Quality Map Thresholding for de-noising of complex-valued fMRI data and its application to ICA of fMRI

Pedro A. Rodriguez, Nicolle M. Correa, Tom Eichele, Vince D. Calhoun, Tülay Adalı

Research output: Contribution to journalArticle

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 to successfully identify and eliminate noisy areas in the fMRI data so they can be used in individual and group studies. 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)
Pages (from-to)497-508
Number of pages12
JournalJournal of Signal Processing Systems
Volume65
Issue number3
DOIs
StatePublished - Dec 2011
Externally publishedYes

Keywords

  • De-noising
  • ICA
  • Phase quality maps
  • fMRI

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Signal Processing
  • Information Systems
  • Modeling and Simulation
  • Hardware and Architecture

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