Noise suppression in functional MRI data using anisotropic spatial averaging

We present an adaptive smoothing scheme for denoising functional magnetic resonance imaging (fMRI) data using weighted average filtering. A novel metric is proposed that assigns the weights of the smoothing kernel on the basis of similarity of the voxels under the smoothing kernel with the voxel under consideration as well as a reference time course. Pearson´s coefficient of correlation is used as the similarity measure. The performance of this simple yet effective smoothing scheme is tested by applying it on both synthetic and real fMRI data. The method is found to be effective in suppressing random noise while preserving the shapes of the activated regions.