English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The k-space data generated from magnetic resonance imaging (MRI) is only afinite sampling of underlying signals. Therefore, MRI images often suffer fromlow spatial resolution and Gibbs ringing artifacts. Previous studies tackledthese two problems separately, where super resolution methods tend to enhanceGibbs artifacts, whereas Gibbs ringing removal methods tend to blur the images.It is also a challenge that high resolution ground truth is hard to obtain inclinical MRI. In this paper, we propose an unsupervised learning framework forboth MRI super resolution and Gibbs artifacts removal without using highresolution ground truth. Furthermore, we propose regularization methods toimprove the model's generalizability across out-of-distribution MRI images. Weevaluated our proposed methods with other state-of-the-art methods on eight MRIdatasets with various contrasts and anatomical structures. Our method not onlyachieves the best SR performance but also significantly reduces the Gibbsartifacts. Our method also demonstrates good generalizability across differentdatasets, which is beneficial to clinical applications where training data areusually scarce and biased.

An Unsupervised Framework for Joint MRI Super Resolution and Gibbs Artifact Removal