基于低秩约束的CT重建算法  

CT RECONSTRUCTION ALGORITHM BASED ON LOW RANK CONSTRAINT

作　　者：杨春德[1] 高健 姜小明 Yang Chunde;Gao Jian;Jiang Xiaoming(Department of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Department of Bioinformatics,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学计算机科学与技术学院,重庆400065 [2]重庆邮电大学生物信息学院,重庆400065

出　　处：《计算机应用与软件》2024年第1期204-210,218,共8页Computer Applications and Software

基　　金：国家自然科学基金项目(61801069);重庆市教委科学技术研究项目(KJ1704073);重庆市人力与社会保障局留创计划创新类项目(cx2017011)。

摘　　要：为提高图像重建质量,结合压缩感知理论,提出一种非局部的基于低秩约束的图像重建算法。采用Shepp-Logan头模以及真实脑部CT切片进行重建,以峰值信噪比作为重建图像质量评判标准,并与其他两种重建算法的重建结果比较。经过一定次数迭代后,基于该算法的重建图像结果更贴近原始图像,且收敛时间更早。实验结果表明,在重建低剂量CT图像上,提出的算法在重建质量和收敛速度上均优于对比算法。In order to improve the quality of image reconstruction,a non-local image reconstruction algorithm based on low rank constraint is proposed based on compressed sensing theory.Shepp-Logan head phantom and real brain CT slices were used for reconstruction,and the peak signal-to-noise ratio(PSNR)was used as the evaluation standard of reconstructed image quality,and the reconstruction results of other two reconstruction algorithms were compared.After a certain number of iterations,the reconstructed image results based on this algorithm were closer to the original image,and the convergence time was earlier.The experimental results show that the proposed algorithm is superior to the contrast algorithm in terms of reconstruction quality and convergence speed.

关 键 词：计算机断层成像 压缩感知 低秩约束 全变差 图像去噪 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]