Computer Tomography(CT)is a widely used medical image in clinical medicine,which can clearly display the fine structural details of the human body,providing great help for doctors in diagnosing diseases.Recent research shows that the group sparse regularization based SART(Simultaneous Algebraic Reconstruction Technique)reconstruction can achieve better performance in the context of sparse angle sampling.However,while removing artifacts,group sparse regularization may over smooth the edges or details,re-ducing the contrast and making it impossible to obtain high-resolution images consistent with human visual effects.Therefore,we propose a sparse angle image reconstruction method based on improved group sparse regularization.Firstly,the Shepp-Logan model at the sparse angle is reconstructed by SART,and then the group sparse regularization is used to remove the image artifacts.Finally,the rolling guided filtering(RGF)is used to improve the contrast,and it is iterated again as the input of SART.The experimental results show that the proposed method outperforms other algorithms in terms of vision,PSNR(Peak Signal to Noise Ratio),MSE(Mean Squared Error),and FSIM(Feature Similarity),and has good convergence performance at the initial iteration stage.
维普
万方数据