Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
562777 | Biomedical Signal Processing and Control | 2009 | 10 Pages |
Abstract
We propose a novel iterative scheme for adaptive smoothing of functional MR images. The method estimates a signal model at every voxel in the time-series, which is subsequently used in determining the weights of the smoothing kernel. The method does not require any information about the test hypothesis and is well-suited as a preprocessing step for both hypothesis-driven and data-driven analysis techniques. We demonstrate the performance of the proposed method by applying it to preprocess both simulated and real fMRI data. The method is found to effectively suppress the noise while preserving the shapes of the active brain regions.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Syed Muhammad Ghazanfar Monir, Mohammed Yakoob Siyal,