Total Variation (TV) l1 Norm Minimization Based Limited Data X-ray CT Image Reconstruction

2019 ◽  
Vol 31 (3) ◽  
pp. 164-186 ◽  
Author(s):  
Shubhabrata Sarkar ◽  
Pankaj Wahi ◽  
Prabhat Munshi
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Ct Image ◽  
Limited Data ◽  
L1 Norm ◽  
X Ray ◽  
Norm Minimization ◽  
Ct Image Reconstruction

Correction to “Total Variation-Stokes Strategy for Sparse-View X-ray CT Image Reconstruction” [Mar 14 749-763]

2014 ◽  
Vol 33 (4) ◽  
pp. 1004-1004 ◽  
Author(s):  
Yan Liu ◽  
Zhengrong Liang ◽  
Jianhua Ma ◽  
Hongbing Lu ◽  
Ke Wang ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Ct Image ◽  
X Ray ◽  
Ct Image Reconstruction

scholarly journals Total Variation-Stokes Strategy for Sparse-View X-ray CT Image Reconstruction

2014 ◽  
Vol 33 (3) ◽  
pp. 749-763 ◽  
Author(s):  
Yan Liu ◽  
Zhengrong Liang ◽  
Jianhua Ma ◽  
Hongbing Lu ◽  
Ke Wang ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Ct Image ◽  
X Ray ◽  
Ct Image Reconstruction

X-Ray CT Image Reconstruction via Wavelet Frame Based Regularization and Radon Domain Inpainting

2012 ◽  
Vol 54 (2-3) ◽  
pp. 333-349 ◽  
Author(s):  
Bin Dong ◽  
Jia Li ◽  
Zuowei Shen
Keyword(s):  
Image Reconstruction ◽  
Ct Image ◽  
Wavelet Frame ◽  
X Ray ◽  
Ct Image Reconstruction

scholarly journals Line Integral Alternating Minimization Algorithm for Dual-Energy X-Ray CT Image Reconstruction

2016 ◽  
Vol 35 (2) ◽  
pp. 685-698 ◽  
Author(s):  
Yaqi Chen ◽  
Joseph A. O'Sullivan ◽  
David G. Politte ◽  
Joshua D. Evans ◽  
Dong Han ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Dual Energy ◽  
Ct Image ◽  
Alternating Minimization ◽  
Minimization Algorithm ◽  
Line Integral ◽  
X Ray ◽  
Alternating Minimization Algorithm ◽  
Ct Image Reconstruction

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.

Low-Dose X-ray CT Image Reconstruction Based on a Shearlet Transform and Denoising Autoencoder

2019 ◽  
Vol 9 (7) ◽  
pp. 1469-1473
Author(s):  
Wei Zhang ◽  
Yueyang Teng ◽  
Haiyan Wang ◽  
Yan Kang
Keyword(s):  
Image Reconstruction ◽  
Low Dose ◽  
Ct Image ◽  
Denoising Autoencoder ◽  
Shearlet Transform ◽  
X Ray ◽  
Ct Image Reconstruction
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