scholarly journals Low-dimensional-structure self-learning and thresholding: Regularization beyond compressed sensing for MRI Reconstruction

2011 ◽  
Vol 66 (3) ◽  
pp. 756-767 ◽  
Author(s):  
Mehmet Akçakaya ◽  
Tamer A. Basha ◽  
Beth Goddu ◽  
Lois A. Goepfert ◽  
Kraig V. Kissinger ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Dimensional Structure ◽  
Mri Reconstruction ◽  
Low Dimensional ◽  
Self Learning

scholarly journals Accelerated contrast-enhanced whole-heart coronary MRI using low-dimensional-structure self-learning and thresholding

2012 ◽  
Vol 67 (5) ◽  
pp. spcone-spcone
Author(s):  
Mehmet Akçakaya ◽  
Tamer A. Basha ◽  
Raymond H. Chan ◽  
Hussein Rayatzadeh ◽  
Kraig V. Kissinger ◽  
...  
Keyword(s):  
Dimensional Structure ◽  
Contrast Enhanced ◽  
Low Dimensional ◽  
Whole Heart ◽  
Self Learning ◽  
Coronary Mri

scholarly journals Accelerated contrast-enhanced whole-heart coronary MRI using low-dimensional-structure self-learning and thresholding

2012 ◽  
Vol 67 (5) ◽  
pp. 1434-1443 ◽  
Author(s):  
Mehmet Akçakaya ◽  
Tamer A. Basha ◽  
Raymond H. Chan ◽  
Hussein Rayatzadeh ◽  
Kraig V. Kissinger ◽  
...  
Keyword(s):  
Dimensional Structure ◽  
Contrast Enhanced ◽  
Low Dimensional ◽  
Whole Heart ◽  
Self Learning ◽  
Coronary Mri

An Algorithm Combining Analysis-based Blind Compressed Sensing and Nonlocal Low-rank Constraints for MRI Reconstruction

2019 ◽  
Vol 15 (3) ◽  
pp. 281-291 ◽  
Author(s):  
Mei Sun ◽  
Jinxu Tao ◽  
Zhongfu Ye ◽  
Bensheng Qiu ◽  
Jinzhang Xu ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Signal Reconstruction ◽  
Low Rank ◽  
Mr Images ◽  
Scanning Time ◽  
Previous State ◽  
Rank Approximation ◽  
Mri Reconstruction ◽  
Magnetic Resonance Imaging Mri ◽  
Rank Constraints

Background: In order to overcome the limitation of long scanning time, compressive sensing (CS) technology exploits the sparsity of image in some transform domain to reduce the amount of acquired data. Therefore, CS has been widely used in magnetic resonance imaging (MRI) reconstruction. </P><P> Discussion: Blind compressed sensing enables to recover the image successfully from highly under- sampled measurements, because of the data-driven adaption of the unknown transform basis priori. Moreover, analysis-based blind compressed sensing often leads to more efficient signal reconstruction with less time than synthesis-based blind compressed sensing. Recently, some experiments have shown that nonlocal low-rank property has the ability to preserve the details of the image for MRI reconstruction. Methods: Here, we focus on analysis-based blind compressed sensing, and combine it with additional nonlocal low-rank constraint to achieve better MR images from fewer measurements. Instead of nuclear norm, we exploit non-convex Schatten p-functionals for the rank approximation. </P><P> Results & Conclusion: Simulation results indicate that the proposed approach performs better than the previous state-of-the-art algorithms.

scholarly journals Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss

2018 ◽  
Vol 37 (6) ◽  
pp. 1488-1497 ◽  
Author(s):  
Tran Minh Quan ◽  
Thanh Nguyen-Duc ◽  
Won-Ki Jeong
Keyword(s):  
Compressed Sensing ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Mri Reconstruction

ℓ1 Minimization in ℓ1-SPIRiT Compressed Sensing MRI Reconstruction

2011 ◽  
pp. 723-735 ◽  
Author(s):  
Mark Murphy ◽  
Miki Lustig
Keyword(s):  
Compressed Sensing ◽  
ℓ1 Minimization ◽  
Mri Reconstruction

Low-Dimensional-Structure Devices for Future ElectronicsBehaviors

2018 ◽  
Author(s):  
Shunri Oda ◽  
Takamasa Kawanago ◽  
Hitoshi Wakabayashi
Keyword(s):  
Dimensional Structure ◽  
Low Dimensional

Emission in Wide Band Gap II-VI Semiconductor Compounds with Low Dimensional Structure

2003 ◽  
pp. 211-216
Author(s):  
Y Yang ◽  
X Fan ◽  
J Zhang ◽  
Y Liu ◽  
Y Lu ◽  
...  
Keyword(s):  
Band Gap ◽  
Wide Band ◽  
Dimensional Structure ◽  
Wide Band Gap ◽  
Semiconductor Compounds ◽  
Low Dimensional
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