摘要
本文提出了一种基于稀疏贝叶斯智能优化(SBL-RNAMBO)算法的低剂量医学CT图像的盲复原重建方法.首先,利用群智能算法的全局搜索能力,同时引入稀疏贝叶斯方法进行训练,将大量全剂量CT图像作为先验信息,改善图像重建过程中的欠定问题.其次,加入二次惩罚项约束解空间,构造了参数未知的后验概率目标函数,采用RNAMBO算法优化稀疏贝叶斯超参数,建立优化后的稀疏贝叶斯模型.最后,用SBL-RNAMBO方法对所有盲复原未知量进行估计并求解后验概率目标函数.将SBL-RNAMBO算法与其他5种对比算法进行Shepp-Logan体膜、临床盆腔CT、临床脑部CT的定性及定量实验,实验结果表明,在定性实验中该方法可以获得良好的CT重建图像,保留清晰的纹理细节和结构特征;在145/20 mA及90/20 mA定量实验中峰值信噪比(PSNR)、通用图像质量指数(UIQI)、结构相似性指数(SSIM)、误差方差和(SSDE)指标均优于对比算法,算法复杂度最大减少854.6 s,通过不同初始值PSNR实验,验证了该算法的稳定性及有效性.
This paper proposes a blind reconstruction method for low-dose medical computer tomography(CT)images based on the sparse Bayesian intelligent optimization(SBL-RNAMBO)algorithm.First,we applied the global search capability of the swarm intelligence algorithm and introduced the sparse Bayesian method for training,and a large number of full-dose CT images were used as prior information to improve the underdetermined problem in the image reconstruction process.Second,a quadratic penalty term was added to constrain the solution space,and a posterior probability objective function with unknown parameters was constructed.The RNAMBO algorithm was used to optimize the sparse Bayesian hyperparameters to establish the optimized sparse Bayesian model.Finally,the SBL-RNAMBO method was used to estimate all unknown variables of blind restoration as well as to solve the posterior probability objective function.The SBL-RNAMBO algorithm was then applied with five other algorithms to perform qualitative and quantitative experiments on the Shepp-Logan body membrane,clinical pelvic CT,and clinical brain CT.The results showed that the SBL-RNMABO method could obtain good CT reconstruction images in qualitative experiments,while retaining clear textural details and structural features.In the 145/20 mA and 90/20mA quantitative experiments,the peak signal-to-noise ratio(PSNR),universal image quality index(UIQI),structure similarity index(SSIM),sum of squared difference error(SSDE)indices were better than the contrast algorithm,and the algo-rithm complexity could be reduced by 854.6 s.The PSNR experiment with different initial values verified the stability and effectiveness of the algorithm.
作者
刘晓培
滕建辅
费腾
孙云山
Liu Xiaopei;Teng Jianfu;Fei Teng;Sun Yunshan(School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China;School of Communication,Tianjin University of Commerce,Tianjin 300134,China;School of Microelectronics,Tianjin University,Tianjin 300072,China)
出处
《天津大学学报（自然科学与工程技术版）》
EI
CAS
CSCD
北大核心
2021年第10期1076-1085,共10页
Journal of Tianjin University：Science and Technology
基金
天津市自然科学基金资助项目(16JCYBJC28800).
关键词
低剂量CT图像
图像盲复原重建
群智能优化
稀疏贝叶斯算法
low dose CT image
blind image restoration and reconstruction
swarm intelligence optimization
sparse Bayesian algorithm
