Gibbs-Ringing Artifact Removal Based on Local Subvoxel-shifts
This addresses artifact reduction in MRI imaging, which is incremental as it builds on existing interpretations of the Gibbs phenomenon.
The paper tackles Gibbs-ringing artifacts in MRI by re-interpolating images based on local subvoxel shifts to sample at zero-crossings of the sinc-function, effectively removing artifacts with minimal smoothing.
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.