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CLC number: TN911

On-line Access: 2015-12-07

Received: 2014-12-08

Revision Accepted: 2015-10-09

Crosschecked: 2015-10-12

Cited: 1

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Citations:  Bibtex RefMan EndNote GB/T7714


Min Yuan


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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.12 P.1069-1087


Multi-scale UDCT dictionary learning based highly undersampled MR image reconstruction using patch-based constraint splitting augmented Lagrangian shrinkage algorithm

Author(s):  Min Yuan, Bing-xin Yang, Yi-de Ma, Jiu-wen Zhang, Fu-xiang Lu, Tong-feng Zhang

Affiliation(s):  School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China

Corresponding email(s):   ydma01@126.com

Key Words:  Compressed sensing (CS), Magnetic resonance imaging (MRI), Uniform discrete curvelet transform (UDCT), Multi-scale dictionary learning (MSDL), Patch-based constraint splitting augmented Lagrangian shrinkage algorithm (PB C-SALSA)

Min Yuan, Bing-xin Yang, Yi-de Ma, Jiu-wen Zhang, Fu-xiang Lu, Tong-feng Zhang. Multi-scale UDCT dictionary learning based highly undersampled MR image reconstruction using patch-based constraint splitting augmented Lagrangian shrinkage algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(12): 1069-1087.

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author="Min Yuan, Bing-xin Yang, Yi-de Ma, Jiu-wen Zhang, Fu-xiang Lu, Tong-feng Zhang",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%T Multi-scale UDCT dictionary learning based highly undersampled MR image reconstruction using patch-based constraint splitting augmented Lagrangian shrinkage algorithm
%A Min Yuan
%A Bing-xin Yang
%A Yi-de Ma
%A Jiu-wen Zhang
%A Fu-xiang Lu
%A Tong-feng Zhang
%J Frontiers of Information Technology & Electronic Engineering
%V 16
%N 12
%P 1069-1087
%@ 2095-9184
%D 2015
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1400423

T1 - Multi-scale UDCT dictionary learning based highly undersampled MR image reconstruction using patch-based constraint splitting augmented Lagrangian shrinkage algorithm
A1 - Min Yuan
A1 - Bing-xin Yang
A1 - Yi-de Ma
A1 - Jiu-wen Zhang
A1 - Fu-xiang Lu
A1 - Tong-feng Zhang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 16
IS - 12
SP - 1069
EP - 1087
%@ 2095-9184
Y1 - 2015
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1400423

Recently, dictionary learning (DL) based methods have been introduced to compressed sensing magnetic resonance imaging (CS-MRI), which outperforms pre-defined analytic sparse priors. However, single-scale trained dictionary directly from image patches is incapable of representing image features from multi-scale, multi-directional perspective, which influences the reconstruction performance. In this paper, incorporating the superior multi-scale properties of uniform discrete curvelet transform (UDCT) with the data matching adaptability of trained dictionaries, we propose a flexible sparsity framework to allow sparser representation and prominent hierarchical essential features capture for magnetic resonance (MR) images. Multi-scale decomposition is implemented by using UDCT due to its prominent properties of lower redundancy ratio, hierarchical data structure, and ease of implementation. Each sub-dictionary of different sub-bands is trained independently to form the multi-scale dictionaries. Corresponding to this brand-new sparsity model, we modify the constraint splitting augmented Lagrangian shrinkage algorithm (C-SALSA) as patch-based C-SALSA (PB C-SALSA) to solve the constraint optimization problem of regularized image reconstruction. Experimental results demonstrate that the trained sub-dictionaries at different scales, enforcing sparsity at multiple scales, can then be efficiently used for MRI reconstruction to obtain satisfactory results with further reduced undersampling rate. Multi-scale UDCT dictionaries potentially outperform both single-scale trained dictionaries and multi-scale analytic transforms. Our proposed sparsity model achieves sparser representation for reconstructed data, which results in fast convergence of reconstruction exploiting PB C-SALSA. Simulation results demonstrate that the proposed method outperforms conventional CS-MRI methods in maintaining intrinsic properties, eliminating aliasing, reducing unexpected artifacts, and removing noise. It can achieve comparable performance of reconstruction with the state-of-the-art methods even under substantially high undersampling factors.


创新点:改进了基本的字典学习模型,提出了一种基于均匀离散Curvelet变换(Uniform Discrete Curvelet Transform, UDCT)域多尺度字典学习的稀疏化模型,并应用于CS-MRI重构。为适应多尺度分层和分块稀疏化结构,进一步扩展约束型分裂增广拉格朗日收缩方法,并用于模型的数值求解。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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