@inproceedings{9f85918e5d2f4c7cb94452d84e7b6e27,
title = "Signal sampling for efficient sparse representation of resting state FMRI data",
abstract = "As brain imaging data such as fMRI is growing explosively, how to reduce its size but not to lose much information becomes a pressing problem. To address this problem, this work aims to represent resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse representation. Specifically, we improve the online dictionary learning and sparse coding algorithm by adding a sampling step before the whole-brain sparse representation. Our comparison experiments demonstrated that this sampling-enabled sparse representation method can speedup by ten times without losing much information. In particular, our results showed that anatomical landmark-guided sampling is substantially better than statistical random sampling in reconstructing concurrent functional brain networks from the Human Connectome Project (HCP) rs-fMRI data.",
keywords = "DICCCOL, DTI, resting state fMRI, resting state networks, sampling",
author = "Bao Ge and Jin Wang and Jinglei Lv and Shu Zhang and Shijie Zhao and Wei Zhang and Qinghua Zhao and Xiang Li and Xi Jiang and Junwei Han and Lei Guo and Tianming Liu",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 ; Conference date: 16-04-2015 Through 19-04-2015",
year = "2015",
month = jul,
day = "21",
doi = "10.1109/ISBI.2015.7164128",
language = "英语",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1360--1363",
booktitle = "2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015",
}