基于相关系数和双向扩散结合的优质正电子发射断层重建算法

doi:10.11772/j.issn.1001-9081.2014.05.1482

计算机应用 ›› 2014, Vol. 34 ›› Issue (5): 1482-1485.DOI: 10.11772/j.issn.1001-9081.2014.05.1482

基于相关系数和双向扩散结合的优质正电子发射断层重建算法

上官宏¹,刘祎¹,张权¹,桂志国¹,²

1. 中北大学电子测试技术国防重点实验室，太原 030051；
2. 仪器科学与动态测试教育部重点实验室（中北大学），太原 030051

收稿日期:2013-10-31 修回日期:2013-12-24 发布日期:2014-05-30 出版日期:2014-05-01
通讯作者: 上官宏
作者简介:上官宏(1988-)，女，山西临汾人，博士研究生，主要研究方向：图像处理、医学图像重建；刘祎(1987-)，女，河南商丘人，博士研究生，主要研究方向：图像处理、医学图像重建；张权(1974 -)，男，山西大同人，副教授，博士研究生，主要研究方向：图像处理、图像重建；桂志国(1972-)，男，天津人，教授，博士，主要研究方向：无损检测、图像处理与重建。
基金资助:
国家自然科学基金资助项目;国家自然科学基金资助项目;山西省研究生优秀创新项目;山西省研究生优秀创新项目;山西省国际合作项目

High quality positron emission tomography reconstruction algorithm based on correlation coefficient and forward-and-backward diffusion

SHANG Guanhong¹,LIU Yi¹,ZHANG Quan¹,GUI Zhiguo¹,²

1. National Key laboratory for Electronic Measurement Technology, North University of China, Taiyuan Shanxi 030051, China;
2. Key Laboratory of Instrumentation Science and Dynamic Measurement, Ministry of Education (North University of China), Taiyuan Shanxi 030051, China

Received:2013-10-31 Revised:2013-12-24 Online:2014-05-30 Published:2014-05-01
Contact: SHANG Guanhong
Supported by:
National Natural Science Foundation;National Natural Science Foundation

摘要/Abstract

摘要：

在正电子发射断层成像（PET）中，传统迭代算法会造成重建图像细节信息丢失或目标边界模糊。为了解决上述问题，提出一种基于相关系数和双向扩散结合的优质中值先验（MP）重建算法。首先，引入特征因子相关系数来表征图像局部灰度统计信息，构造出结合相关系数的双向扩散模型；其次，考虑到双向模型对背景和边缘区别处理的优点，将新模型应用到中值先验分布的最大后验重建算法中，形成基于双向扩散的中值先验重建算法。实验结果表明，该算法在去除噪声的同时能够较好地保持图像中的目标边界信息，信噪比（SNR）和均方误差（RMSE）的变化也能直观体现重建图像质量的提高。

Abstract:

In Positron Emission Tomography (PET) computed imaging, traditional iterative algorithms have the problem of details loss and fuzzy object edges. A high quality Median Prior (MP) reconstruction algorithm based on correlation coefficient and Forward-And-Backward (FAB) diffusion was proposed to solve the problem in this paper. Firstly, a characteristic factor called correlation coefficient was introduced to represent the image local gray information. Then through combining the correlation coefficient and forward-and-backward diffusion model, a new model was made up. Secondly, considering that the forward-and-backward diffusion model has the advantages of dealing with background and edge separately, the proposed model was applied to Maximum A Posterior (MAP) reconstruction algorithm of the median prior distribution, thus a median prior reconstruction algorithm based on forward-and-backward diffusion was obtained. The simulation results show that, the new algorithm can remove the image noise while preserving object edges well. The Signal-to-Noise Ratio (SNR) and Root Mean Squared Error (RMSE) also show visually the improvement of the reconstructed image quality.

中图分类号:

TP391

上官宏刘祎张权桂志国. 基于相关系数和双向扩散结合的优质正电子发射断层重建算法[J]. 计算机应用, 2014, 34(5): 1482-1485.

SHANG Guanhong LIU Yi ZHANG Quan GUI Zhiguo. High quality positron emission tomography reconstruction algorithm based on correlation coefficient and forward-and-backward diffusion[J]. Journal of Computer Applications, 2014, 34(5): 1482-1485.

