scholarly journals Undersampled MR Image Reconstruction with Data-Driven Tight Frame

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Jianbo Liu ◽  
Shanshan Wang ◽  
Xi Peng ◽  
Dong Liang
Keyword(s):  
Image Reconstruction ◽  
Tight Frame ◽  
Data Driven ◽  
Iteration Algorithm ◽  
Mr Image ◽  
Magnetic Image ◽  
Structure Information ◽  
Reconstruction Methods ◽  
Proposed Model ◽  
Efficiency And Effectiveness

Undersampled magnetic resonance image reconstruction employing sparsity regularization has fascinated many researchers in recent years under the support of compressed sensing theory. Nevertheless, most existing sparsity-regularized reconstruction methods either lack adaptability to capture the structure information or suffer from high computational load. With the aim of further improving image reconstruction accuracy without introducing too much computation, this paper proposes a data-driven tight frame magnetic image reconstruction (DDTF-MRI) method. By taking advantage of the efficiency and effectiveness of data-driven tight frame, DDTF-MRI trains an adaptive tight frame to sparsify the to-be-reconstructed MR image. Furthermore, a two-level Bregman iteration algorithm has been developed to solve the proposed model. The proposed method has been compared to two state-of-the-art methods on four datasets and encouraging performances have been achieved by DDTF-MRI.

scholarly journals CT Image Reconstruction via Nonlocal Low-Rank Regularization and Data-Driven Tight Frame

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1873
Author(s):  
Yanfeng Shen ◽  
Shuli Sun ◽  
Fengsheng Xu ◽  
Yanqin Liu ◽  
Xiuling Yin ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Image Inpainting ◽  
Tight Frame ◽  
Data Driven ◽  
Low Rank ◽  
Superior Performance ◽  
Ct Image ◽  
Low Rank Approximation ◽  
Proposed Model ◽  
Ct Image Reconstruction

X-ray computed tomography (CT) is widely used in medical applications, where many efforts have been made for decades to eliminate artifacts caused by incomplete projection. In this paper, we propose a new CT image reconstruction model based on nonlocal low-rank regularity and data-driven tight frame (NLR-DDTF). Unlike the Spatial-Radon domain data-driven tight frame regularization, the proposed NLR-DDTF model uses an asymmetric treatment for image reconstruction and Radon domain inpainting, which combines the nonlocal low-rank approximation method for spatial domain CT image reconstruction and data-driven tight frame-based regularization for Radon domain image inpainting. An alternative direction minimization algorithm is designed to solve the proposed model. Several numerical experiments and comparisons are provided to illustrate the superior performance of the NLR-DDTF method.

scholarly journals CT Image Reconstruction from Sparse Projections Using Adaptive TpV Regularization

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hongliang Qi ◽  
Zijia Chen ◽  
Linghong Zhou
Keyword(s):  
Image Reconstruction ◽  
Ct Image ◽  
Minimization Method ◽  
X Ray ◽  
Structure Information ◽  
Reconstruction Methods ◽  
Projection Image ◽  
Sparse Projection ◽  
Ct Image Quality ◽  
Better Than

Radiation dose reduction without losing CT image quality has been an increasing concern. Reducing the number of X-ray projections to reconstruct CT images, which is also called sparse-projection reconstruction, can potentially avoid excessive dose delivered to patients in CT examination. To overcome the disadvantages of total variation (TV) minimization method, in this work we introduce a novel adaptive TpV regularization into sparse-projection image reconstruction and use FISTA technique to accelerate iterative convergence. The numerical experiments demonstrate that the proposed method suppresses noise and artifacts more efficiently, and preserves structure information better than other existing reconstruction methods.

scholarly journals Approximate Sparsity and Nonlocal Total Variation Based Compressive MR Image Reconstruction

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Chengzhi Deng ◽  
Shengqian Wang ◽  
Wei Tian ◽  
Zhaoming Wu ◽  
Saifeng Hu
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Optimization Problems ◽  
Reconstruction Method ◽  
Mr Image ◽  
Scanning Time ◽  
Reconstruction Methods ◽  
Nonlocal Total Variation ◽  
Space Data ◽  
Mr Image Reconstruction

Recent developments in compressive sensing (CS) show that it is possible to accurately reconstruct the magnetic resonance (MR) image from undersampledk-space data by solving nonsmooth convex optimization problems, which therefore significantly reduce the scanning time. In this paper, we propose a new MR image reconstruction method based on a compound regularization model associated with the nonlocal total variation (NLTV) and the wavelet approximate sparsity. Nonlocal total variation can restore periodic textures and local geometric information better than total variation. The wavelet approximate sparsity achieves more accurate sparse reconstruction than fixed waveletl0andl1norm. Furthermore, a variable splitting and augmented Lagrangian algorithm is presented to solve the proposed minimization problem. Experimental results on MR image reconstruction demonstrate that the proposed method outperforms many existing MR image reconstruction methods both in quantitative and in visual quality assessment.

