摘要
Inspired by total variation(TV), this paper represents a new iterative algorithm based on diagonal total variation(DTV) to address the computed tomography image reconstruction problem. To improve the quality of a reconstructed image, we used DTV to sparsely represent images when iterative convergence of the reconstructed algorithm with TV-constraint had no effect during the reconstruction process. To investigate our proposed algorithm, the numerical and experimental studies were performed, and rootmean-square error(RMSE) and structure similarity(SSIM)were used to evaluate the reconstructed image quality. The results demonstrated that the proposed method could effectively reduce noise, suppress artifacts, and reconstruct highquality image from incomplete projection data.
Inspired by total variation(TV), this paper represents a new iterative algorithm based on diagonal total variation(DTV) to address the computed tomography image reconstruction problem. To improve the quality of a reconstructed image, we used DTV to sparsely represent images when iterative convergence of the reconstructed algorithm with TV-constraint had no effect during the reconstruction process. To investigate our proposed algorithm, the numerical and experimental studies were performed, and rootmean-square error(RMSE) and structure similarity(SSIM)were used to evaluate the reconstructed image quality. The results demonstrated that the proposed method could effectively reduce noise, suppress artifacts, and reconstruct highquality image from incomplete projection data.
基金
supported in part by the National Natural Science Foundation of China(No.61401049)
the Chongqing Foundation and Frontier Research Project(Nos.cstc2016jcyjA0473,cstc2013jcyjA0763)
the Graduate Scientific Research and Innovation Foundation of Chongqing,China(No.CYB16044)
the Strategic Industry Key Generic Technology Innovation Project of Chongqing(No.cstc2015zdcy-ztzxX0002)
China Scholarship Council
the Fundamental Research Funds for the Central Universities Nos.CDJZR14125501,106112016CDJXY120003,10611CDJXZ238826