MO-FG-204-07: Nonlocal Total Variation Based Spectral CT Image Reconstruction

2015 ◽  
Vol 42 (6Part29) ◽  
pp. 3570-3570 ◽  
Author(s):  
J Liu ◽  
H Ding ◽  
S Molloi ◽  
X Zhang ◽  
H Gao
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Ct Image ◽  
Spectral Ct ◽  
Nonlocal Total Variation ◽  
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 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 Distributed Reconstruction via Alternating Direction Method

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Linyuan Wang ◽  
Ailong Cai ◽  
Hanming Zhang ◽  
Bin Yan ◽  
Lei Li ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Total Variation ◽  
Reconstruction Algorithm ◽  
Alternating Direction Method ◽  
Ct Image ◽  
Total Variation Minimization ◽  
Considerable Research ◽  
Alternating Direction ◽  
Ct Image Reconstruction

With the development of compressive sensing theory, image reconstruction from few-view projections has received considerable research attentions in the field of computed tomography (CT). Total-variation- (TV-) based CT image reconstruction has been shown to be experimentally capable of producing accurate reconstructions from sparse-view data. In this study, a distributed reconstruction algorithm based on TV minimization has been developed. This algorithm is very simple as it uses the alternating direction method. The proposed method can accelerate the alternating direction total variation minimization (ADTVM) algorithm without losing accuracy.

scholarly journals Sparse View CT Image Reconstruction Based on Total Variation and Wavelet Frame Regularization

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 57400-57413
Author(s):  
Zhaoyan Qu ◽  
Ximing Yan ◽  
Jinxiao Pan ◽  
Ping Chen
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Ct Image ◽  
Wavelet Frame ◽  
Ct Image Reconstruction

Improved total variation-based CT image reconstruction applied to clinical data

2011 ◽  
Vol 56 (6) ◽  
pp. 1545-1561 ◽  
Author(s):  
Ludwig Ritschl ◽  
Frank Bergner ◽  
Christof Fleischmann ◽  
Marc Kachelrieß
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Clinical Data ◽  
Ct Image ◽  
Ct Image Reconstruction
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