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

Quantitative magnetic resonance (MR) T1\r{ho} mapping is a promising approachfor characterizing intrinsic tissue-dependent information. However, long scantime significantly hinders its widespread applications. Recently, low-ranktensor has been employed and demonstrated good performance in accelerating MRT1\r{ho} mapping. In this study, we propose a novel method that uses spatialpatch-based and parametric group-based low rank tensors simultaneously (SMART)to reconstruct images from highly undersampled k-space data. The spatialpatch-based low-rank tensor exploits the high local and nonlocal redundanciesand similarities between the contrast images in T1\r{ho} mapping. Theparametric group based low-rank tensor, which integrates similar exponentialbehavior of the image signals, is jointly used to enforce the multidimensionallow-rankness in the reconstruction process. In vivo brain datasets were used todemonstrate the validity of the proposed method. Experimental results havedemonstrated that the proposed method achieves 11.7-fold and 13.21-foldaccelerations in two-dimensional and three-dimensional acquisitions,respectively, with more accurate reconstructed images and maps than severalstate-of-the-art methods. Prospective reconstruction results furtherdemonstrate the capability of the SMART method in accelerating MR T1\r{ho}imaging.

Accelerating Magnetic Resonance T1\r{ho} Mapping Using Simultaneously Spatial Patch-based and Parametric Group-based Low-rank Tensors (SMART)