A deep unrolling network inspired by total variation for compressed sensing MRI

2020 ◽  
Vol 107 ◽  
pp. 102856
Author(s):  
Xiaohua Zhang ◽  
Qiusheng Lian ◽  
Yuchi Yang ◽  
Yueming Su
Keyword(s):  
Compressed Sensing ◽  
Total Variation

scholarly journals Optimization of STEM‐HAADF Electron Tomography Reconstructions by Parameter Selection in Compressed Sensing Total Variation Minimization‐Based Algorithms

2020 ◽  
Vol 37 (6) ◽  
pp. 2000070
Author(s):  
Juan M. Muñoz‐Ocaña ◽  
Ainouna Bouziane ◽  
Farzeen Sakina ◽  
Richard T. Baker ◽  
Ana B. Hungría ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Total Variation ◽  
Electron Tomography ◽  
Parameter Selection ◽  
Total Variation Minimization

scholarly journals Improved Compressed Sensing-Based Algorithm for Sparse-View CT Image Reconstruction

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Zangen Zhu ◽  
Khan Wahid ◽  
Paul Babyn ◽  
David Cooper ◽  
Isaac Pratt ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Compressed Sensing ◽  
Total Variation ◽  
Sampling Rate ◽  
Discrete Wavelet ◽  
Ct Image ◽  
Streak Artifact ◽  
Limited Sampling ◽  
Streak Artifacts ◽  
Ct Image Reconstruction

In computed tomography (CT), there are many situations where reconstruction has to be performed with sparse-view data. In sparse-view CT imaging, strong streak artifacts may appear in conventionally reconstructed images due to limited sampling rate that compromises image quality. Compressed sensing (CS) algorithm has shown potential to accurately recover images from highly undersampled data. In the past few years, total-variation-(TV-) based compressed sensing algorithms have been proposed to suppress the streak artifact in CT image reconstruction. In this paper, we propose an efficient compressed sensing-based algorithm for CT image reconstruction from few-view data where we simultaneously minimize three parameters: theℓ1norm, total variation, and a least squares measure. The main feature of our algorithm is the use of two sparsity transforms—discrete wavelet transform and discrete gradient transform. Experiments have been conducted using simulated phantoms and clinical data to evaluate the performance of the proposed algorithm. The results using the proposed scheme show much smaller streaking artifacts and reconstruction errors than other conventional methods.

scholarly journals A General Total Variation Minimization Theorem for Compressed Sensing Based Interior Tomography

2009 ◽  
Vol 2009 ◽  
pp. 1-3 ◽  
Author(s):  
Weimin Han ◽  
Hengyong Yu ◽  
Ge Wang
Keyword(s):  
Compressed Sensing ◽  
Total Variation ◽  
General Theorem ◽  
Region Of Interest ◽  
Delta Function ◽  
Piecewise Constant Function ◽  
Mathematical Tool ◽  
Total Variation Minimization ◽  
Piecewise Constant ◽  
Dirac Delta

Recently, in the compressed sensing framework we found that a two-dimensional interior region-of-interest (ROI) can be exactly reconstructed via the total variation minimization if the ROI is piecewise constant (Yu and Wang, 2009). Here we present a general theorem charactering a minimization property for a piecewise constant function defined on a domain in any dimension. Our major mathematical tool to prove this result is functional analysis without involving the Dirac delta function, which was heuristically used by Yu and Wang (2009).

scholarly journals Jacobian weighted temporal total variation for motion compensated compressed sensing reconstruction of dynamic MRI

2016 ◽  
Vol 77 (3) ◽  
pp. 1208-1215 ◽  
Author(s):  
Javier Royuela-del-Val ◽  
Lucilio Cordero-Grande ◽  
Federico Simmross-Wattenberg ◽  
Marcos Martín-Fernández ◽  
Carlos Alberola-López
Keyword(s):  
Compressed Sensing ◽  
Total Variation ◽  
Dynamic Mri
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