ABSTRACT
Functional MRI is an imaging technique that provides high resolution information about brain blood flow and oxygenation. This information is used to deduce which regions of the brain are activated by various stimuli. Analysis of functional MRI data is complicated by the low signal-to-noise ratios typically encountered. Two different wavelet-based noise removal algorithms are investigated for potential utility in functional MRI analysis.
