Total generalized variation minimization based on projection data for low-dose CT reconstruction
NIU Shanzhou
WU Heng
YU Zefeng
ZHENG Zijun
YU Gaohang
Abstract：Objective To obtain high-quality low-dose CT images using total generalized variation regularization based on the projection data for low-dose CT reconstruction.Methods The 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.Results The 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.Conclusion The proposed method can effectively suppress noise and strip artifacts in low-dose CT images when piecewise constant assumption is not possible.
Keywords：low-dose CT reconstructiontotal variationtotal generalized variationGaussian distributionfiltered back-projec-tion algorithm
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 1585-1591 )