基于时间串流的OSEM图像重建算法研究  

OSEM image reconstruction algorithm based on time streaming

作　　者：徐蕾 赵敏[1] 郭瑞鹏[1] 姚敏[1] 单尧[1] Xu Lei;Zhao Min;Guo Ruipeng;Yao Min;Shan Yao(College of Automation Eingineering,Nanjing Unirersily of Aeronautics and Astronautics,Nanjing 211106,China)

机构地区：[1]南京航空航天大学自动化学院,南京211106

出　　处：《仪器仪表学报》2022年第6期194-204,共11页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(62071229,51875289,61873124);航空基金(2020Z060052001);研究生创新基金(kycx21_0203,xcxjh20210302)项目资助。

摘　　要：利用γ光子探测腔体内部动态流场需要快速的图像重建算法,传统处理方式是先采集所有事件、再进行OSEM等算法处理。本文提出了一种按时间流对响应事件进行子集划分的图像重建(T-OSEM)算法。在连续采样数据的同时,按时间段将采样到的数据划分为子采样数据集,对子集进行OSEM迭代实现图像重建。并将上一帧图像作为迭代输入,利用帧间图像相关性,以加快收敛速度。该算法中数据流的采样与上一帧图像的处理同时进行,并通过多线程并行运算加速图像重建过程。研究了最优子集事件数量及相对应采样时间的关系,以实现在尽可能短的采样时间下达到最优的重建效果。实验表明,当采样时间段达到1 s时,T-OSEM算法仍有很好的粒子跟踪效果,粒子轨迹图像结构相似比为0.92,表明T-OSEM算法对于动态图像重建是一个比较好的解决方案。Using γ photons to detect dynamic flow fields inside a cavity requires a fast image reconstruction algorithm. The traditional processing method is to collect all events first. Then, the algorithm processing is performed, such as OSEM. This study proposes an image reconstruction(T-OSEM) algorithm that subdivides the response events according to the time stream. At the same time of continuous sampling data, the sampled data are divided into sub-sampling data sets according to the time period, and OSEM iteration is performed on the subset to achieve image reconstruction. The previous frame image is taken as the iterative input, and the correlation between frames is used to accelerate the convergence of iterative operation. The sampling of the data stream in T-OSEM is carried out simultaneously with the processing of the previous image frame. The image reconstruction process is accelerated by the multithreaded parallel operation. The relationship between the optimal number of subset events and the corresponding sampling time is studied to achieve the optimal reconstruction effect under the shortest sampling time. Experiments show that when the sampling time period reaches 1 s, there is still a good particle tracking effect, and the mean structural similarity of the particle trajectory image is 0.92. Results indicate that the T-OSEM algorithm is a good solution for dynamic image reconstruction.

关 键 词：γ光子 T-OSEM 流体状态检测 动态粒子跟踪 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TH878[自动化与计算机技术—计算机科学与技术]