基于图割的肺4D-CT图像超分辨率重建  

Super- resolution reconstruction for 4-dimensional computed tomography of the lung using graph cuts

作　　者：陈瑾[1,2] 申正文 席卫文[1,2] 张煜[1,2] CHEN Jin SHEN Zhengwen XI Weiwen ZHANG Yu(School of Biomedical Engineering, 2Guangdong Provincial Key Laboratory of Medical Images, Southern Medical University, Guangzhou 510515, China)

机构地区：[1]南方医科大学生物医学工程学院,广东广州510515 [2]南方医科大学广东省医学图像重点实验室,广东广州510515

出　　处：《南方医科大学学报》2016年第9期1260-1264,共5页Journal of Southern Medical University

基　　金：国家自然科学基金(31271067);广东省自然科学基金(S2013010014049);广州市科技计划(201607010097)~~

摘　　要：肺4D-CT在当今的肺癌放射治疗中起着重要的作用。本文提出了一种基于全局图割方法的肺4D-CT图像超分辨率重建方法,来提升肺4D-CT图像的质量。该方法首先在最大后验马尔科夫随机场框架下建立一个肺4D-CT各相位高分辨率图像重建的全局能量函数,然后,将该能量函数转化成图的表达方式,最后用图割方法和α-βswap算法优化能量函数来恢复高分辨率图像细节结构。实验结果表明,在恢复图像的细节方面,本文方法要优于传统的线性插值和凸集投影超分辨率重建算法。Four-dimensional computer tomography （4D-CT） has a great value in lung cancer radiotherapy for its capability in providing lung information with respiratory motion. We employed a global graph cuts super-resolution （SR） reconstruction method to reconstruct high-resolution lung 4D-CT images. First, the high-resolution images reconstruction energy function was built based on a Maximum a posteriori Markov Random Field （MAP-MRF） formulation. The energy function was then transformed to a graph formulation, which was solved using graph cut algorithm. All the evaluation results showed that this approach outperformed the line interpolation and projection onto convex sets （POCS） approach with an improved structural clarity.

关 键 词：肺4D-CT 超分辨率重建 图割 α-βswap 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]