Iterative adaptive spatial filtering for noise‐suppression in functional magnetic resonance imaging time‐series

We present an iterative scheme for adaptive smoothing of functional magnetic resonance images. We propose a novel similarity measure to estimate the weights of the smoothing filter based on the functional similarity of the voxels under the smoothing kernel with the voxel under consideration as well as their similarity with a reference time‐course representing the expected BOLD response. We demonstrate the performance of the proposed method by applying the method to preprocess both simulated and real fMRI data. The method improves the functional SNR of the data while preserving the shapes of the functionally active region and its performance is not compromised when structured noise is the dominant noise source. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 260‐270, 2011;