Abstract
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.
| Original language | English (US) |
|---|---|
| Title of host publication | Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009 |
| DOIs | |
| State | Published - 2009 |
| Externally published | Yes |
| Event | Machine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009 - Grenoble, France Duration: Sep 2 2009 → Sep 4 2009 |
Other
| Other | Machine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009 |
|---|---|
| Country | France |
| City | Grenoble |
| Period | 9/2/09 → 9/4/09 |
Fingerprint
ASJC Scopus subject areas
- Human-Computer Interaction
- Signal Processing
- Education
Cite this
Quality map thresholding for de-noising of complex-valued fMRI data and its application to ICA of fMRI. / Rodriguez, Pedro A.; Correa, Nicolle M.; Adali, Tülay; Calhoun, Vince Daniel.
Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009. 2009. 5306263.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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 Daniel
PY - 2009
Y1 - 2009
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
UR - http://www.scopus.com/inward/citedby.url?scp=77950955100&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2009.5306263
DO - 10.1109/MLSP.2009.5306263
M3 - Conference contribution
AN - SCOPUS:77950955100
SN - 9781424449484
BT - Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009
ER -