ArticleProceedings. IEEE International Symposium on Biomedical Imaging2017
A Novel Framework for Groupwise Registration of fMRI Images based on Common Functional Networks.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Non-Rigid Cycle Consistent Bidirectional Network with Transformer for Unsupervised Deformable Functional Magnetic Resonance Imaging Registration.Brain sciences · 2025Article
- fMRI-based Alzheimer's disease detection via functional connectivity analysis: a systematic review.PeerJ. Computer science · 2024Article
- rfDemons: Resting fMRI-based Cortical Surface Registration using the BrainSync Transform.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2018Article
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8 authors.
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Abstract
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.
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