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

Deep learning approaches to accelerated MRI take a matrix of sampledFourier-space lines as input and produce a spatial image as output. In thiswork we show that by careful choice of the offset used in the samplingprocedure, the symmetries in k-space can be better exploited, producing higherquality reconstructions than given by standard equally-spaced samples orrandomized samples motivated by compressed sensing.

Offset Sampling Improves Deep Learning based Accelerated MRI Reconstructions by Exploiting Symmetry