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Local sparsity enhanced compressed sensing magnetic resonance imaging in uniform discrete curvelet domain

Overview of attention for article published in BMC Medical Imaging, August 2015
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Title
Local sparsity enhanced compressed sensing magnetic resonance imaging in uniform discrete curvelet domain
Published in
BMC Medical Imaging, August 2015
DOI 10.1186/s12880-015-0065-0
Pubmed ID
Authors

Bingxin Yang, Min Yuan, Yide Ma, Jiuwen Zhang, Kun Zhan

Abstract

Compressed sensing(CS) has been well applied to speed up imaging by exploring image sparsity over predefined basis functions or learnt dictionary. Firstly, the sparse representation is generally obtained in a single transform domain by using wavelet-like methods, which cannot produce optimal sparsity considering sparsity, data adaptivity and computational complexity. Secondly, most state-of-the-art reconstruction models seldom consider composite regularization upon the various structural features of images and transform coefficients sub-bands. Therefore, these two points lead to high sampling rates for reconstructing high-quality images. In this paper, an efficient composite sparsity structure is proposed. It learns adaptive dictionary from lowpass uniform discrete curvelet transform sub-band coefficients patches. Consistent with the sparsity structure, a novel composite regularization reconstruction model is developed to improve reconstruction results from highly undersampled k-space data. It is established via minimizing spatial image and lowpass sub-band coefficients total variation regularization, transform sub-bands coefficients l 1 sparse regularization and constraining k-space measurements fidelity. A new augmented Lagrangian method is then introduced to optimize the reconstruction model. It updates representation coefficients of lowpass sub-band coefficients over dictionary, transform sub-bands coefficients and k-space measurements upon the ideas of constrained split augmented Lagrangian shrinkage algorithm. Experimental results on in vivo data show that the proposed method obtains high-quality reconstructed images. The reconstructed images exhibit the least aliasing artifacts and reconstruction error among current CS MRI methods. The proposed sparsity structure can fit and provide hierarchical sparsity for magnetic resonance images simultaneously, bridging the gap between predefined sparse representation methods and explicit dictionary. The new augmented Lagrangian method provides solutions fully complying to the composite regularization reconstruction model with fast convergence speed.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 29%
Other 1 7%
Professor 1 7%
Student > Bachelor 1 7%
Student > Master 1 7%
Other 3 21%
Unknown 3 21%
Readers by discipline Count As %
Engineering 4 29%
Neuroscience 2 14%
Agricultural and Biological Sciences 1 7%
Computer Science 1 7%
Mathematics 1 7%
Other 1 7%
Unknown 4 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 August 2015.
All research outputs
#12,610,430
of 14,268,069 outputs
Outputs from BMC Medical Imaging
#256
of 332 outputs
Outputs of similar age
#194,316
of 235,604 outputs
Outputs of similar age from BMC Medical Imaging
#1
of 1 outputs
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