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Iterative Image Reconstruction for Limited-Angle CT using Optimized Initial Image


Affiliations
1 School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
 

Limited-angle computed tomography (CT) has great impact in some clinical applications. Existing iterative reconstruction algorithms could not reconstruct high-quality images, leading to severe artifacts nearby edges. Optimal selection of initial image would influence the iterative reconstruction performance but has not been studied deeply yet. In this work, we proposed to generate optimized initial image followed by total variation (TV) based iterative reconstruction considering the feature of image symmetry. The simulated data and real data reconstruction results indicate that the proposed method effectively removes the artifacts nearby edges.
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  • Iterative Image Reconstruction for Limited-Angle CT using Optimized Initial Image

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Authors

Jingyu Guo
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
Hongliang Qi
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
Yuan Xu
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
Zijia Chen
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
Shulong Li
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
Linghong Zhou
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China

Abstract


Limited-angle computed tomography (CT) has great impact in some clinical applications. Existing iterative reconstruction algorithms could not reconstruct high-quality images, leading to severe artifacts nearby edges. Optimal selection of initial image would influence the iterative reconstruction performance but has not been studied deeply yet. In this work, we proposed to generate optimized initial image followed by total variation (TV) based iterative reconstruction considering the feature of image symmetry. The simulated data and real data reconstruction results indicate that the proposed method effectively removes the artifacts nearby edges.