NDLI: fMRI Noise Reduction Based on Canonical Correlation Analysis and Surrogate Test

Content Provider	IEEE Xplore Digital Library
Author	Yadong Liu Dewen Hu Zongtan Zhou Hui Shen Xiang Wang
Copyright Year	2007
Abstract	In this paper, we proposed a noise-reduction method for functional magnetic resonance imaging (fMRI). We classified noise into structured and unstructured ones. Canonical correlation analysis was exploited to extract the underlying components among which the structured ones were recognised. Furtherly, The task-related components were detected among the structured ones by using surrogate test based on reduced autoregression model. The low degree of temporal correlation of the unstructured residuals was reduced by using randomization technique. The task-related components and the randomly permuted unstructured residuals were used to generate the reconstructed data. With application of our method, SNR of data can be significantly improved. In addition, the temporal correlation of unstructured background noise can be efficiently reduced. Twenty sets of true fMRI data for finger tapping task were processed. Some task-related areas which cannot be detected from the original data set were discerned.
Sponsorship	IEEE Signal Processing Society
Starting Page	870
Ending Page	878
Page Count	9
File Size	1176167
File Format	PDF
ISSN	19324553
Volume Number	2
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise reduction Testing Magnetic noise Signal to noise ratio Background noise Colored noise Magnetic analysis Independent component analysis Magnetic resonance imaging Signal analysis surrogate test Canonical correlation analysis (CCA) noise reduction randomization
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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