TY - GEN
T1 - A novel framework for groupwise registration of fMRI images based on common functional networks
AU - Zhao, Yu
AU - Zhang, Shu
AU - Chen, Hanbo
AU - Zhang, Wei
AU - Lv, Jinglei
AU - Jiang, Xi
AU - Shen, Dinggang
AU - Liu, Tianming
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/15
Y1 - 2017/6/15
N2 - Accurate registration plays a critical role in group-wise functional Magnetic Resonance Imaging (fMRI) image analysis, as spatial correspondence among different brain images is a prerequisite for inferring meaningful patterns. However, the problem is challenging and remains open, and more effort should be made to advance the state-of-the-art image registration methods for fMRI images. Inspired by the observation that common functional networks can be reconstructed from fMRI image across individuals, we propose a novel computational framework for simultaneous groupwise fMRI image registration by utilizing those common functional networks as references for spatial alignments. In this framework, firstly, individualized functional networks in each subject are inferred using Independent Component Analysis (ICA); secondly, congealing groupwise registration that takes entropy of stacked independent components (ICs) from all the subjects as objective function is applied to register individual functional maps for maximal matching. The proposed framework is evaluated by and applied to an Alzheimer's Disease (AD) fMRI dataset and shows reasonably good results.
AB - Accurate registration plays a critical role in group-wise functional Magnetic Resonance Imaging (fMRI) image analysis, as spatial correspondence among different brain images is a prerequisite for inferring meaningful patterns. However, the problem is challenging and remains open, and more effort should be made to advance the state-of-the-art image registration methods for fMRI images. Inspired by the observation that common functional networks can be reconstructed from fMRI image across individuals, we propose a novel computational framework for simultaneous groupwise fMRI image registration by utilizing those common functional networks as references for spatial alignments. In this framework, firstly, individualized functional networks in each subject are inferred using Independent Component Analysis (ICA); secondly, congealing groupwise registration that takes entropy of stacked independent components (ICs) from all the subjects as objective function is applied to register individual functional maps for maximal matching. The proposed framework is evaluated by and applied to an Alzheimer's Disease (AD) fMRI dataset and shows reasonably good results.
KW - Alzheimer's Disease
KW - FMRI
KW - Functional networks
KW - Groupwise registration
KW - ICA
UR - http://www.scopus.com/inward/record.url?scp=85023208331&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85023208331&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2017.7950566
DO - 10.1109/ISBI.2017.7950566
M3 - Conference contribution
AN - SCOPUS:85023208331
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 485
EP - 489
BT - 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
PB - IEEE Computer Society
T2 - 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
Y2 - 18 April 2017 through 21 April 2017
ER -