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

Parallel MRI (pMRI) and compressed sensing MRI (CS-MRI) have been consideredas two distinct reconstruction problems. Inspired by recent k-spaceinterpolation methods, an annihilating filter based low-rank Hankel matrixapproach (ALOHA) is proposed as a general framework for sparsity-driven k-spaceinterpolation method which unifies pMRI and CS-MRI. Specifically, our frameworkis based on the fundamental duality between the transform domain sparsity inthe primary space and the low-rankness of weighted Hankel matrix in thereciprocal space, which converts pMRI and CS-MRI to a k-space interpolationproblem using structured matrix completion. Using theoretical results from thelatest compressed sensing literatures, we showed that the required samplingrates for ALOHA may achieve the optimal rate. Experimental results with in vivodata for single/multi-coil imaging as well as dynamic imaging confirmed thatthe proposed method outperforms the state-of-the-art pMRI and CS-MRI.

A general framework for compressed sensing and parallel MRI using annihilating filter based low-rank Hankel matrix