[1]王婷婷,张煜. 基于图像自相似性的多尺度稀疏表示肺4D-CT图像超分辨率重建[J].中国医学物理学杂志,2019,36(6):658-666.[doi:DOI:10.3969/j.issn.1005-202X.2019.06.008]
　WANG Tingting,ZHANG Yu. Lung 4D-CT image super-resolution reconstruction using self-similarity-based multiscale sparse representation[J].Chinese Journal of Medical Physics,2019,36(6):658-666.[doi:DOI:10.3969/j.issn.1005-202X.2019.06.008]
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 基于图像自相似性的多尺度稀疏表示肺4D-CT图像超分辨率重建()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
36卷
期数:
2019年第6期
页码:
658-666
栏目:
医学影像物理
出版日期:
2019-06-25

文章信息/Info

Title:
 Lung 4D-CT image super-resolution reconstruction using self-similarity-based multiscale sparse representation
文章编号:
R318
作者:
 王婷婷1; 张煜2
 1.南方医科大学南方医院设备器材科， 广东 广州 510515； 2.南方医科大学生物医学工程学院， 广东 广州 510515
Author(s):
 WANG Tingting1;  ZHANG Yu2
 1. Department of Medical Equipment, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; 2. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
关键词:
 ; 肺; 四维计算机断层摄影; 超分辨率重建; 图像自相似性; 多尺度分析; 稀疏表示
Keywords:
 ; Keywords: lung;  four-dimensional computed tomography;  super-resolution reconstruction;  image self-similarity;  multiscale analysis;  sparse representation
分类号:
1005-202X（2019）06-0658-09
DOI:
DOI:10.3969/j.issn.1005-202X.2019.06.008
文献标志码:
A
摘要:
 针对肺四维计算机断层摄影（4D-CT）数据层间分辨率远低于层内分辨率的各项数据异性问题，提出一种基于图像自相似性的多尺度稀疏表示超分辨率重建方法，用以提升肺4D-CT图像的分辨率。所提出的方法通过探索验证肺4D-CT数据横断面与冠矢状面图像的自相似性，以横断面图像代替冠矢状面图像，组成高分辨率和低分辨率图像块对，构建训练集；引入多尺度策略，根据四叉树原理划分得到不同尺度的图像块，训练得出全局多尺度字典，以捕获更多肺部解剖结构特征；通过稀疏表示超分辨算法重建得出冠矢状面的高分辨率图像。利用仿真数据和真实数据对提出的算法进行实验验证，量化评价与视觉效果均体现本研究算法在提升图像清晰度以及改善图像细微结构显示质量方面的优越性，同时还可有效避免对算法精度及耗时产生影响的运动估计过程。
Abstract:
 Abstract: Based on the anisotropy that the interlayer resolution of lung four-dimensional computed tomography (4D-CT) image is far lower than the intralayer resolution, a self-similarity-based multiscale sparse representation for super-resolution reconstruction is developed to enhance the resolution of lung 4D-CT image. The self-similarity of transverse and coronal/sagittal lung 4D-CT images is explored with the proposed approach. Based on the validity of cross-scale self-similarity, the transverse images of lung 4D-CT data instead of coronal/sagittal images are utilized as training database for learning the mapping between low-resolution and high-resolution image patches. Subsequently, multiscale strategy is introduced. The image patches of different scales are obtained based on quad-tree decomposition. After training, multiscale global dictionaries are obtained for capturing more anatomical features. Finally, the coronal/sagittal high-resolution images are reconstructed by sparse representation based super-resolution algorithm. Herein the simulated and real data are employed to verity the effectiveness of the proposed approach. Both quantitative evaluation and visual results reveal that the proposed approach has good performances in generating clear image with significantly enhanced structures, and meanwhile effectively avoids the process of motion evaluation which restricts the efficiency and accuracy of reconstructed result.

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备注/Memo

备注/Memo:
 【收稿日期】2019-02-11
【基金项目】国家自然科学基金（61671230）；广东省科技攻关项目（2017A020211012）；广州市科技计划项目（201607010097）
【作者简介】王婷婷，助理工程师，研究方向：医学图像处理、医疗设备质控，E-mail: 458864374@qq.com
【通信作者】张煜，教授，博士生导师，研究方向：医学图像处理，E-mail: 526933560@qq.com
更新日期/Last Update: 2019-06-25