scholarly journals Median Filter Based Compressed Sensing Model with Application to MR Image Reconstruction

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yunyun Yang ◽  
Xuxu Qin ◽  
Boying Wu
Keyword(s):  
Magnetic Resonance ◽  
Image Reconstruction ◽  
Compressed Sensing ◽  
Total Variation ◽  
Median Filter ◽  
Mr Images ◽  
Mr Image ◽  
Reconstruction Process ◽  
Sensing Model ◽  
Mr Image Reconstruction

Magnetic resonance imaging (MRI) has become a helpful technique and developed rapidly in clinical medicine and diagnosis. Magnetic resonance (MR) images can display more clearly soft tissue structures and are important for doctors to diagnose diseases. However, the long acquisition and transformation time of MR images may limit their application in clinical diagnosis. Compressed sensing methods have been widely used in faithfully reconstructing MR images and greatly shorten the scanning and transforming time. In this paper we present a compressed sensing model based on median filter for MR image reconstruction. By combining a total variation term, a median filter term, and a data fitting term together, we first propose a minimization problem for image reconstruction. The median filter term makes our method eliminate additional noise from the reconstruction process and obtain much clearer reconstruction results. One key point of the proposed method lies in the fact that both the total variation term and the median filter term are presented in the L1 norm formulation. We then apply the split Bregman technique for fast minimization and give an efficient algorithm. Finally, we apply our method to numbers of MR images and compare it with a related method. Reconstruction results and comparisons demonstrate the accuracy and efficiency of the proposed model.

scholarly journals Compressed Sensing Based Dynamic MR Image Reconstruction By Using 3D-Total Generalized Variation and Tensor Decomposition: k-t TGV-TD

2021 ◽  
Author(s):  
Jucheng Zhang ◽  
Lulu Han ◽  
Jianzhong Sun ◽  
Zhikang Wang ◽  
Wenlong Xu ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Image Reconstruction ◽  
Compressed Sensing ◽  
Sparse Representation ◽  
Tensor Decomposition ◽  
Low Rank ◽  
Reconstruction Accuracy ◽  
Mr Image ◽  
Cardiac Mr ◽  
Mr Image Reconstruction

Abstract Purpose: Compressed Sensing Magnetic Resonance Imaging (CS-MRI) is a promising technique to accelerate dynamic cardiac MR imaging (DCMRI). For DCMRI, the CS-MRI usually exploits image signal sparsity and low-rank property to reconstruct dynamic images from the undersampled k-space data. In this paper, a novel CS algorithm is investigated to improve dynamic cardiac MR image reconstruction quality under the condition of minimizing the k-space recording.Methods: The sparse representation of 3D cardiac magnetic resonance data is implemented by synergistically integrating 3D TGV algorithm and high order singular value decomposition (HOSVD) based Tensor Decomposition, termed as k-t TGV-TD method. In the proposed method, the low rank structure of the 3D dynamic cardiac MR data is performed by the HOSVD method, and the localized image sparsity is achieved by the 3D TGV method. Moreover, the Fast Composite Splitting Algorithm (FCSA) method, combining the variable splitting with operator splitting techniques, is employed to solve the low-rank and sparse problem. Two different cardiac MR datasets (cardiac cine and cardiac perfusion MR data) are used to evaluate the performance of the proposed method.Results: Compared with the state-of-art methods, such as the k-t SLR method, 3D TGV method and HOSVD based tensor decomposition method, the proposed method can offer improved reconstruction accuracy in terms of higher signal-to-error ratio (SER).Conclusions: This work proved that the k-t TGV-TD method was an effective sparse representation way for DC-MRI, which was capable of significantly improving the reconstruction accuracy with different reduction factor.

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.

Wavelet encoded MR image reconstruction with compressed sensing

2011 ◽  
Author(s):  
Zheng Liu ◽  
Brian Nutter ◽  
Jingqi Ao ◽  
Sunanda Mitra
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Mr Image ◽  
Mr Image Reconstruction

An Efficient Light-Weight Network for Fast Reconstruction on MR Images

2021 ◽  
Vol 17 ◽  
Author(s):  
Bowen Zhen ◽  
Yingjie Zheng ◽  
Bensheng Qiu
Keyword(s):  
Feature Extraction ◽  
Image Reconstruction ◽  
Data Consistency ◽  
Training Procedure ◽  
Light Weight ◽  
Mr Images ◽  
High Quality ◽  
Mr Image ◽  
Generalization Ability ◽  
Mr Image Reconstruction

Background: In recent years, deep learning (DL) algorithms have emerged in endlessly and achieved impressive performance, which makes it possible to accelerate magnetic resonance (MR) image reconstruction with DL instead of compressed sensing (CS) methods. However, a DL-based MR image reconstruction method has always suffered from its heavy learning parameters and poor generalization ability so far. Therefore, an efficient light-weight network is still in desperate need of fast MR image reconstruction. Methods: We propose an efficient and light-weight MR reconstruction network (named RecNet) that uses a Convolutional Neural Network (CNN) to fast reconstruct high-quality MR images. Specifically, the network is composed of cascade modules, and each cascade module is further divided into feature extraction blocks and a data consistency layer. The feature extraction block can not only effectively extract the features of MR images, but also do not introduce too many parameters for the whole network. To stabilize the training procedure, the correction information of image frequency is adopted in the data consistency (DC) layer. Results: We have evaluated RecNet on a public dataset and the results show that the image quality reconstructed by RecNet is the best on the peak a signal-to-noise ratio (PSNR) and structural similarity index (SSIM) evaluation standards. In addition, the pre-trained RecNet can also reconstruct high-quality MR images on an unseen dataset. Conclusion: The results demonstrate that the RecNet has superior reconstruction ability in various metrics than comparative methods. The RecNet can quickly generate high-quality MR images in fewer parameters. Furthermore, the RecNet has an excellent generalization ability on pathological images and different sampling rates data.

scholarly journals Multi-Layer Basis Pursuit for Compressed Sensing MR Image Reconstruction

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 186222-186232
Author(s):  
Abdul Wahid ◽  
Jawad Ali Shah ◽  
Adnan Umar Khan ◽  
Manzoor Ahmed ◽  
Hanif Razali
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Basis Pursuit ◽  
Mr Image ◽  
Mr Image Reconstruction

scholarly journals MR Image Reconstruction Based on Iterative Split Bregman Algorithm and Nonlocal Total Variation

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Varun P. Gopi ◽  
P. Palanisamy ◽  
Khan A. Wahid ◽  
Paul Babyn
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Least Square ◽  
Split Bregman ◽  
Mr Image ◽  
Split Bregman Iteration ◽  
Nonlocal Total Variation ◽  
Comparison Results ◽  
Space Data ◽  
Mr Image Reconstruction

This paper introduces an efficient algorithm for magnetic resonance (MR) image reconstruction. The proposed method minimizes a linear combination of nonlocal total variation and least-square data-fitting term to reconstruct the MR images from undersampledk-space data. The nonlocal total variation is taken as theL1-regularization functional and solved using Split Bregman iteration. The proposed algorithm is compared with previous methods in terms of the reconstruction accuracy and computational complexity. The comparison results demonstrate the superiority of the proposed algorithm for compressed MR image reconstruction.

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