参考文献

［1］KARAKATSANIS N A, LODGE M A, TAHARI A K, et al. Dynamic whole-body PET parametric imaging: Ⅰ. Concept, acquisition protocol optimization and clinical application ［J］. Physics in Medicine and Biology, 2013,58(20):7391.
［2］GERAMIFAR P, ZAFARGHANDI M S, GHAFARIAN P, et al. Respiratory-induced errors in tumor quantification and delineation in CT attenuation-corrected PET images: effects of tumor size, tumor location, and respiratory trace: a simulation study using the 4D XCAT phantom ［J］. Molecular Imaging and Biology, 2013,15(6):655-665.
［3］CHEN Y. High quality PET image reconstruction based on different prior acquisitions ［D］. Guangzhou: Southern Medical University, 2007.(陈阳.基于不同先验获取的 PET 图像优质重建新方法研究［D］.广州:南方医科大学,2007.)
［4］CHEN Y, CHEN W, FENG Y, et al. Convergent Bayesian reconstruction for PET using new MRF quadratic membrane-plate hybrid multi-order prior ［C］// MIAR '06: Proceedings of the 3rd International Conference on Medical Imaging and Augmented Reality. Berlin: Springer-Verlag, 2006:309-316.
［5］ALENIUS S, RUOTSALAINEN U. Bayesian image reconstruction for emission tomography based on median root prior ［J］. European Journal of Nuclear Medicine, 1997,24(3):258-265.
［6］ALENIUS S, RUOTSALAINEN U. Generalization of median root prior reconstruction ［J］. IEEE Transactions on Medical Imaging, 2002,21(11):1413-1420.
［7］HSIAO I T, RANGARAJAN A, GINDI G. A new convex edge-preserving median prior with applications to tomography ［J］. IEEE Transactions on Medical Imaging, 2003,22(5):580-585.
［8］YAN J. Investigation of positron emission tomography image reconstruction［D］.Wuhan: Huazhong University of Science and Technology, 2007.(颜建华.正电子发射断层图像重建算法研究［D］.武汉:华中科技大学, 2007.)
［9］GUI Z, LIU Y, HE J. PML algorithm for positron emission tomography combined with nonlocal fuzzy anisotropic diffusion filtering ［J］. IEEE Transactions on Nuclear Science, 2012,59(5):1984-1989.
［10］SHEPP L A, VARDI Y. Maximum likelihood reconstruction for emission tomography ［J］. IEEE Transactions on Medical Imaging, 1982,1(2):113-122.
［11］WHITAKER R T, PIZER S M. A multi-scale approach to nonuniform diffusion ［J］. CVGIP: Image Understanding, 1993,57(1):99-110.
［12］GILBOA G, SOCHEN N, ZEEVI Y Y. Forward-and-backward diffusion processes for adaptive image enhancement and denoising ［J］. IEEE Transactions on Image Processing, 2002,11(7): 689-703.
［13］WANG Y, NIU R, ZHANG L, et al. A scale-based forward-and-backward diffusion process for adaptive image enhancement and denoising ［J］. EURASIP Journal on Advances in Signal Processing, 2011,2011:1-19.
［14］WELK M, GILBOA G, WEICKERT J. Theoretical foundations for discrete forward-and-backward diffusion filtering ［C］// SSVM '09: Proceedings of the 2nd International Conference on Scale Space and Variational Methods in Computer Vision. Berlin: Springer-Verlag, 2009:527-538.
［15］LI X, ZHANG Q, LIU Y, et al. High quality MP image reconstruction based on wavelet shrinkage and forward-and-backward diffusion ［J］. Journal of Computer Applications, 2012,32(12):3357-3360.(李晓红,张权,刘祎,等.基于小波收缩和正逆扩散结合的优质中值先验图像重建算法［J］.计算机应用,2012,32(12):3357-3360.)
［16］MA X. Research of positron emission tomography statistical iterative reconstruction algorithm ［D］. Taiyuan: North University of China, 2011.(马小波.正电子发射断层成像统计迭代重建算法研究［D］.太原:中北大学, 2011.)

[1]	张佳慧李晓明张嘉祥. 强化形态感知的路面缺陷检测算法[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[2]	杨建锋陈斌李雨轩. 基于点云重构的自监督点云异常检测方法[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[3]	薛振华李强黄超. 视觉大模型驱动的像素级图像异常检测模型[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[4]	蒋畅江向杰何旭颖. 面向机械臂抓取的双目视觉目标定位算法[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[5]	边小勇胡其仁袁培洋. 多注意力对比学习的红外小目标检测[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[6]	李钟华钟庚辛范萍朱恒亮. 通过边界挖掘和背景引导的伪装目标检测[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[7]	吴松霖张广朝姚远彭博. 基于判别区域引导的多视图困难气道识别[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[8]	李强白少雄熊源袁薇. 基于视觉大模型隐私保护的监控图像定位[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[9]	薛雅丽徐忠敏刘世豪. 基于多级小波残差网络的重力数据去噪方法[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[10]	况世雄姚俊波陆佳炜王琪冰肖刚. 基于动态图卷积网络的电梯乘客异常行为数据增强方法[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[11]	康斌陈斌王俊杰李昱林赵军智咸伟志. 基于多粒度共享语义中心关联的文本到人物检索方法[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[12]	张庆杨凡方宇涵. 基于多模态信息融合的中文拼写纠错算法[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[13]	王昊王金伟程鑫张家伟吴昊罗向阳马宾. 彩色图像JPEG重压缩取证综述[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[14]	王磊胡节彭博. 用于半监督火灾检测的分布自适应和动态课程伪标签框架[J]. 《计算机应用》唯一官方网站, 0, (): 0-0.
[15]	刘晋文王磊马博董瑞杨雅婷艾合塔木江·艾合麦提王欣乐. 基于弱监督模态语义增强的多模态有害信息检测方法 [J]. 《计算机应用》唯一官方网站, 0, (): 0-0.

基于相关系数和双向扩散结合的优质正电子发射断层重建算法

High quality positron emission tomography reconstruction algorithm based on correlation coefficient and forward-and-backward diffusion

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics