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

TIAN Ling-ling; HUANG Jing; MA Jian-hua; LU Li-jun; BIAN Zhao-ying; ZHANG Hua; GAO Yang; YU Gao-hang; CHEN Wu-fan

doi:10.3969/j.issn.0372-2112.2012.06.033

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

更新时间：2025-07-16

- 10.3969/j.issn.0372-2112.2012.06.033 Total Variation Based α-Divergence Minimization Reconstruction for Positron Emission Tomography
- Acta Electronica Sinica Vol. 40, Issue 6, Pages: 1263-1268(2012)
- 作者机构：
  
  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
  3. 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
  4. 赣南师范学院数学与计算机科学学院江西赣州,341000
- 作者简介：
- 基金信息：
  
  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)
- DOI：10.3969/j.issn.0372-2112.2012.06.033
  CLC： R391.4
- Published：2012
- 稿件说明：
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TIAN Ling-ling, HUANG Jing, MA Jian-hua, et al. 10.3969/j.issn.0372-2112.2012.06.033 Total Variation Based α-Divergence Minimization Reconstruction for Positron Emission Tomography[J]. Acta Electronica Sinica, 2012, 40(6): 1263-1268.
DOI：

TIAN Ling-ling, HUANG Jing, MA Jian-hua, et al. 10.3969/j.issn.0372-2112.2012.06.033 Total Variation Based α-Divergence Minimization Reconstruction for Positron Emission Tomography[J]. Acta Electronica Sinica, 2012, 40(6): 1263-1268. DOI： 10.3969/j.issn.0372-2112.2012.06.033.

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.

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.

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Image Mixed Denoising Using Quaternion-Based Non-Local Low Rank and Total Variation

Generalized Gibbs Priors in Positron Emission Tomography

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Related Institution

College of Electrical and Information Engineering, Hunan University

Department of Computer and Information Science, University of Macau, Macau

（南方医科大学生物医学工程学院医学信息研究所，）

College of Electrical and Information Engineering， Hunan University

Department of Computer and Information Science， University of Macau， Macau

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