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

Cardiac magnetic resonance imaging (CMR) has been widely used in clinicalpractice for the medical diagnosis of cardiac diseases. However, the longacquisition time hinders its development in real-time applications. Here, wepropose a novel self-consistency guided multi-prior learning framework named$k$-$t$ CLAIR to exploit spatiotemporal correlations from highly undersampleddata for accelerated dynamic parallel MRI reconstruction. The $k$-$t$ CLAIRprogressively reconstructs faithful images by leveraging multiple complementarypriors learned in the $x$-$t$, $x$-$f$, and $k$-$t$ domains in an iterativefashion, as dynamic MRI exhibits high spatiotemporal redundancy. Additionally,$k$-$t$ CLAIR incorporates calibration information for prior learning,resulting in a more consistent reconstruction. Experimental results on cardiaccine and T1W/T2W images demonstrate that $k$-$t$ CLAIR achieves high-qualitydynamic MR reconstruction in terms of both quantitative and qualitativeperformance.

$k$-$t$ CLAIR: Self-Consistency Guided Multi-Prior Learning for Dynamic Parallel MR Image Reconstruction