摘要
正电子发射断层扫描(PET)是一种功能性核医学成像设备,已广泛应用于临床检验和临床前研究。其核心探测器主要采用闪烁晶体阵列耦合光电器件阵列的模块化设计。该类型探测器需要对其泛场图像进行分割,制作晶体位置查找表。本文开发了一种针对双层错位的DOI-PET探测器的泛场图像晶体响应中心自动识别和分割算法。基于奇异值分解和均值漂移的算法实现顶层晶体中心的识别;基于自组织映射(SOM)神经网络的算法和均值漂移实现底层晶体中心的识别;采用基于欧氏距离的算法,实现了泛场图像晶体单元的分割。将本文所开发的算法用于整环(48张)PET泛场图像,晶体模块中心识别的准确率为99.34%,完成分割整张泛场图像的平均耗时为101 s。测试结果表明,本文所开发的泛场图像晶体响应中心自动识别和分割算法适用于双层错位的DOI-PET探测器,算法鲁棒性强、准确率高、运算速度快。
The positron emission tomography(PET)is a nuclear medicine device for molecular,metabolic and functional imaging,which is extensively used in nuclear medicine for clinical examination and pre-clinical research.The key component of a PET device is the gamma-ray detectors,which commonly consist of scintillator arrays coupled to photon sensor arrays.This type of detector needs to segment its flood source image to generate a crystal position look-up table(LUT).The accuracy of the LUT is critical to the system performance.For a whole PET system,the number of detector blocks may be hundreds,thus it will be time consuming if the process is done by manual segmentation.An automatic algorithm for the crystal recognition and segmentation of flood maps generated by an animal PET system with depth of interaction(DOI)capability based on 48 dual-layer-offset detector blocks was proposed in this paper.The top and bottom layers were directly distinguished using the intensity difference and offset grid pattern.The identification of the response peaks of the top layer was based on the singular value decomposition(SVD)and mean-shift algorithm.SVD was employed to create a principal component image of the top layer.Then,projection profiles along the x and y directions are obtained.A local maximum identification method was utilized to locate the peaks from these projections.At last,the mean-shift algorithm was used to improve the accuracy of the peaks.Identification of the response peaks of the bottom layer was based on self-organizing map(SOM)neural networks and mean-shift algorithm.Initial peaks of the bottom layer were generated based on the shift of the top peaks.Then they were adjusted using the SOM algorithm simultaneously.At last,they were modified individually using the mean-shift algorithm.After locating all response peaks,the flood map was segmented using an Euclidean distance based algorithm.The proposed algorithm was run on a laptop with the Intel i5-6300@2.30 GHz CPU for the whole PET system.The results show that it achieves a crystal peak identification accuracy of 99.56%for the top layer and 99.11%for the bottom layer,the average accuracy of the whole system is 99.34%.The average processing time for a block based on the laptop is 101 s.Compared with the algorithm with only mean-shift algorithm,the SOM algorithm improves the identification quality for the bottom layer.In conclusion,a robust,fast,high accuracy crystal identification method for dual-layer-offset DOI-PET detectors are developed.The proposed method can also be utilized for single layer PET detector blocks.
作者
徐一帆
侯岩松
纪英财
孙立风
魏清阳
XU Yifan;HOU Yansong;JI Yingcai;SUN Lifeng;WEI Qingyang(Beijing Engineering Research Center of Industrial Spectrum Imaging,School of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,China;Beijing Novel Medical Equipment Ltd.,Beijing 102206,China;CNNC High Energy Equipment(Tianjin)Co.,Ltd.,Tianjin 300300,China)
出处
《原子能科学技术》
EI
CAS
CSCD
北大核心
2022年第S01期235-242,共8页
Atomic Energy Science and Technology
基金
国家自然科学基金(11975044)
中核集团“青年英才”项目
中央高校基本科研业务费项目(FRF-TP-19-019A3)
国防科工局核能开发项目(HNKF-2020)