scholarly journals A Novel Model and ADMM Algorithm for MR Image Reconstruction

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Bo Zhou ◽  
Yu-Fei Yang ◽  
Wei-Si Xie
Keyword(s):  
Image Reconstruction ◽  
Coupling Model ◽  
Step Size ◽  
Split Bregman ◽  
Mr Image ◽  
Minimization Method ◽  
Selection Scheme ◽  
Proposed Model ◽  
Alternating Minimization Method ◽  
Mr Image Reconstruction

Motivated by the ideas from the LOT model and its deformations, we propose a coupling model for the MR image reconstruction and apply the split Bregman iterative method on the proposed model by utilizing the augmented Lagrangian technique. The related minimization problem is then divided into four subproblems by means of the alternating minimization method. And on this basis, by combining the Barzilai-Borwein step size selection scheme, generalized shrinkage formulas, and the shrink operator, we propose an ADMM type algorithm to solve the proposed model. Several numerical examples are implemented; the experimental results demonstrate the feasibility and effectiveness of the proposed model and algorithm.

An Algorithm for the Proximity Operator in Hybrid TV-Wavelet Regularization, with Application to MR Image Reconstruction

2014 ◽  
Vol 4 (1) ◽  
pp. 21-34 ◽  
Author(s):  
Yu-Wen Fang ◽  
Xiao-Mei Huo ◽  
You-Wei Wen
Keyword(s):  
Image Reconstruction ◽  
Optimization Procedure ◽  
Mr Image ◽  
Ringing Artifacts ◽  
Tv Regularization ◽  
Efficiency And Effectiveness ◽  
Wavelet Regularization ◽  
Space Data ◽  
Numerical Difficulty ◽  
Mr Image Reconstruction

AbstractTotal variation (TV) and waveletL1norms have often been used as regularization terms in image restoration and reconstruction problems. However, TV regularization can introduce staircase effects and wavelet regularization some ringing artifacts, but hybrid TV and wavelet regularization can reduce or remove these drawbacks in the reconstructed images. We need to compute the proximal operator of hybrid regularizations, which is generally a sub-problem in the optimization procedure. Both TV and waveletL1regularisers are nonlinear and non-smooth, causing numerical difficulty. We propose a dual iterative approach to solve the minimization problem for hybrid regularizations and also prove the convergence of our proposed method, which we then apply to the problem of MR image reconstruction from highly random under-sampled k-space data. Numerical results show the efficiency and effectiveness of this proposed method.

scholarly journals POCS-Augmented CycleGAN for MR Image Reconstruction

2021 ◽  
Vol 12 (1) ◽  
pp. 114
Author(s):  
Yiran Li ◽  
Hanlu Yang ◽  
Danfeng Xie ◽  
David Dreizin ◽  
Fuqing Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Image Reconstruction ◽  
State Of The Art ◽  
Training Data ◽  
Reconstruction Method ◽  
Mr Image ◽  
Reconstruction Process ◽  
Reconstruction Methods ◽  
Mr Image Reconstruction

Recent years have seen increased research interest in replacing the computationally intensive Magnetic resonance (MR) image reconstruction process with deep neural networks. We claim in this paper that the traditional image reconstruction methods and deep learning (DL) are mutually complementary and can be combined to achieve better image reconstruction quality. To test this hypothesis, a hybrid DL image reconstruction method was proposed by combining a state-of-the-art deep learning network, namely a generative adversarial network with cycle loss (CycleGAN), with a traditional data reconstruction algorithm: Projection Onto Convex Set (POCS). The output of the first iteration’s training results of the CycleGAN was updated by POCS and used as the extra training data for the second training iteration of the CycleGAN. The method was validated using sub-sampled Magnetic resonance imaging data. Compared with other state-of-the-art, DL-based methods (e.g., U-Net, GAN, and RefineGAN) and a traditional method (compressed sensing), our method showed the best reconstruction results.

Zero-filling Technique in Fresnel Transfrom Image Reconstruction for MR Image Denoising

PIERS Online ◽  
2005 ◽  
Vol 1 (4) ◽  
pp. 473-477
Author(s):  
Bin-Rong Wu ◽  
Satoshi Ito ◽  
Yoshitsugu Kamimura ◽  
Yoshifumi Yamada
Keyword(s):  
Image Reconstruction ◽  
Image Denoising ◽  
Mr Image
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