Exponential Wavelet Iterative Shrinkage Thresholding Algorithm for compressed sensing magnetic resonance imaging

2015 ◽  
Vol 322 ◽  
pp. 115-132 ◽  
Author(s):  
Yudong Zhang ◽  
Zhengchao Dong ◽  
Preetha Phillips ◽  
Shuihua Wang ◽  
Genlin Ji ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Compressed Sensing ◽  
Resonance Imaging ◽  
Iterative Shrinkage ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

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 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 Performance of compressed sensing for fluorine‐19 magnetic resonance imaging at low signal‐to‐noise ratio conditions

2019 ◽  
Vol 84 (2) ◽  
pp. 592-608
Author(s):  
Ludger Starke ◽  
Andreas Pohlmann ◽  
Christian Prinz ◽  
Thoralf Niendorf ◽  
Sonia Waiczies
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Compressed Sensing ◽  
Signal To Noise Ratio ◽  
Resonance Imaging ◽  
Signal To Noise ◽  
Noise Ratio

Improved compressed sensing reconstruction for $$^{19}$$ 19 F magnetic resonance imaging

2019 ◽  
Vol 32 (1) ◽  
pp. 63-77 ◽  
Author(s):  
Thomas Kampf ◽  
Volker J. F. Sturm ◽  
Thomas C. Basse-Lüsebrink ◽  
André Fischer ◽  
Lukas R. Buschle ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Compressed Sensing ◽  
Resonance Imaging
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