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基于小波变换的PET图像分析(英文)

PET Image Analysis Based on Wavelet Transform

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【作者】 闫镔王鹏李可郝晶吴义根谢千河支联合王崴鲁娜袁秀丽单保慈

【Author】 YAN Bin 1 WANG Peng 1,2 LI Ke 1 HAO Jing 3 WU Yi-Gen 1 XIE Qian-He 2 ZHI Lian-He 1 WANG Wei 3 LU Na 1 YUAN Xiu-Li 1 SHAN Bao-Ci 1 (1 Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049,China; 2 Chengdu Institute of Computer Applications,?Chinese Academy of Sciences, Chengdu 610041,China; 3 Department of Radiology, Xuanwu Hospital, Capital University of Medical Sciences, Beijing 100053,China)

【机构】 中国科学院高能物理研究所首都医科大学宣武医院放射科中国科学院成都计算机应用研究所中国科学院高能物理研究所 北京100049北京100049成都610041北京100049北京100053

【摘要】 提出一种把小波变换和统计检验结合起来检测PET图像激活区的方法.首先,采用模拟的PET图像来评价算法的可靠性,结果显示,在小波域上进行统计检验比传统的直接在空间域上进行统计检验具有更高的灵敏度,更强的抗噪声干扰性能,更快的计算速度.最后,用该方法处理真实的PET图像,也得到了满意的结果.该方法为PET医学图像处理和脑功能研究提供了一种新的多尺度、高性能的分析手段,对于脑功能研究的功能区定位、临床诊断、药物疗效评估等有着重要意义

【Abstract】 In this paper wavelet transform and test of hypothesis are combined to detect activated areas in PET images. The algorithm is validated by using the simulation images under different conditions. Compared with the spatial domain algorithm, the wavelet domain algorithm can get more precise results,better SNR trait and more rapid computing speed .The probability of type Ⅱ error in wavelet domain is less than that in the spatial domain, so the wavelet domain algorithm is a kind of sensitive algorithm.Finally, we used this algorithm to process the real PET images and achieved good result. This algorithm may result in important significance to the detection of functional areas, the clinic diagnosis and the evaluation of curative effect.

【基金】 supportedbytheMajorStateBasicResearchDevelopmentProgramofChina (G1999054000),theKnowledgeInnovationProjectofChineseAcademyofSciences (U 5 16 3)
  • 【文献出处】 中国科学院研究生院学报 ,Journal of the Graduate School of the Chinese Academy of Science , 编辑部邮箱 ,2005年04期
  • 【分类号】TP391.41
  • 【被引频次】1
  • 【下载频次】129
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