摘要
目的使用NeuroQ软件建立本地人脑^(18)F-FDG PET数据库并测试其实用价值。方法采用NeuroQ软件对筛选入组的93例受检者的PET图像进行分析并建立本地正常人数据库。对比本地数据库和软件内置数据库的原始数据差异,获得本地人群代谢特征,并比较两者在病例分析中的差异。结果相比于内置数据库,本地数据库代谢值较高的区域集中在额叶区域(右额上回:4.61%,右额中回:4.49%;左额上回:3.77%,左额中回:3.11%)、右后扣带回(6.28%)、顶叶下部(右:2.46%,左:2.66%),代谢值较低区域主要为小脑(右:−3.57%,左:−5.27%)、脑桥(−3.59%)。在病例分析中,本地数据库结果与病例的临床评估符合度更好。结论建立本地数据库有助于消除地域、采集条件等差异,对于提高诊断的准确性具有重要价值。
Objective To establish a local human brain ^(18)F-fluorodeoxyglucose positron emission tomography(PET)database with NeuroQ software,and to test its practical value.Methods With NeuroQ software,the PET images of 93 selected subjects were analyzed,and the results were used to create a local normal human database.The metabolism characteristics of the local population were obtained by comparing the original data of the local database with those of the built-in software database,and the difference in case analysis was compared between the two databases.Results Compared with the built-in database,the local database showed the region with high metabolic rate concentrated in the frontal lobe(right superior frontal gyrus:4.61%,right middle frontal gyrus:4.49%,left superior frontal gyrus:3.77%,and left middle frontal gyrus:3.11%),right posterior cingulate gyrus(6.28%),right inferior parietal lobule(2.46%),left inferior parietal lobule(2.66%),the region with reduced metabolism mainly in the right cerebellum(−3.57%),left cerebellum(−5.27%),and pons(−3.59%).For case analysis,the local database generated results were better consistent with the clinical assessment results of cases.Conclusion Establishment of a local database would eliminate the differences in regions and acquisition conditions,which is of great value to improve the accuracy of diagnosis.
作者
冯洪波
解敬慧
高雅
黎蕾
张延军
FENG Hongbo;XIE Jinghui;GAO Ya;LI Lei;ZHANG Yanjun(Nuclear Medical Department,the First Affiliated Hospital to Dalian Medical University,Dalian 116011 China)
出处
《中国辐射卫生》
2022年第2期229-233,238,共6页
Chinese Journal of Radiological Health
关键词
正电子发射断层成像
正常数据库
大脑
定量分析
Positron emission tomography
Normal database
Brain
Quantitative analysis