Quality map thresholding for de-noising of complex-valued fMRI data and its application to ICA of fMRI

Pedro A. Rodriguez, Nicolle M. Correa, Tülay Adali, Vince Daniel Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publicationMachine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009
DOIs
StatePublished - 2009
Externally publishedYes
EventMachine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009 - Grenoble, France
Duration: Sep 2 2009Sep 4 2009

Other

OtherMachine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009
CountryFrance
CityGrenoble
Period9/2/099/4/09

Fingerprint

Independent component analysis
Blind source separation
Signal processing
Magnetic Resonance Imaging

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing
  • Education

Cite this

Rodriguez, P. A., Correa, N. M., Adali, T., & Calhoun, V. D. (2009). Quality map thresholding for de-noising of complex-valued fMRI data and its application to ICA of fMRI. In Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009 [5306263] https://doi.org/10.1109/MLSP.2009.5306263

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 proceedingConference contribution

Rodriguez, PA, Correa, NM, Adali, T & Calhoun, VD 2009, Quality map thresholding for de-noising of complex-valued fMRI data and its application to ICA of fMRI. in Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009., 5306263, Machine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009, Grenoble, France, 9/2/09. https://doi.org/10.1109/MLSP.2009.5306263
Rodriguez PA, Correa NM, Adali T, Calhoun VD. Quality map thresholding for de-noising of complex-valued fMRI data and its application to ICA of fMRI. In Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009. 2009. 5306263 https://doi.org/10.1109/MLSP.2009.5306263
Rodriguez, Pedro A. ; Correa, Nicolle M. ; Adali, Tülay ; Calhoun, Vince Daniel. / Quality map thresholding for de-noising of complex-valued fMRI data and its application to ICA of fMRI. Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009. 2009.
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