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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Compressed Sensing (CS) significantly speeds up Magnetic Resonance Image(MRI) processing and achieves accurate MRI reconstruction from under-sampledk-space data. According to the current research, there are still severalproblems with dynamic MRI k-space reconstruction based on CS. 1) There aredifferences between the Fourier domain and the Image domain, and thedifferences between MRI processing of different domains need to be considered.2) As three-dimensional data, dynamic MRI has its spatial-temporalcharacteristics, which need to calculate the difference and consistency ofsurface textures while preserving structural integrity and uniqueness. 3)Dynamic MRI reconstruction is time-consuming and computationallyresource-dependent. In this paper, we propose a novel robust low-rank dynamicMRI reconstruction optimization model via highly under-sampled and DiscreteFourier Transform (DFT) called the Robust Depth Linear Error DecompositionModel (RDLEDM). Our method mainly includes linear decomposition, double TotalVariation (TV), and double Nuclear Norm (NN) regularizations. By adding linearimage domain error analysis, the noise is reduced after under-sampled and DFTprocessing, and the anti-interference ability of the algorithm is enhanced.Double TV and NN regularizations can utilize both spatial-temporalcharacteristics and explore the complementary relationship between differentdimensions in dynamic MRI sequences. In addition, Due to the non-smoothness andnon-convexity of TV and NN terms, it is difficult to optimize the unifiedobjective model. To address this issue, we utilize a fast algorithm by solvinga primal-dual form of the original problem. Compared with five state-of-the-artmethods, extensive experiments on dynamic MRI data demonstrate the superiorperformance of the proposed method in terms of both reconstruction accuracy andtime complexity.

Robust Depth Linear Error Decomposition with Double Total Variation and Nuclear Norm for Dynamic MRI Reconstruction