scholarly journals A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Yudong Zhang ◽  
Jiquan Yang ◽  
Jianfei Yang ◽  
Aijun Liu ◽  
Ping Sun
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Compressed Sensing ◽  
Mean Squared Error ◽  
State Of The Art ◽  
Computation Time ◽  
Resonance Imaging ◽  
Iterative Shrinkage ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

Aim. It can help improve the hospital throughput to accelerate magnetic resonance imaging (MRI) scanning. Patients will benefit from less waiting time.Task. In the last decade, various rapid MRI techniques on the basis of compressed sensing (CS) were proposed. However, both computation time and reconstruction quality of traditional CS-MRI did not meet the requirement of clinical use.Method. In this study, a novel method was proposed with the name of exponential wavelet iterative shrinkage-thresholding algorithm with random shift (abbreviated as EWISTARS). It is composed of three successful components: (i) exponential wavelet transform, (ii) iterative shrinkage-thresholding algorithm, and (iii) random shift.Results. Experimental results validated that, compared to state-of-the-art approaches, EWISTARS obtained the least mean absolute error, the least mean-squared error, and the highest peak signal-to-noise ratio.Conclusion. EWISTARS is superior to state-of-the-art approaches.

scholarly journals Parallel Computing of Patch-Based Nonlocal Operator and Its Application in Compressed Sensing MRI

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Qiyue Li ◽  
Xiaobo Qu ◽  
Yunsong Liu ◽  
Di Guo ◽  
Jing Ye ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Compressed Sensing ◽  
Multicore Processors ◽  
Parallel Architecture ◽  
Computation Time ◽  
Reconstruction Error ◽  
Nonlocal Operator ◽  
Resonance Imaging ◽  
Reconstruction Process

Magnetic resonance imaging has been benefited from compressed sensing in improving imaging speed. But the computation time of compressed sensing magnetic resonance imaging (CS-MRI) is relatively long due to its iterative reconstruction process. Recently, a patch-based nonlocal operator (PANO) has been applied in CS-MRI to significantly reduce the reconstruction error by making use of self-similarity in images. But the two major steps in PANO, learning similarities and performing 3D wavelet transform, require extensive computations. In this paper, a parallel architecture based on multicore processors is proposed to accelerate computations of PANO. Simulation results demonstrate that the acceleration factor approaches the number of CPU cores and overall PANO-based CS-MRI reconstruction can be accomplished in several seconds.

Accelerated 3D blind separation of convolved mixtures based on the fast iterative shrinkage thresholding algorithm for adaptive multiple subtraction

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. V99-V113 ◽  
Author(s):  
Zhong-Xiao Li ◽  
Zhen-Chun Li
Keyword(s):  
Field Data ◽  
Optimization Problem ◽  
Computational Cost ◽  
Computation Time ◽  
Blind Separation ◽  
L1 Norm ◽  
Norm Minimization ◽  
Iterative Shrinkage ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

After multiple prediction, adaptive multiple subtraction is essential for the success of multiple removal. The 3D blind separation of convolved mixtures (3D BSCM) method, which is effective in conducting adaptive multiple subtraction, needs to solve an optimization problem containing L1-norm minimization constraints on primaries by the iterative reweighted least-squares (IRLS) algorithm. The 3D BSCM method can better separate primaries and multiples than the 1D/2D BSCM method and the method with energy minimization constraints on primaries. However, the 3D BSCM method has high computational cost because the IRLS algorithm achieves nonquadratic optimization with an LS optimization problem solved in each iteration. In general, it is good to have a faster 3D BSCM method. To improve the adaptability of field data processing, the fast iterative shrinkage thresholding algorithm (FISTA) is introduced into the 3D BSCM method. The proximity operator of FISTA can solve the L1-norm minimization problem efficiently. We demonstrate that our FISTA-based 3D BSCM method achieves similar accuracy of estimating primaries as that of the reference IRLS-based 3D BSCM method. Furthermore, our FISTA-based 3D BSCM method reduces computation time by approximately 60% compared with the reference IRLS-based 3D BSCM method in the synthetic and field data examples.

scholarly journals An Augmented Lagrangian Based Compressed Sensing Reconstruction for Non-Cartesian Magnetic Resonance Imaging without Gridding and Regridding at Every Iteration

PLoS ONE ◽  
2014 ◽  
Vol 9 (9) ◽  
pp. e107107 ◽  
Author(s):  
Mehmet Akçakaya ◽  
Seunghoon Nam ◽  
Tamer A. Basha ◽  
Keigo Kawaji ◽  
Vahid Tarokh ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Compressed Sensing ◽  
Augmented Lagrangian ◽  
Resonance Imaging

scholarly journals Cardiovascular magnetic resonance imaging for amyloidosis: The state-of-the-art

2019 ◽  
Vol 29 (2) ◽  
pp. 83-94 ◽  
Author(s):  
Chun Xiang Tang ◽  
Steffen E. Petersen ◽  
Mihir M. Sanghvi ◽  
Guang Ming Lu ◽  
Long Jiang Zhang
Keyword(s):  
Magnetic Resonance Imaging ◽  
Cardiovascular Magnetic Resonance ◽  
Magnetic Resonance ◽  
Cardiovascular Magnetic Resonance Imaging ◽  
State Of The Art ◽  
The State ◽  
Resonance Imaging

Applications of Magnetic Resonance Imaging of the Thorax in Pleural Diseases: A State-of-the-Art Review

Lung ◽  
2016 ◽  
Vol 194 (4) ◽  
pp. 501-509 ◽  
Author(s):  
Fernanda Miraldi Clemente Pessôa ◽  
Alessandro Severo Alves de Melo ◽  
Arthur Soares Souza ◽  
Luciana Soares de Souza ◽  
Bruno Hochhegger ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
State Of The Art ◽  
Resonance Imaging ◽  
Pleural Diseases
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