Delay Correlation Subspace Decomposition Algorithm and Its Application in fMRI

Author

Chen, Huanting ; Yao, Danfeng ; Chen, Weijie ; Chen, Luo-nan

Volume

24

Issue

12

fYear

2005

Firstpage

1647

Lastpage

1651

Abstract

This paper reports a new delay subspace decomposition (DSD) algorithm. Instead of using the canonical zero-delay correlation matrix, the new DSD algorithm introduces a delay into the correlation matrix of the subspace decomposition to suppress noises in the data. The algorithm is applied to functional magnetic resonance imaging (fMRI) to detect the regions of focal activities in the brain. The efficiency is evaluated by comparing with independent component analysis and principal component analysis method of fMRI.

Keywords

Functional magnetic resonance imaging (fMRI); independent component analysis (ICA); noise suppression; principal component analysis (PCA); subspace decomposition; Brain; Data analysis; Delay; Independent component analysis; Magnetic noise; Magnetic resonance imaging; Matrix decomposition; Multiple signal classification; Principal component analysis; Shape; Functional magnetic resonance imaging (fMRI); independent component analysis (ICA); noise suppression; principal component analysis (PCA); subspace decomposition; Algorithms; Brain; Brain Mapping; Evoked Potentials, Visual; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Principal Component Analysis; Reproducibility of Results; Sensitivity and Specificity; Statistics as Topic; Time Factors;

fLanguage

English

Journal_Title

Medical Imaging, IEEE Transactions on

Publisher

ieee

ISSN

0278-0062

Type

jour

DOI

10.1109/TMI.2005.859211

Filename

1546125