摘要
目的为减少螺旋CT扫描X射线辐射剂量,提出一种基于凸集投影的张量广义全变分最小(TTGV-POCS)的稀疏角度螺旋CT迭代重建算法。方法将螺旋CT三维体数据看作三阶张量,利用张量广义全变分(TTGV)最小约束刻画其三维图像的数据特性,并纳入凸集投影迭代重建框架,实现稀疏角度螺旋CT的鲁棒重建。TTGV-POCS算法充分利用螺旋CT图像数据的一阶梯度与二阶梯度的空间结构稀疏性和三维数据层间相关性,可有效抑制稀疏角度重建图像中的伪影与噪声,并较好保持图像边缘信息。结果XCAT体模数据与病人扫描数据的实验结果表明,TTGV-POCS算法相比现有重建算法在降低噪声、去除伪影和保持边缘等方面均有较好的表现;比较XCAT体模数据稀疏角度重建结果,本文提出的TTGV-POCS算法相比现有重建算法PSNR定量指标可提升9.17%~15.24%;FSIM定量指标可提升1.27%~9.30%。结论TTGV-POCS算法可有效改善稀疏角度螺旋CT重建图像质量,降低螺旋CT检查辐射剂量,更好服务于临床影像诊断。
Objective We propose a sparse-view helical CT iterative reconstruction algorithm based on projection of convex set tensor total generalized variation minimization(TTGV-POCS)to reduce the X-ray dose of helical CT scanning.Methods The three-dimensional volume data of helical CT reconstruction was viewed as the third-order tensor.The tensor generalized total variation(TTGV)was used to describe the structural sparsity of the three-dimensional image.The POCS iterative reconstruction framework was adopted to achieve a robust result of sparse-view helical CT reconstruction.The TTGV-POCS algorithm fully used the structural sparsity of first-order and second-order derivation and the correlation between the slices of helical CT image data to effectively suppress artifacts and noise in the image of sparse-view reconstruction and better preserve image edge information.Results The experimental results of XCAT phantom and patient scan data showed that the TTGVPOCS algorithm had better performance in reducing noise,removing artifacts and maintaining edges than the existing reconstruction algorithms.Comparison of the sparse-view reconstruction results of XCAT phantom data with 144 exposure views showed that the TTGV-POCS algorithm proposed herein increased the PSNR quantitative index by 9.17%-15.24%compared with the experimental comparison algorithm;the FSIM quantitative index was increased by 1.27%-9.30%.Conclusion The TTGV-POCS algorithm can effectively improve the image quality of helical CT sparse-view reconstruction and reduce the radiation dose of helical CT examination to improve the clinical imaging diagnosis.
作者
谌高峰
王永波
边兆英
韦子权
邓耀宏
李明强
马昆
陶熙
李彬
马建华
黄静
CHEN Gaofeng;WANG Yongbo;BIAN Zhaoying;WEI Ziquan;DENG Yaohong;LI Mingqiang;MA Kun;TAO Xi;LI Bin;MA Jianhua;HUANG Jing(School of Biomedical Engineering,Southern Medical University//Guangzhou Key Laboratory of Medical Radioimaging and Detection Technology,Guangzhou 510515,China)
出处
《南方医科大学学报》
CAS
CSCD
北大核心
2019年第10期1213-1220,共8页
Journal of Southern Medical University
基金
国家自然科学基金(U1708261,81701690,61571214,61701217)
广东省应用型科技研发专项(2015B020233008)
广州市科技计划项目(201705030009)
广东省科技计划项目(2017B020229004)~~
关键词
螺旋CT
稀疏角度
张量广义全变分
凸集投影
helical CT
sparse-view
tensor total generalized variation
projection on convex set
