TY - GEN
T1 - Quality map thresholding for de-noising of complex-valued fMRI data and its application to ICA of fMRI
AU - Rodriguez, Pedro A.
AU - Correa, Nicolle M.
AU - Adali, Tülay
AU - Calhoun, Vince D.
PY - 2009/12/1
Y1 - 2009/12/1
N2 - Although functional magnetic resonance imaging (fMRI) data are acquired as complex-valued images, traditionally most fMRI studies only use the magnitude of the data. FMRI analysis in the complex domain promises to provide more statistically significant information; however, the noisy nature of the phase poses a challenge for successful study of fMRI by complex-valued signal processing algorithms. In this paper, we introduce a physiologically motivated de-noising method that uses phase quality maps and demonstrate its effectiveness in successfully identifying and eliminating noisy areas in the fMRI data. Additionally, we show how the developed de-noising method improves the results of complex-valued independent component analysis of fMRI data, a very successful tool for blind source separation of biomedical data.
AB - Although functional magnetic resonance imaging (fMRI) data are acquired as complex-valued images, traditionally most fMRI studies only use the magnitude of the data. FMRI analysis in the complex domain promises to provide more statistically significant information; however, the noisy nature of the phase poses a challenge for successful study of fMRI by complex-valued signal processing algorithms. In this paper, we introduce a physiologically motivated de-noising method that uses phase quality maps and demonstrate its effectiveness in successfully identifying and eliminating noisy areas in the fMRI data. Additionally, we show how the developed de-noising method improves the results of complex-valued independent component analysis of fMRI data, a very successful tool for blind source separation of biomedical data.
UR - http://www.scopus.com/inward/record.url?scp=77950955100&partnerID=8YFLogxK
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U2 - 10.1109/MLSP.2009.5306263
DO - 10.1109/MLSP.2009.5306263
M3 - Conference contribution
AN - SCOPUS:77950955100
SN - 9781424449484
T3 - Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009
BT - Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009
T2 - Machine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009
Y2 - 2 September 2009 through 4 September 2009
ER -