基于全变分α散度最小化的PET优质重建

doi:10.3969/j.issn.0372-2112.2012.06.033

电子学报

基于全变分α散度最小化的PET优质重建

田玲玲¹, 黄静¹, 马建华¹, 路利军¹, 边兆英¹, 张华¹, 高杨¹, 喻高航², 陈武凡¹

1. 南方医科大学生物医学工程学院医学信息研究所,广东广州 510515;
2. 赣南师范学院数学与计算机科学学院,江西赣州 341000

收稿日期:2011-05-29 修回日期:2011-12-29 出版日期:2012-06-25
- 通讯作者:
- 黄静 女,1977年生于湖北嘉鱼,南方医科大学生物医学工程学院讲师,博士,研究方向:低剂量CT成像技术. E-mail:hjing@fimmu.com
- 作者简介:
- 田玲玲 女,1987年6月出生于河南睢县,目前在南方医科大学生物医学工程学院攻读医学图像处理方向硕士学位,主要研究方向为医用PET成像技术. E-mail:tian7515@fimmu.com
- 基金资助:
- 国家自然科学基金 (No.81101046,No.81000613,No.11001060); 国家"九七三"重点基础研究发展计划项目 (No.2010CB732503); 国家科技支撑计划项目 (No.2011BAI12B03); 国家重大仪器专项 (No.2011YQ03011404); 广东省科技计划项目 (No.2011A030300005); 江西省青年科学家培养对象计划项目 (No.20112BCB23027)

10.3969/j.issn.0372-2112.2012.06.033 Total Variation Based α-Divergence Minimization Reconstruction for Positron Emission Tomography

TIAN Ling-ling¹, HUANG Jing¹, MA Jian-hua¹, LU Li-jun¹, BIAN Zhao-ying¹, ZHANG Hua¹, GAO Yang¹, YU Gao-hang², CHEN Wu-fan¹

1. School of Biomedical Engineering,Institute of Medical Information & Technology,Southern Medical University,Guangzhou, Guangdong 510515,China;
2. School of Mathematics and Computer Science,Gannan Normal University,Ganzhou,Jiangxi 341000,China

Received:2011-05-29 Revised:2011-12-29 Online:2012-06-25 Published:2012-06-25
- Supported by:
- National Natural Science Foundation of China (No.81101046, No.81000613, No.11001060); Program of Major State Basic Research Development Program of China (973 Program) (No.2010CB732503); Project of National Key Technology R&D Program (No.2011BAI12B03); National Key Instrument and Equipment Development Project (No.2011YQ03011404); Science and Technology Project of Guangdong Province (No.2011A030300005); Young Student Scientist Program of Jiangxi Province (No.20112BCB23027)

摘要/Abstract

摘要： 为了获得优质的PET成像,本文提出一种基于全变分阿尔法散度最小化的PET重建新方法.新方法通过引入阿尔法散度度量投影数据和估计值之间的偏差;通过增加全变分正则化修正阿尔法散度最小化解的一致性.针对新构建的PET重建目标函数的求解,本文提出一种基于次梯度理论的交替式迭代策略,期间运用自适应非单调线性搜索来保证算法的收敛性.仿真和临床PET数据实验表明,本文方法在噪声抑制和边缘保持方面均优于传统的PET重建方法.

关键词: 正电子发射成像, 阿尔法散度, 全变分, 自适应非单调线性搜索

Abstract: To achieve high diagnostic PET imaging,we propose a novel total variation (TV) based alpha-divergence minimization reconstruction algorithm.The presented cost function uses the alpha-divergence to measure the discrepancy between the measured and the estimated emission projection data and utilizes the TV regularization to regularize the consistency of solution.A semi-implicit iteration scheme is used in the proposed algorithm by adapting the subgradient theory; and then an adaptive nonmonotone line search scheme is taken to guarantee the algorithm convergence.The experiments from the simulated phantom data and the real emission data show that the presented algorithm performs better than the other classical PET reconstruction methods in the noise suppressing and the edge details preserving.

Key words: positron emission tomography (PET), alpha-divergence, total variation, adaptive nonmonotone line search

中图分类号:

R391.4

田玲玲, 黄静, 马建华, 路利军, 边兆英, 张华, 高杨, 喻高航, 陈武凡. 基于全变分α散度最小化的PET优质重建[J]. 电子学报, DOI: 10.3969/j.issn.0372-2112.2012.06.033.

TIAN Ling-ling, HUANG Jing, MA Jian-hua, LU Li-jun, BIAN Zhao-ying, ZHANG Hua, GAO Yang, YU Gao-hang, CHEN Wu-fan. 10.3969/j.issn.0372-2112.2012.06.033 Total Variation Based α-Divergence Minimization Reconstruction for Positron Emission Tomography[J]. Acta Electronica Sinica, DOI: 10.3969/j.issn.0372-2112.2012.06.033.

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基于全变分α散度最小化的PET优质重建

10.3969/j.issn.0372-2112.2012.06.033 Total Variation Based α-Divergence Minimization Reconstruction for Positron Emission Tomography

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