Adaptive smoothing strategies to eliminate the scalp/ventricle artifact in statistical parametric mapping

In positron emission tomographic (PET) or magnetic resonance imaging (MRI) neuroimaging studies, spatial smoothing technique is commonly applied to increase the signal to noise ratio and to condition neuroimaging data for subsequent statistical analysis, and to reduce errors associated with registration or spatial normalizations. Usually, the smoothing step is applied to the images without any masking. Thus, some artifacts adjacent but outside of the brain will enter the brain volume. Masking the brain volume before smoothing has been suggested as one way to eliminate the introduced artifact, but it will introduce zero-in (intensities within-mask voxels are reduced) and nonzero-out (intensities outside the mask become non-zero) artifacts. Here we proposed an adaptive smoothing method to reduce the influence of such artifacts. Unlike the conventional smoothing method, the adaptive strategy did not introduce artificial addition (due to nonzero-out artifact) and deletion (due to zero-in), suggesting that such adaptive smoothing methods may be helpful in reducing the influence of non-brain tissue in the analysis of neuroimaging data.