Iterative adaptive filtering for random noise reduction in functional MRI time-series

This paper presents a novel method for adaptive filtering of functional magnetic resonance imaging (fMRI) time-series. The method progressively reduces noise from the fMRI time courses based on selective spatial averaging of the underlying voxels. A new similarity measure is proposed to assign the weights of the averaging kernel. The performance of the proposed method is verified by its application on synthetic as well as real fMRI data. The results show that pre-processing the data with the proposed method results in an increased sensitivity along with an excellent specificity of fMRI analysis.