband. Compared with the existing infant longitudinal atlases, our experimental results indicate that our approach is able to produce longitudinal atlases with richer structural details and also better longitudinal consistency, thus leading to higher performance when used for spatial normalization of a group of infant brain images.
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Collections - Graduate School > Department of Artificial Intelligence > 1. Journal Articles
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