摘要
提出了一种稀疏张量约束重建算法,该方法利用非局部相似的先验信息,将CT图像分割成一系列图像块组;采用张量的多维低秩分解方法,将这一先验信息引入低剂量CT重建中,构造目标函数;通过重建图像更新和图像块组张量稀疏编码两个步骤,交替迭代求解目标函数。基于仿真数据和临床数据的实验结果验证了该算法的有效性,实验结果表明:与经典重建算法相比,所提算法在抑制噪声的同时,能更好地保持重建图像的细节,获得更高质量的图像。
We develop a sparse tensor constrained reconstruction(STCR)algorithm which utilizes the nonlocal similarity prior information and divides the computed tomography(CT)image into a series of patch groups.The multidimensional low-rank decomposition method for tensors is used,and the prior information is introduced in the low dose computed tomography(LDCT)reconstruction to establish an object function.The object function is optimized by alternating iteration of the CT reconstruction image update step and the patch group sparse coding step in the iterative process.The performance of the STCR algorithm is verified through experiments based on simulation data and clinical data.Preliminary experimental results show that,compared to the classical reconstruction methods,the proposed method can produce better images in terms of structure preservation and noise suppression.
作者
刘进
亢艳芹
顾云波
陈阳
Liu Jin;Kang Yanqin;Gu Yunbo;Chen Yang(College of Computer and Information,Anhui Polytechnic University,Wuhu,Anhui 241000,China;Laboratory of Image Science and Technology,Southeast University,Nanjing,Jiangsu 210096,China;Key Laboratory of Computer Network and Information Integration (Southeast University),Ministry of Education,Nanjing,Jiangsu 210096,China;School of Cyber Science and Engineering,Southeast University,Nanjing,Jiangsu 210096,China)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2019年第8期159-168,共10页
Acta Optica Sinica
基金
国家自然科学基金(61801003)
安徽工程大学引进人才科研启动基金(2018YQQ021)
关键词
成像系统
计算机断层扫描
低剂量
图像重建
稀疏表示
张量约束
imaging systems
computed tomography
low dose
image reconstruction
sparse representation
tensor constraint
