High quality ordered subset expectation maximization reconstruction algorithm based on multi-resolution for PET images

doi:10.3724/SP.J.1087.2013.00648

Journal of Computer Applications ›› 2013, Vol. 33 ›› Issue (03): 648-650.DOI: 10.3724/SP.J.1087.2013.00648

• Network and distributed techno • Previous Articles Next Articles

High quality ordered subset expectation maximization reconstruction algorithm based on multi-resolution for PET images

ZHANG Quan¹, FU Xuejing², LI Xiaohong^1*, GUI Zhiguo¹

1.School of Information and Communication Engineering, North University of China, Taiyuan Shanxi 030051, China;
2.Shanghai Chang'an Information Technology Consultation and Development Center, Shanghai 200032, China

Received:2012-09-03 Revised:2012-10-31 Online:2013-03-01 Published:2013-03-01
Contact: LI Xiao-hong

基于多分辨率的PET图像优质有序子集最大期望重建算法

张权¹,付学敬²,李晓红^1*,桂志国¹

1.中北大学信息与通信工程学院,太原 030051;
2.上海长安信息技术咨询开发中心,上海 200032

通讯作者: 李晓红
作者简介:张权(1974-),男,山西大同人,讲师,博士研究生,主要研究方向:图像处理、科学可视化; 付学敬(1971-),男,天津人,工程师,硕士,主要研究方向:计算机信息处理; 李晓红(1986-),女,河南商丘人,硕士研究生,主要研究方向:医学图像处理与重建; 桂志国(1972-),男,天津人,教授,博士,主要研究方向:图像处理与重建。
基金资助:
国家自然科学基金资助项目(61071192, 61271357); 山西省自然科学基金资助项目(2009011020-2); 山西省高等学校优秀青年学术带头人支持计划项目。

Abstract

Abstract: In Positron Emission Tomography (PET) imaging, Maximum Likelihood Expectation Maximization (MLEM) algorithm cannot be directly applied to clinical diagnosis due to suppressing noise ineffectively and converging slowly. Although Ordered Subset Expectation Maximization (OSEM) algorithm converges fast, it will lead to a significant decline in the quality of the reconstructed image. To address this problem, multi-resolution technology was introduced into the subset of the OSEM reconstruction algorithm to suppress noise and stabilize solving process. The experimental results indicate that the new algorithm overcomes the drawback of the traditional algorithm on degrading the reconstructed image and has the advantage of fast convergence. The proposed reconstruction algorithm can obtain a higher Signal-to-Noise Ratio (SNR) and a superior visual effect.

Key words: Positron Emission Tomography (PET) imaging, Maximum Likelihood Expectation Maximization (MLEM), Ordered Subset Expectation Maximization (OSEM), multi-resolution, wavelet shrinkage

摘要： 在正电子发射断层扫描(PET)成像中,最大似然期望(MLEM)算法因不能有效地抑制噪声、收敛速度慢而难以直接应用于临床。有序子集最大期望(OSEM)算法具有较快的收敛速度,但是在迭代一定次数之后重建质量会迅速下降。针对此问题,将多分辨率技术引入到有序子集最大期望重建算法的子集中,以此抑制噪声,同时稳定求解过程。实验结果表明,新的重建算法克服了传统算法图像退化的缺点,并具有加快算法收敛速度的优点,能获得较高的信噪比(SNR)和较好的图像视觉效果。

关键词: 正电子发射断层扫描成像, 最大似然期望, 有序子集最大期望, 多分辨率, 小波收缩

CLC Number:

TP391.41

ZHANG Quan FU Xuejing LI Xiaohong GUI Zhiguo. High quality ordered subset expectation maximization reconstruction algorithm based on multi-resolution for PET images[J]. Journal of Computer Applications, 2013, 33(03): 648-650.

张权付学敬李晓红桂志国. 基于多分辨率的PET图像优质有序子集最大期望重建算法[J]. 计算机应用, 2013, 33(03): 648-650.

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High quality ordered subset expectation maximization reconstruction algorithm based on multi-resolution for PET images

基于多分辨率的PET图像优质有序子集最大期望重建算法

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