DocumentCode :
1655084
Title :
Collaborative denoising of multi-subject fMRI data
Author :
Lorbert, Alexander ; Guntupalli, J. Swaroop ; Eis, David J. ; Haxby, James V. ; Ramadge, Peter J.
Author_Institution :
Dept. of Electr. Eng., Princeton Univ., Princeton, NJ, USA
fYear :
2013
Firstpage :
1008
Lastpage :
1012
Abstract :
We propose a novel collaborative denoising scheme for multi-subject fMRI data. The scheme assumes that subjects experience a common, synchronous stimulus and uses the across-subject shared response structure to jointly denoise each subject´s fMRI response along the spatial or voxel domain. Denoising is accomplished by learning subject-specfic orthonormal bases that yield sparse representations in a common transform domain. We provide empirical results using a real-world, multi-subject fMRI dataset.
Keywords :
biomedical MRI; image denoising; medical image processing; collaborative denoising; multisubject fMRI data; subject-specfic orthonormal bases; Accuracy; Collaboration; Correlation; Motion pictures; Noise reduction; Transforms; Vectors; Procrustes problems; fMRI; principal axes; signal denoising;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
Conference_Location :
Vancouver, BC
ISSN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2013.6637801
Filename :
6637801
Link To Document :
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