title: Wavelet transform-based Wiener filtering of event-related fMRI data.
abstract: The advent of event-related functional magnetic resonance imaging (fMRI) has resulted in many exciting studies that have exploited its unique capability. However, the utility of event-related fMRI is still limited by several technical difficulties. One significant limitation in event-related fMRI is the low signal-to-noise ratio (SNR). In this work, a method based on Wiener filtering in the wavelet domain is developed and demonstrated for denoising event-related fMRI data. Application of the technique to simulated and experimental data demonstrates that the technique is effective in reducing noise while preserving neuronal activity-induced response.
authors.affiliation: University of Minnesota, Center for Magnetic Resonance Research, Minneapolis, Minnesota 55455, USA.
authors.forename: S M
authors.initials: SM
authors.lastname: LaConte
authors.forename: S C
authors.initials: SC
authors.lastname: Ngan
authors.forename: X
authors.initials: X
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008962:Models, Theoretical