The benefit of tree sparsity in accelerated MRI

Chen, Chen; Huang, Junzhou

doi:10.1016/j.media.2013.12.004

Cited by 51 publications

(32 citation statements)

References 30 publications

(23 reference statements)

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“…FCSA [6] decomposed this problem into two simpler problems and then accelerated them with FISTA [18,19] respectively. The results can be further improved by exploiting the complex structure of wavelet coefficients [21,22]. These are current state-ofthe-art algorithms for solving the CS-MRI problem (1).…”

Section: Compressive Sensing Mrimentioning

confidence: 99%

Fast multi-contrast MRI reconstruction

Huang

Chen

Axel

2014

Magnetic Resonance Imaging

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120

166

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Multi-contrast magnetic resonance imaging (MRI) is a useful technique to aid clinical diagnosis. This paper proposes an efficient algorithm to jointly reconstruct multiple T1/T2-weighted images of the same anatomical cross section from partially sampled k-space data. The joint reconstruction problem is formulated as minimizing a linear combination of three terms, corresponding to a least squares data fitting, joint total variation (TV) and group wavelet-sparsity regularization. It is rooted in two observations: 1) the variance of image gradients should be similar for the same spatial position across multiple contrasts; 2) the wavelet coefficients of all images from the same anatomical cross section should have similar sparse modes. To efficiently solve this problem, we decompose it into joint TV regularization and group sparsity subproblems, respectively. Finally, the reconstructed image is obtained from the weighted average of solutions from the two subproblems, in an iterative framework. Experiments demonstrate the efficiency and effectiveness of the proposed method compared to existing multi-contrast MRI methods.

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Section: Compressive Sensing Mrimentioning

confidence: 99%

Fast multi-contrast MRI reconstruction

Huang

Chen

Axel

2014

Magnetic Resonance Imaging

Self Cite

120

166

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“…More important, JLISD makes use of the joint sparsity property of the wavelet frame coefficients [63], [64] to further improve the recovery quality. Therefore future research includes studying specific image classes (including color images) and developing more effective corresponding support detection methods.…”

Section: Discussionmentioning

confidence: 99%

Iterative Support Detection-Based Split Bregman Method for Wavelet Frame-Based Image Inpainting

Wang

2014

IEEE Trans. on Image Process.

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The wavelet frame systems have been extensively studied due to their capability of sparsely approximating piece-wise smooth functions, such as images, and the corresponding wavelet frame-based image restoration models are mostly based on the penalization of the l1 norm of wavelet frame coefficients for sparsity enforcement. In this paper, we focus on the image inpainting problem based on the wavelet frame, propose a weighted sparse restoration model, and develop a corresponding efficient algorithm. The new algorithm combines the idea of iterative support detection method, first proposed by Wang and Yin for sparse signal reconstruction, and the split Bregman method for wavelet frame l1 model of image inpainting, and more important, naturally makes use of the specific multilevel structure of the wavelet frame coefficients to enhance the recovery quality. This new algorithm can be considered as the incorporation of prior structural information of the wavelet frame coefficients into the traditional l1 model. Our numerical experiments show that the proposed method is superior to the original split Bregman method for wavelet frame-based l1 norm image inpainting model as well as some typical l(p) (0 ≤ p < 1) norm-based nonconvex algorithms such as mean doubly augmented Lagrangian method, in terms of better preservation of sharp edges, due to their failing to make use of the structure of the wavelet frame coefficients.

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“…And special irregular sampling pattern and specialized reconstructed algorithm may contribute to enhance the spatial resolution and temporal resolution of the results. Recently study showed that tree sparsity is beneficial to accelerate MRI [22]. The reconstructed algorithms for MR images play essential role in the practical application of CS to MRI.…”

Section: Fista Algorithm For Mr Image Reconstructionmentioning

confidence: 99%

Investigating the stability of fast iterative shrinkage thresholding algorithm for MR imaging reconstruction using compressed sensing

Zhang

Hai-tao

Chen

et al. 2015

2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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Compressed sensing (CS) has been applied to the field of sub-sampled magnetic resonance imaging (MRI) reconstruction (CS-MRI). Fast iterative shrinkage-thresholding algorithm (FISTA) is an effective method for CS-MR images reconstruction. To investigate the accuracy and efficiency of the proposed algorithm, we applied it to the under-sampling MR images gained by different MRI scanning sequences. We found the peak signal to noise ratio (PSNR) of reconstructed MRI with varying sampling ratios diminished from Axial T1 weighted images (Ax T1) (45.13±12.97 dB), Axial T2 weighted images (Ax T2) (42.8 ± 14.95 dB), FLAIR (41.74 ± 14.15 dB), Diffusion Weighted Imaging (DWI) (40.23 ± 17.40 dB) and Sagittal T2 weighted images (Sag T2) (36.28±12.32 dB), but there was no significant difference among the groups. In addition, the changes to the elapsed time of them was minor, Ax T1 (1.09±0.13 s), Ax T2 (1.30±0.13s), FLAIR (1.02±0.12s), DWI (1.07±0.13s) and Sag T2 (1.12±0.07s). Our results confirmed the stability of the proposed fast MRI reconstruction method for different scanning sequences. Further efforts are still needed to design the clinical sequence with sub-sampled acquisition strategy which may be a developing technique with clinical value.

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The benefit of tree sparsity in accelerated MRI

Cited by 51 publications

References 30 publications

Fast multi-contrast MRI reconstruction

Fast multi-contrast MRI reconstruction

Iterative Support Detection-Based Split Bregman Method for Wavelet Frame-Based Image Inpainting

Investigating the stability of fast iterative shrinkage thresholding algorithm for MR imaging reconstruction using compressed sensing

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