TY - JOUR
T1 - A novel framework for longitudinal atlas construction with groupwise registration of subject image sequences
AU - Liao, Shu
AU - Jia, Hongjun
AU - Wu, Guorong
AU - Shen, Dinggang
N1 - Funding Information:
Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904 ). ADNI is funded by the National Institute on Aging , the National Institute of Biomedical Imaging and Bioengineering , and through generous contributions from the following: Abbott , AstraZeneca AB , Bayer Schering Pharma AG , Bristol-Myers Squibb , Eisai Global Clinical Development , Elan Corporation , Genentech , GE Healthcare , GlaxoSmithKline , Innogenetics , Johnson and Johnson , Eli Lilly and Co. , Medpace, Inc. , Merck and Co., Inc. , Novartis AG , Pfizer Inc , F. Hoffman-La Roche , Schering-Plough , Synarc, Inc. , as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation , with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129 , K01 AG030514 , and the Dana Foundation .
Funding Information:
This work was supported in part by NIH grants EB006733 , EB008374 , EB009634 and MH088520 . The BLSA dataset used in this paper was provided by Dr. Susan Resnick and Dr. Christos Davatzikos.
Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2012/1/16
Y1 - 2012/1/16
N2 - Longitudinal atlas construction plays an important role in medical image analysis. Given a set of longitudinal images from different subjects, the task of longitudinal atlas construction is to build an atlas sequence which can represent the trend of anatomical changes of the population. The major challenge for longitudinal atlas construction is how to effectively incorporate both the subject-specific information and population information to build the unbiased atlases. In this paper, a novel groupwise longitudinal atlas construction framework is proposed to address this challenge, and the main contributions of the proposed framework lie in the following aspects: (1) The subject-specific longitudinal information is captured by building the growth model for each subject. (2) The longitudinal atlas sequence is constructed by performing groupwise registration among all the subject image sequences, and only one transformation is needed to transform each subject's image sequence to the atlas space. The constructed longitudinal atlases are unbiased and no explicit template is assumed. (3) The proposed method is general, where the number of longitudinal images of each subject and the time points at which they are taken can be different. The proposed method is extensively evaluated on two longitudinal databases, namely the BLSA and ADNI databases, to construct the longitudinal atlas sequence. It is also compared with a state-of-the-art longitudinal atlas construction algorithm based on kernel regression on the temporal domain. Experimental results demonstrate that the proposed method consistently achieves higher registration accuracies and more consistent spatial-temporal correspondences than the compared method on both databases.
AB - Longitudinal atlas construction plays an important role in medical image analysis. Given a set of longitudinal images from different subjects, the task of longitudinal atlas construction is to build an atlas sequence which can represent the trend of anatomical changes of the population. The major challenge for longitudinal atlas construction is how to effectively incorporate both the subject-specific information and population information to build the unbiased atlases. In this paper, a novel groupwise longitudinal atlas construction framework is proposed to address this challenge, and the main contributions of the proposed framework lie in the following aspects: (1) The subject-specific longitudinal information is captured by building the growth model for each subject. (2) The longitudinal atlas sequence is constructed by performing groupwise registration among all the subject image sequences, and only one transformation is needed to transform each subject's image sequence to the atlas space. The constructed longitudinal atlases are unbiased and no explicit template is assumed. (3) The proposed method is general, where the number of longitudinal images of each subject and the time points at which they are taken can be different. The proposed method is extensively evaluated on two longitudinal databases, namely the BLSA and ADNI databases, to construct the longitudinal atlas sequence. It is also compared with a state-of-the-art longitudinal atlas construction algorithm based on kernel regression on the temporal domain. Experimental results demonstrate that the proposed method consistently achieves higher registration accuracies and more consistent spatial-temporal correspondences than the compared method on both databases.
KW - Groupwise registration
KW - Longitudinal atlas construction
KW - Unbiased atlas
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U2 - 10.1016/j.neuroimage.2011.07.095
DO - 10.1016/j.neuroimage.2011.07.095
M3 - Article
C2 - 21884801
AN - SCOPUS:83055188093
VL - 59
SP - 1275
EP - 1289
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
IS - 2
ER -