基于低秩与全变分联合正则化的低剂量CT图像重建  

Low-dose CT image reconstruction based on low-rank and total variation joint regularization

作　　者：刘宇[1,2] 张鹏程 张丽媛[1,2] 刘祎 桂志国[1,2] 张雪怡[1,2] 朱陈一菲 汤豪威 LIU Yu;ZHANG Pengcheng;ZHANG Liyuan;LIU Yi;GUI Zhiguo;ZHANG Xueyi;ZHU Chenyifei;TANG Haowei(Shanxi Provincial Key Laboratory for Biomedical Imaging and Big Data(North University of China),Taiyuan Shanxi 030051,China;School of Information and Communication Engineering,North University of China,Taiyuan Shanxi 030051,China)

机构地区：[1]生物医学成像与影像大数据山西省重点实验室(中北大学),太原030051 [2]中北大学信息与通信工程学院,太原030051

出　　处：《计算机应用》2025年第3期978-987,共10页journal of Computer Applications

基　　金：山西省基础研究计划项目(20210302124403,202303021211148);山西省回国留学人员科研资助项目(2021-111)。

摘　　要：针对全变分(TV)最小化方法在低剂量计算机断层扫描(LDCT)图像重建中易导致的图像过平滑和块状效应等问题,提出一种基于低秩与TV联合正则化的LDCT图像重建方法,以提升LDCT重建图像的视觉质量。首先,建立一个基于低秩与TV联合正则化的图像重建模型,从而从理论上获得更精确和自然的重建结果;其次,通过引入具有非局部自相似特性的低秩先验克服仅使用TV最小化方法存在的局限性;最后,采用Chambolle-Pock(CP)算法优化求解上述模型,以提高模型的求解效率,并保证模型能有效求解。在3种不同LDCT扫描条件下验证所提方法的有效性。在Mayo数据集上的实验结果表明,与PWLS-LDMM(Penalized Weighted Least-Squares based on Low-Dimensional Manifold)方法、NOWNUNM(NOnlocal Weighted NUclear Norm Minimization)方法和CP方法相比,在25%剂量下,所提方法的视觉信息保真度(VIF)分别提升了28.39%、8.30%和2.93%;在15%剂量下,所提方法的VIF分别提升了29.96%、13.83%和4.53%;在10%剂量下,所提方法的VIF分别提升了30.22%、17.10%和7.66%。可见,所提方法在消除噪声和条纹伪影的同时能保留更多的细节纹理信息,验证了所提方法具有较好的噪声伪影抑制能力。Aiming at the problems that the Total Variation(TV)minimization method easily leads to image oversmoothing and block effects in Low-Dose Computed Tomography(LDCT)image reconstruction,an LDCT image reconstruction method based on low-rank and TV joint regularization was proposed to improve the visual quality of LDCT reconstructed images.Firstly,a low-rank and TV joint regularization based image reconstruction model was established,thus,more accurate and natural reconstruction results were obtained theoretically.Secondly,a low-rank prior with non-local self-similarity property was introduced to overcome the limitations of only using the TV minimization method.Finally,the Chambolle-Pock(CP)algorithm was used to optimize and solve the model,which improved the solution efficiency of the model and ensured the effective solution of the model.The effectiveness of the proposed method was verified under three different LDCT scanning conditions.Experimental results on Mayo dataset show that compared with the PWLS-LDMM(Penalized Weighted Least-Squares based on Low-Dimensional Manifold)method,NOWNUNM(NOnlocal Weighted NUclear Norm Minimization)method and CP method,at 25%dose,the proposed method increases the Visual Information Fidelity(VIF)by 28.39%,8.30%and 2.93%,respectively;at 15%dose,the proposed method increases the VIF by 29.96%,13.83%and 4.53%,respectively;at 10%dose,the proposed method increases the VIF by 30.22%,17.10%and 7.66%,respectively.It can be seen that the proposed method can retain more detailed texture information while removing noise and stripe artifacts,which verifies that the proposed method has better noise artifact suppression capability.

关 键 词：低剂量计算机断层扫描 Chambolle-Pock算法 低秩 全变分 图像重建 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]