Hybrid Method for Gibbs-Ringing Artifact Suppression in Magnetic Resonance Images

Suppression of ringing artifacts in images is a well-known image restoration problem. Gibbs-ringing artifacts occur when, in the process of magnetic resonance imaging, the source data from the frequency domain are mapped onto the spatial domain by using the discrete Fourier transform. The artifacts...

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Bibliographic Details
Published in:Programming and computer software 2021-05, Vol.47 (3), p.207-214
Main Authors: Penkin, M. A., Krylov, A. S., Khvostikov, A. V.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computer Science
Deep learning
Fourier transforms
Image restoration
Machine learning
Magnetic resonance imaging
Neural networks
Operating Systems
Software Engineering
Software Engineering/Programming and Operating Systems
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Summary:Suppression of ringing artifacts in images is a well-known image restoration problem. Gibbs-ringing artifacts occur when, in the process of magnetic resonance imaging, the source data from the frequency domain are mapped onto the spatial domain by using the discrete Fourier transform. The artifacts are caused by the incompleteness of these data, which, in turn, is due to cutting off the high frequencies of the Fourier spectrum. In this paper, we propose a hybrid method for Gibbs-ringing artifact suppression in magnetic resonance images that combines a deep learning model and a classical non-machine-learning algorithm for Gibbs-ringing artifact suppression based on optimal subvoxel shifts.
ISSN:0361-7688
1608-3261
DOI:10.1134/S0361768821030087