Open AccessImage gradient L0-norm based PICCS for swinging multi-source CT reconstruction
Author(s) -
Haijun Yu,
Weiwen Wu,
Peijun Chen,
Changcheng Gong,
Junru Jiang,
Shaoyu Wang,
Fenglin Liu,
Hengyong Yu
Publication year - 2019
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.27.005264
Subject(s) - computer science , computer vision , artificial intelligence , iterative reconstruction , image restoration , compressed sensing , norm (philosophy) , artifact (error) , image resolution , image quality , image processing , optics , image (mathematics) , physics , political science , law
Dynamic computed tomography (CT) is usually employed to image motion objects, such as beating heart, coronary artery and cerebral perfusion, etc. Recently, to further improve the temporal resolution for aperiodic industrial process imaging, the swinging multi-source CT (SMCT) systems and the corresponding swinging multi-source prior image constrained compressed sensing (SM-PICCS) method were developed. Since the SM-PICCS uses the L 1 -norm of image gradient, the edge structures in the reconstructed images are blurred and motion artifacts are still present. Inspired by the advantages in terms of image edge preservation and fine structure recovering, the L 0 -norm of image gradient is incorporated into the prior image constrained compressed sensing, leading to an L 0 -PICCS Algorithm 1Table 1The parameters of L0-PICCS (δ 1 ,δ 2 ,λ1*,λ2*) for numerical simulation.Sourceswδ 1 (10-2)δ 2 (10-2)λ1*(10-2)λ2*(10-8)Noise-free510522.001.525522.001.55035002.00471014.33332.00500025522.00500050222.005000Noise51062002.505002554502.501.55054502.901.571027.385.91.5810000258.285.91.5850050522.001.5. The experimental results confirm that the L 0 -PICCS outperforms the SM-PICCS in both visual inspection and quantitative analysis.