scholarly journals Spectral CT Reconstruction Based on PICCS and Dictionary Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 133367-133376
Author(s):  
Huihua Kong ◽  
Xiaoxue Lei ◽  
Lei Lei ◽  
Yanbo Zhang ◽  
Hengyong Yu
Keyword(s):  
Dictionary Learning ◽  
Ct Reconstruction ◽  
Spectral Ct

scholarly journals Tensor Dictionary Learning with an Enhanced Sparsity Constraint for Sparse-View Spectral CT Reconstruction

Photonics ◽  
2022 ◽  
Vol 9 (1) ◽  
pp. 35
Author(s):  
Xuru Li ◽  
Xueqin Sun ◽  
Yanbo Zhang ◽  
Jinxiao Pan ◽  
Ping Chen
Keyword(s):  
Dictionary Learning ◽  
Ct Images ◽  
Ct Reconstruction ◽  
Spectral Ct ◽  
Noise Elimination ◽  
Split Bregman ◽  
Edge Preservation ◽  
Edge Information ◽  
Energy Channel ◽  
Sparsity Constraint

Spectral computed tomography (CT) can divide collected photons into multi-energy channels and gain multi-channel projections synchronously by using photon-counting detectors. However, reconstructed images usually contain severe noise due to the limited number of photons in the corresponding energy channel. Tensor dictionary learning (TDL)-based methods have achieved better performance, but usually lose image edge information and details, especially from an under-sampling dataset. To address this problem, this paper proposes a method termed TDL with an enhanced sparsity constraint for spectral CT reconstruction. The proposed algorithm inherits the superiority of TDL by exploring the correlation of spectral CT images. Moreover, the method designs a regularization using the L0-norm of the image gradient to constrain images and the difference between images and a prior image in each energy channel simultaneously, further improving the ability to preserve edge information and subtle image details. The split-Bregman algorithm has been applied to address the proposed objective minimization model. Several numerical simulations and realistic preclinical mice are studied to assess the effectiveness of the proposed algorithm. The results demonstrate that the proposed method improves the quality of spectral CT images in terms of noise elimination, edge preservation, and image detail recovery compared to the several existing better methods.

scholarly journals Tensor-Based Dictionary Learning for Spectral CT Reconstruction

2017 ◽  
Vol 36 (1) ◽  
pp. 142-154 ◽  
Author(s):  
Yanbo Zhang ◽  
Xuanqin Mou ◽  
Ge Wang ◽  
Hengyong Yu
Keyword(s):  
Dictionary Learning ◽  
Ct Reconstruction ◽  
Spectral Ct

scholarly journals Iterative spectral CT reconstruction based on low rank and average-image-incorporated BM3D

2018 ◽  
Vol 63 (15) ◽  
pp. 155021 ◽  
Author(s):  
Morteza Salehjahromi ◽  
Yanbo Zhang ◽  
Hengyong Yu
Keyword(s):  
Low Rank ◽  
Ct Reconstruction ◽  
Spectral Ct ◽  
Average Image

MO-DE-207A-05: Dictionary Learning Based Reconstruction with Low-Rank Constraint for Low-Dose Spectral CT

2016 ◽  
Vol 43 (6Part30) ◽  
pp. 3701-3701 ◽  
Author(s):  
Q Xu ◽  
H Liu ◽  
H Yu ◽  
G Wang ◽  
L Xing
Keyword(s):  
Dictionary Learning ◽  
Low Dose ◽  
Low Rank ◽  
Spectral Ct ◽  
Rank Constraint

SU-F-I-12: Region-Specific Dictionary Learning for Low-Dose X-Ray CT Reconstruction

2016 ◽  
Vol 43 (6Part7) ◽  
pp. 3389-3389 ◽  
Author(s):  
Q Xu ◽  
H Han ◽  
L Xing
Keyword(s):  
Dictionary Learning ◽  
Low Dose ◽  
Ct Reconstruction ◽  
X Ray

Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Weiwen Wu ◽  
Peijun Chen ◽  
Shaoyu Wang ◽  
Varut Vardhanabhuti ◽  
Fenglin Liu ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Spectral Ct ◽  
Material Decomposition ◽  
Image Domain

scholarly journals Spectral CT Reconstruction With Image Sparsity and Spectral Mean

2016 ◽  
Vol 2 (4) ◽  
pp. 510-523 ◽  
Author(s):  
Yi Zhang ◽  
Yan Xi ◽  
Qingsong Yang ◽  
Wenxiang Cong ◽  
Jiliu Zhou ◽  
...  
Keyword(s):  
Ct Reconstruction ◽  
Spectral Ct
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