Spatial filtering is an important step in the preprocessing of task-based fMRI to improve sensitivity in statistical analyses. This is usually implemented as a pure distance-based filter such as Gaussian filtering or an optimized matched filter. Adaptive non-local means (ANLM) filtering is a patch-based approach that is sensitive to the local noise model, especially at low signal to noise ratio such as fMRI. We show how ANLM filtering is a simple drop-in replacement at the spatial smoothing step of fMRI preprocessing pipeline that compares favorably to other approaches while better preserving local high frequency features.
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