  Gibbs-ringing is a well known artifact which manifests itself as spurious
oscillations in the vicinity of sharp image transients, e.g. at tissue
boundaries. The origin can be seen in the truncation of k-space during MRI
data-acquisition. Consequently, correction techniques like Gegenbauer
reconstruction or extrapolation methods aim at recovering these missing data.
Here, we present a simple and robust method which exploits a different view on
the Gibbs-phenomena. The truncation in k-space can be interpreted as a
convolution with a sinc-function in image space. Hence, the severity of the
artifacts depends on how the sinc-function is sampled. We propose to
re-interpolate the image based on local, subvoxel shifts to sample the ringing
pattern at the zero-crossings of the oscillating sinc-function. With this, the
artifact can effectively and robustly be removed with a minimal amount of
smoothing.
