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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Applying compressed sensing (CS) to magnetic resonance imaging (MRI) makes it possible to reconstruct a MR image from undersampled data. Traditional CS based MR image reconstruction schemes only use the signals' sparsity in an appropriate transform domain to reduce sampling rate. This paper proposes a new MR image reconstruction method which utilizes structure features of the image besides sparsity. The proposed method exploits the distribution of the wavelet coefficients' magnitudes and support information to formulate the objective function by minimizing which the image is reconstructed. The objective function consists of the data consistence term and two regularization terms: l1-l2 norm of the groups which contains the coefficients with the unknown support and total variation (TV) norm of the image. The simulation results from real MR images show that the proposed method outperforms the conventional CS based MR image reconstruction method.

Reference-Driven Compressed Sensing MR Image Reconstruction with Partially Known Support and Group Sparsity Constraints