南方医科大学学报2017，Vol.37Issue(12)：1585-1591,7.DOI:10.3969/j.issn.1673-4254.2017.12.04
基于投影数据全广义变分最小化的低剂量CT重建
Total generalized variation minimization based on projection data for low-dose CT reconstruction
摘要
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.关键词
低剂量CT重建/全变分/全广义变分正则化/Gaussian分布/滤波反投影算法Key words
low-dose CT reconstruction/total variation/total generalized variation/Gaussian distribution/filtered back-projec-tion algorithm引用本文复制引用
牛善洲,吴恒,喻泽峰,郑子君,喻高航..基于投影数据全广义变分最小化的低剂量CT重建[J].南方医科大学学报,2017,37(12):1585-1591,7.基金项目
国家自然科学基金(11701097,11661007,11661008) （11701097,11661007,11661008）
江西省自然科学基金青年基金(2016BAB212055) （2016BAB212055）
江西省教育厅科学技术研究项目(GJJ150994)Supported by National Natural Science Foundation of China(11701097, 11661007,11661008). （GJJ150994）
