scholarly journals Multi‐shot diffusion‐weighted MRI reconstruction with magnitude‐based spatial‐angular locally low‐rank regularization (SPA‐LLR)

2019 ◽  
Vol 83 (5) ◽  
pp. 1596-1607 ◽  
Author(s):  
Yuxin Hu ◽  
Xiaole Wang ◽  
Qiyuan Tian ◽  
Grant Yang ◽  
Bruce Daniel ◽  
...  
Keyword(s):  
Low Rank ◽  
Diffusion Weighted Mri ◽  
Diffusion Weighted ◽  
Mri Reconstruction

scholarly journals PCLR: Phase-constrained low-rank model for compressive diffusion-weighted MRI

2013 ◽  
Vol 72 (5) ◽  
pp. 1330-1341 ◽  
Author(s):  
Hao Gao ◽  
Longchuan Li ◽  
Kai Zhang ◽  
Weifeng Zhou ◽  
Xiaoping Hu
Keyword(s):  
Low Rank ◽  
Diffusion Weighted Mri ◽  
Diffusion Weighted

scholarly journals RUN‐UP: Accelerated multishot diffusion‐weighted MRI reconstruction using an unrolled network with U‐Net as priors

2020 ◽  
Vol 85 (2) ◽  
pp. 709-720 ◽  
Author(s):  
Yuxin Hu ◽  
Yunyingying Xu ◽  
Qiyuan Tian ◽  
Feiyu Chen ◽  
Xinwei Shi ◽  
...  
Keyword(s):  
Diffusion Weighted Mri ◽  
Diffusion Weighted ◽  
Mri Reconstruction ◽  
Run Up

scholarly journals Comparison of Diffusion-Weighted MRI Reconstruction Methods for Visualization of Cranial Nerves in Posterior Fossa Surgery

2017 ◽  
Vol 11 ◽  
Author(s):  
Brendan Behan ◽  
David Q. Chen ◽  
Francesco Sammartino ◽  
Danielle D. DeSouza ◽  
Erika Wharton-Shukster ◽  
...  
Keyword(s):  
Posterior Fossa ◽  
Cranial Nerves ◽  
Posterior Fossa Surgery ◽  
Diffusion Weighted Mri ◽  
Diffusion Weighted ◽  
Reconstruction Methods ◽  
Mri Reconstruction

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.

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