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Gibbs‐ringing artifact removal based on local subvoxel‐shifts
Author(s) -
Kellner Elias,
Dhital Bibek,
Kiselev Valerij G.,
Reisert Marco
Publication year - 2016
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.26054
Subject(s) - ringing artifacts , gibbs phenomenon , ringing , sinc function , artifact (error) , computer science , image processing , artificial intelligence , spurious relationship , robustness (evolution) , algorithm , smoothing , initialization , computer vision , mathematics , image (mathematics) , filter (signal processing) , mathematical analysis , fourier transform , biochemistry , chemistry , gene , programming language , machine learning
Purpose To develop a fast and stable method for correcting the gibbs‐ringing artifact. Methods Gibbs‐ringing is a well‐known artifact which manifests itself as spurious oscillations in the vicinity of sharp image gradients at tissue boundaries. The origin can be seen in the truncation of k‐space during MRI data‐acquisition. 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‐phenomenon: The truncation in k‐space can be interpreted as a convolution of the underlying image with a sinc‐function. As the image is reconstructed on a discretized grid, the severity of the ringing artifacts depends on how this grid is located with respect to the edge and the oscillation pattern of the function. We propose to reinterpolate the image based on local, subvoxel‐shifts to sample the ringing pattern at the zero‐crossings of the oscillating sinc‐function. Results With the proposed method, the artifact can simply, effectively, and robustly be removed with a minimal amount of image smoothing. Conclusions The robustness of the method suggests it as a suitable candidate for an implementation in the standard image processing pipeline in clinical routine. Magn Reson Med 76:1574–1581, 2016. © 2015 International Society for Magnetic Resonance in Medicine