• Shan-Zhou Niu, Heng Wu, Ze-Feng Yu, Zi-Jun Zheng, and Gao-Hang Yu.
• School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China. E-mail: maszniu@163.com.
• Nan Fang Yi Ke Da Xue Xue Bao. 2017 Dec 20; 37 (12): 1585-1591.

ObjectiveTo obtain high?quality low?dose CT images using total generalized variation regularization based on the projection data for low?dose CT reconstruction.MethodsThe projection data of the CT images were transformed from Poisson distribution to Gaussian distribution using the linear Anscombe transform. The transformed data were then restored by an efficient total generalized variation minimization algorithm. Reconstruction was finally achieved by inverse Anscombe transform and filtered back projection (FBP) method.ResultsThe image quality of low?dose CT was greatly improved by the proposed algorithm in both Clock and Shepp?Logan phantoms. The signal?to?noise ratios (SNRs) of the Clock and Shepp-Logan images reconstructed by FBP algorithm were 17.752 dB and 19.379 dB, which were increased by the proposed algorithm to 24.0352 and 23.4181 dB, respectively. The NMSE of the Clock and Shepp?Logan images reconstructed by FBP algorithm was 0.86% and 0.58%, which was reduced by the proposed algorithm to 0.2% and 0.23%, respectively.ConclusionThe proposed method can effectively suppress noise and strip artifacts in low?dose CT images when piecewise constant assumption is not possible.

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