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 D. 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 - Dec 1 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

Publication series

NameMachine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009

Other

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

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing
  • Education

Fingerprint Dive into the research topics of 'Quality map thresholding for de-noising of complex-valued fMRI data and its application to ICA of fMRI'. Together they form a unique fingerprint.

Cite this