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The low signal to noise ratio (SNR) of functional magnetic resonance imaging (fMRI) data necessitates the use of efficient noise filtering techniques to denoise the data while preserving its statistical properties. We propose an adaptive spatial smoothing technique in which we perform weighted-average filtering of fMR images based on correlation of the time(More)
We present a novel method for random noise-suppression in functional magnetic resonance imaging (fMRI) time-series based on modified spectral subtraction. The method estimates the signal and noise models at every voxel in the functional data from a small neighborhood, without prior knowledge of the signal characteristics. Spectral subtraction is then(More)
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