Undersampled MRI reconstruction with patch-based directional wavelets

Qu, Xiaobo; Guo, Di; Ning, Bende; Hou, Yingkun; Lin, Yulan; Cai, Shuhui; Chen, Zhong

doi:10.1016/j.mri.2012.02.019

Cited by 229 publications

(165 citation statements)

References 26 publications

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“…Without shift-invariance property, the signal restored using the orthogonal discrete wavelet transform will exhibit much more artifacts in denoising [11,12]. Within the field of MRI, some researchers have utilized SIDWT to reconstruct MR images and found it superior than its orthogonal counterpart in noise suppression and artifacts reduction [5,36,[42][43][44]. In all the experiments, Daubechies wavelets with 4 decomposition levels are utilized in SIDWT.…”

Section: A Main Resultsmentioning

confidence: 99%

“…2 (d) with 40% of k-space data being sampled. Note that in our application on 2D imaging, instead of the fully 2D randomly sampling in [4,6], the undersampling here is only along the phase encoding dimension because the frequency encoding dimension is not time-consuming and is unworthy of undersampling [1,5,38]. The i.i.d.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…This criteria was previously used in MR image reconstructions [5,36]. we have an optimal reconstruction error in terms of RLNE.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…2 (b) based on other three tight frames: contourlet [4,45], patch based directional wavelets (PBDW) [5] and framelet [46][47][48]. Both contourlet and PBDW explore the geometric information to further sparsify MR images thus are good at preserving the edge of MR images [4,5,45]. The filters of contourlet, PBDW and framelet are ladder structure filters, Haar wavelets and Haar wavelets, respectively.…”

Section: ) Experiments On Other Tight Framesmentioning

confidence: 99%

See 3 more Smart Citations

Projected Iterative Soft-Thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging

Liu

Zhan

Cai

et al. 2016

IEEE Trans. Med. Imaging

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163

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Abstract-Compressed sensing has shown great potentials in accelerating magnetic resonance imaging. Fast image reconstruction and high image quality are two main issues faced by this new technology. It has been shown that, redundant image representations, e.g. tight frames, can significantly improve the image quality. But how to efficiently solve the reconstruction problem with these redundant representation systems is still challenging. This paper attempts to address the problem of applying iterative soft-thresholding algorithm (ISTA) to tight frames based magnetic resonance image reconstruction. By introducing the canonical dual frame to construct the orthogonal projection operator on the range of the analysis sparsity operator, we propose a projected iterative soft-thresholding algorithm (pISTA) and further accelerate it by incorporating the strategy proposed by Beck and Teboulle in 2009. We theoretically prove that pISTA converges to the minimum of a function with a balanced tight frame sparsity. Experimental results demonstrate that the proposed algorithm achieves better reconstruction than the widely used synthesis sparse model and the accelerated pISTA converges faster or comparable to the state-of-art smoothing FISTA. One major advantage of pISTA is that only one extra parameter, the step size, is introduced and the numerical solution is stable to it in terms of image reconstruction errors, thus allowing easily setting in many fast magnetic resonance imaging applications.

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Section: A Main Resultsmentioning

confidence: 99%

Section: Numerical Experimentsmentioning

confidence: 99%

“…This criteria was previously used in MR image reconstructions [5,36]. we have an optimal reconstruction error in terms of RLNE.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Section: ) Experiments On Other Tight Framesmentioning

confidence: 99%

See 2 more Smart Citations

Projected Iterative Soft-Thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging

Liu

Zhan

Cai

et al. 2016

IEEE Trans. Med. Imaging

Self Cite

163

View full text Add to dashboard Cite

show abstract

“…Among these approaches, adaptive dictionary learning usually outperforms analytical dictionary approaches in denoising, super-resolution reconstruction, interpolation, inpainting, classification and other applications, since the adaptively learned dictionary suits the signals of interest [13]- [15]. Dictionary learning has been applied to CS-MRI as a sparse basis for reconstruction (e.g., LOST [9] and DLMRI [10]). Results using this approach demonstrate a significant improvement when compared with previous CS-MRI methods.…”

Section: Introductionmentioning

confidence: 99%

Compressed sensing MRI with Bayesian dictionary learning

Ding

Paisley

Huang

et al. 2013

2013 IEEE International Conference on Image Processing

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We present an inversion algorithm for magnetic resonance images (MRI) that are highly undersampled in k-space. The proposed method incorporates spatial finite differences (total variation) and patch-wise sparsity through in situ dictionary learning. We use the beta-Bernoulli process as a Bayesian prior for dictionary learning, which adaptively infers the dictionary size, the sparsity of each patch and the noise parameters. In addition, we employ an efficient numerical algorithm based on the alternating direction method of multipliers (ADMM). We present empirical results on two MR images.

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An edge guided cascaded U‐net approach for accelerated magnetic resonance imaging reconstruction

Dhengre

Sinha

2021

Int J Imaging Syst Tech

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Magnetic resonance imaging, despite of its significant role in today's healthcare, suffers from long image acquisition time which leads to patient discomfort and cost increment. Compressive sensing magnetic resonance imaging, where clinically acceptable images are reconstructed using partially sampled k-space data, is one possible approach to mitigate this problem. The recent evolution in compressive sensing magnetic resonance imaging field is the model based deep learning approach, which is comprised of cascaded convolutional neural network based denoizer and data consistency layer. In this paper, we propose an edge guided model based deep learning approach employing U-net module as an artifact removal unit. The proposed model contains cascaded U-net architectures with interleaved data consistency layer. To effectively retain the fine details in the reconstructed output, along with the image, edge maps of the image were also

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Undersampled MRI reconstruction with patch-based directional wavelets

Cited by 229 publications

References 26 publications

Projected Iterative Soft-Thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging

Projected Iterative Soft-Thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging

Compressed sensing MRI with Bayesian dictionary learning

An edge guided cascaded U‐net approach for accelerated magnetic resonance imaging reconstruction

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