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

The main focus of this work is a novel framework for the joint reconstructionand segmentation of parallel MRI (PMRI) brain data. We introduce an imagedomain deep network for calibrationless recovery of undersampled PMRI data. Theproposed approach is the deep-learning (DL) based generalization of locallow-rank based approaches for uncalibrated PMRI recovery including CLEAR [6].Since the image domain approach exploits additional annihilation relationscompared to k-space based approaches, we expect it to offer improvedperformance. To minimize segmentation errors resulting from undersamplingartifacts, we combined the proposed scheme with a segmentation network andtrained it in an end-to-end fashion. In addition to reducing segmentationerrors, this approach also offers improved reconstruction performance byreducing overfitting; the reconstructed images exhibit reduced blurring andsharper edges than independently trained reconstruction network.

Reconstruction and Segmentation of Parallel MR Data using Image Domain DEEP-SLR