Affiliations: [a]
School of Automation, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, China
| [b]
Automatic Sorting Technology Research Center, Xi’an University of Posts and Telecommunications, State Post Bureau of the People’s Republic of China, Xi’an, Shaanxi, China
| [c]
Xi’an Key Laboratory of Advanced Control and Intelligent Process, Xi’an, Shaanxi, China
| [d]
School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China
| [e] Beijing Hangxing Machinery Co., Ltd., Dongcheng, Beijing, China
| [f]
School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| [g]
School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, China
Correspondence:
[*]
Corresponding author: Shaojie Tang, School of Automation, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi 710121, China. E-mail: tangshaojie@xupt.edu.cn.
Abstract: Tube of X-ray computed tomography (CT) system emitting a polychromatic spectrum of photons leads to beam hardening artifacts such as cupping and streaks, while the metal implants in the imaged object results in metal artifacts in the reconstructed images. The simultaneous emergence of various beam-hardening artifacts degrades the diagnostic accuracy of CT images in clinics. Thus, it should be deeply investigated for suppressing such artifacts. In this study, data consistency condition is exploited to construct an objective function. Non-convex optimization algorithm is employed to solve the optimal scaling factors. Finally, an optimal bone correction is acquired to simultaneously correct for cupping, streaks and metal artifacts. Experimental result acquired by a realistic computer simulation demonstrates that the proposed method can adaptively determine the optimal scaling factors, and then correct for various beam-hardening artifacts in the reconstructed CT images. Especially, as compared to the nonlinear least squares before variable substitution, the running time of the new CT image reconstruction algorithm decreases 82.36% and residual error reduces 55.95%. As compared to the nonlinear least squares after variable substitution, the running time of the new algorithm decreases 67.54% with the same residual error.
