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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Most existing MRI reconstruction methods perform tar-geted reconstruction ofthe entire MR image without tak-ing specific tissue regions into consideration.This may fail to emphasize the reconstruction accuracy on im-portant tissuesfor diagnosis. In this study, leveraging a combination of the properties ofk-space data and the diffusion process, our novel scheme focuses on mining themulti-frequency prior with different strategies to pre-serve fine texturedetails in the reconstructed image. In addition, a diffusion process canconverge more quickly if its target distribution closely resembles the noisedistri-bution in the process. This can be accomplished through varioushigh-frequency prior extractors. The finding further solidifies theeffectiveness of the score-based gen-erative model. On top of all theadvantages, our method improves the accuracy of MRI reconstruction andaccel-erates sampling process. Experimental results verify that the proposedmethod successfully obtains more accurate reconstruction and outperformsstate-of-the-art methods.

Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstruction