Gibbs-Ringing Artifact Removal Based on Local Subvoxel-shifts

TL;DR

This paper introduces a novel, robust method for removing Gibbs-ringing artifacts in MRI images by re-interpolating based on local subvoxel shifts to sample the sinc-function at zero-crossings, reducing artifacts effectively.

Contribution

The proposed approach offers a simple, robust alternative to existing correction techniques by exploiting the sampling properties of the sinc-function in image space.

Findings

Effective removal of Gibbs-ringing artifacts demonstrated

Minimal smoothing preserves image details

Method outperforms traditional correction techniques

Abstract

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.