scholarly journals CT Image Reconstruction Algorithm Based on Anisotropic Total Variation Minimization

2014 ◽  
Vol 04 (10) ◽  
pp. 240-247
Author(s):  
莹 张
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Reconstruction Algorithm ◽  
Ct Image ◽  
Total Variation Minimization ◽  
Image Reconstruction Algorithm ◽  
Ct Image Reconstruction ◽  
Anisotropic Total Variation

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.

Inexact Distributed Reconstruction via Alternating Direction Method

2014 ◽  
Vol 511-512 ◽  
pp. 417-420
Author(s):  
Lin Yuan Wang ◽  
Ai Long Cai ◽  
Bin Yan ◽  
Lei Li ◽  
Han Ming Zhang ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Reconstruction Algorithm ◽  
Alternating Direction Method ◽  
Experimental Results ◽  
Ct Image ◽  
Total Variation Minimization ◽  
Alternating Direction ◽  
Proximal Points ◽  
Ct Image Reconstruction

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, an inexact distributed reconstruction algorithm based on TV minimization has been developed. The algorithm is relatively simple as it uses the inexact alternating direction method, which involves linearization and proximal points techniques. The outstanding acceleration factor is achieved as the algorithm distributes the data and computation to individual nodes. Experimental results demonstrate that the proposed method can accelerate the alternating direction total variation minimization (ADTVM) algorithm with very little accuracy loss.

Ultra-limited-angle CT image reconstruction algorithm based on reweighting and edge-preserving

2022 ◽  
pp. 1-13
Author(s):  
Lei Shi ◽  
Gangrong Qu ◽  
Yunsong Zhao
Keyword(s):  
Image Reconstruction ◽  
Condition Number ◽  
Reconstruction Algorithm ◽  
Projection Data ◽  
Ct Image ◽  
Reconstruction Algorithms ◽  
Edge Preserving ◽  
Image Reconstruction Algorithm ◽  
Ct Image Reconstruction ◽  
Limited Angle

BACKGROUND: Ultra-limited-angle image reconstruction problem with a limited-angle scanning range less than or equal to π 2 is severely ill-posed. Due to the considerably large condition number of a linear system for image reconstruction, it is extremely challenging to generate a valid reconstructed image by traditional iterative reconstruction algorithms. OBJECTIVE: To develop and test a valid ultra-limited-angle CT image reconstruction algorithm. METHODS: We propose a new optimized reconstruction model and Reweighted Alternating Edge-preserving Diffusion and Smoothing algorithm in which a reweighted method of improving the condition number is incorporated into the idea of AEDS image reconstruction algorithm. The AEDS algorithm utilizes the property of image sparsity to improve partially the results. In experiments, the different algorithms (the Pre-Landweber, AEDS algorithms and our algorithm) are used to reconstruct the Shepp-Logan phantom from the simulated projection data with noises and the flat object with a large ratio between length and width from the real projection data. PSNR and SSIM are used as the quantitative indices to evaluate quality of reconstructed images. RESULTS: Experiment results showed that for simulated projection data, our algorithm improves PSNR and SSIM from 22.46db to 39.38db and from 0.71 to 0.96, respectively. For real projection data, our algorithm yields the highest PSNR and SSIM of 30.89db and 0.88, which obtains a valid reconstructed result. CONCLUSIONS: Our algorithm successfully combines the merits of several image processing and reconstruction algorithms. Thus, our new algorithm outperforms significantly other two algorithms and is valid for ultra-limited-angle CT image reconstruction.

scholarly journals EP-2039 Validation Of The Direct Density™ Ct Image Reconstruction Algorithm

2019 ◽  
Vol 133 ◽  
pp. S1119-S1120
Author(s):  
I. Peiro Riera ◽  
E. Fernandez-Velilla Ceprià ◽  
J. Quera Jordana ◽  
O. Pera Cegarra ◽  
N. Anton Comelles ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Ct Image ◽  
Image Reconstruction Algorithm ◽  
Ct Image Reconstruction

scholarly journals CT Image Reconstruction Algorithm to Reduce Metal Artifact

2006 ◽  
Vol 72 (724) ◽  
pp. 1888-1894
Author(s):  
Michihiko KOSEKI ◽  
Shuhei HASHIMOTO ◽  
Shinpei SATO ◽  
Hitoshi KIMURA ◽  
Norio INOU
Keyword(s):  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Metal Artifact ◽  
Ct Image ◽  
Image Reconstruction Algorithm ◽  
Ct Image Reconstruction
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