文章编号 - 中华医学会.doc

文章编号： PET/CT分子影像动力学建模肺 [摘要] 判别肿瘤的良恶性。使用德国Siemens公司，图像采样协议为8×15 秒、6×30 秒、5×300 秒, 总共持续30基于人工免疫网络PET分子影像动力学参数估计方法±标准差）分别为k1=0.1746±0.0531; k2=0.4030±0.3324; k3=0.5208±0.2274; k4=0.1046±0.0543; f=0.1468±0.1305; Ki=0.1003±0.0326。结论实验结果表明，PKAIN的人工免疫网络智能算法更适合于肿瘤组织的PET/CT分子影像动力学建模。与常规的60分钟18F-FDG采相比，30分钟短采样时间，提高了 FDG PET动力学建模方法的临床应用价值，证实了新型PET/CT分子影像动力学建模方法在肺癌诊断中的价值。 [关键词肺肿瘤； The new method for PET / CT molecular imaging kinetic modeling in the diagnosis of lung cancer 【Abstract】Objective To distinguish the benign and malignant of lung tumors using a new method based on the artificial immune network algorithm for PET / CT molecular imaging kinetic modeling. Methods All studies were performed at the Nuclear Medicine Center of the Wuxi Fourth People’s Hospital, Wuxi, China. There are five lung cancer patients under PET/CT scan. The PET/CT scans were performed with Biograph True Point 64 PET/CT. Images were acquired for 30 min with people under injection of FDG drug about 370 to 555 MBq. The scanning schedule was: 8 15-s scans, 6 30-s scans, 5 5-min scans. Small size of ROIs were manually drawn over the PET images to obtain the time-activity curves for the Normal lung tissue, suspected lung tumor tissue and abdominal aortic blood pool area. Kinetic parameters of the both the normal lung tissue and the suspected lung tumor tissue were estimated by using the new method which was based on the artificial immune network algorithm (PKAIN). Results The statistics of parameters of the FDG PET kinetic models of five patients with suspected lung tumor tissue were k1=0.1190±0.0023, k2=0.0397±0.0132; k3=0.7316±0.3421; k4=0.4334±0.3595; f=0.0857±0.0032; Ki=0.1117±0.00299. Conclusion The PKAIN outperformed the KIS software when fitting the observered TACs especially for the lung cancer regions. Compared with the 60min sample schedule, the 30min sample schedule is more applicable and to be proven well in lung cancer diagnosis. 【Keywords】lung tumor; PET/CT; molecular imagin