Method for tumor recognition with short dynamic PET images:Theory and simulation study  

作　　者：Huiting Qiao Jing Bai Yingmao Chen Jiahe Tian 

机构地区：[1]Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China [2]Department of Nuclear Medicine, General Hospital of PLA, Beijing 100853, China

出　　处：《Progress in Natural Science:Materials International》2009年第1期73-77,共5页自然科学进展·国际材料（英文版）

基　　金：supported by National Natural Science Foundation of China (Grant No.30670577);the Tsing-hua-Yue-Yuen Medical Science Foundation;the National Basic Research Program of China;the Special Research Fund for the Doctoral Program of Higher Education of China;the International Science Linkages established under the Australian Government's innovation statement(CH050131)

摘　　要：A new method was provided in this paper to recognize tumor in PET images automatically and reduce the waiting time of patient.Based on the unsupervised clustering algorithm(ISODATA),this method diagnoses tumor with short dynamic PET images.The theoretical basis of this method is that the metabolic characteristics of different tissues,represented by time-activity curve in the region of interest,are distinctive.The computer program was developed and validated using simulated dynamic PET data with small tumor in lung.Simulation study shows that this method could recognize tumor with the short dynamic PET data in 10 min successfully,and this method was not sensitive to initial cluster center.A new method was provided in this paper to recognize tumor in PET images automatically and reduce the waiting time of patient. Based on the unsupervised clustering algorithm （ISODATA）, this method diagnoses tumor with short dynamic PET images. The theoretical basis of this method is that the metabolic characteristics of different tissues, represented by time-activity curve in the region of interest, are distinctive. The computer program was developed and validated using simulated dynamic PET data with small tumor in lung. Simulation study shows that this method could recognize tumor with the short dynamic PET data in 10 min successfully, and this method was not sensitive to initial cluster center.

关 键 词：Dynamic PET Clustering algorithm Tumor recognition Simulation 

分 类 号：R73[医药卫生—肿瘤]