ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2017, Vol. 54 ›› Issue (8): 1703-1712.doi: 10.7544/issn1000-1239.2017.20170346

所属专题: 2017人工智能前沿进展专题

• 人工智能 • 上一篇    下一篇

基于变分光流估计的肺部4D-CT图像超分辨率重建

耿凤欢1,2,刘慧1,2,郭强1,2,尹义龙1,3   

  1. 1(山东财经大学计算机科学与技术学院 济南 250014);2(山东省数字媒体技术重点实验室 济南 250014);3(山东大学计算机科学与技术学院 济南 250013) (gfh0928@outlook.com)
  • 出版日期: 2017-08-01
  • 基金资助: 
    国家自然科学基金项目(61572286,61472220);山东省重点研发项目(2014GGX101037);济南市高校自主创新项目(201401216);山东省高校优势学科人才团队培育项目

Variational Optical Flow Estimation Based Super-Resolution Reconstruction for Lung 4D-CT Image

Geng Fenghuan1,2, Liu Hui1,2, Guo Qiang1,2, Yin Yilong1,3   

  1. 1(College of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014);2(Shandong Provincial Key Laboratory of Digital Media Technology, Jinan 250014);3(College of Computer Science and Technology, Shandong University, Jinan 250013)
  • Online: 2017-08-01

摘要: 由于受到扫描时间和照射剂量的限制,肺部4D-CT数据中纵向采样率远小于面内采样率.为了得到更高质量的肺部图像,从医学图像固有的自相似性出发,提出了一种基于局部和全局相结合的变分光流估计的图像序列超分辨率重建技术,用于提高4D-CT图像重建质量.首先,构建了一个用于求解肺部4D-CT不同相位图像之间的光流场的变分光流模型;然后,利用快速交替方向乘子法求解该模型,得到不同相位图像之间的光流场;最后,基于光流场,并利用非局部迭代反投影超分辨率重建算法,实现了高分辨率肺部图像的重建.实验结果表明:与已有算法相比,本方法在增强图像纹理结构的同时更好地保留了图像的轮廓.

关键词: 4D-CT图像, 超分辨率重建, 光流估计, 交替方向乘子法, 迭代反投影

Abstract: The acquisition of lung 4D computed tomography (4D-CT) data is limited by the scanning time and radiation dose, which leads to the sampling rate in the axial direction is much less than that in the in-plane direction. In order to get better quality of 4D-CT images, based on the inherent self-similarity of medical images, a new method of image sequence super-resolution reconstruction is proposed in this paper. This method uses the local and global variational optical flow estimation to improve the quality of enlarged 4D-CT image. Firstly, we present a combined local and global variational optical flow model, in order to estimate the motion fields (i.e., the optical flow fields) between different phases in the corresponding positions. Then, the optical flow field is obtained by solving the model with the fast alternating direction method of multiplier. Finally, according to the calculated motion fields, we employ the improved non-local iterative back projection (NLIBP) algorithm to reconstruct high resolution lung images. The experimental results have shown that, in both quantification standard and visual perception, this method outperforms non-local iterative back projection algorithm and full search block matching based iterative back projection technique. Furthermore, our method can generate clear edges while enhancing the texture of images.

Key words: 4D-CT image, super-resolution reconstruction, optical flow estimation, alternating direction method of multipliers, iterative back projection

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