何校栋,朱文佳,党永红,霍力,李方,张辉.一种11C-acetate肝脏PET动态成像的逐像素参数估计算法[J].中国医学影像技术,2016,32(7):1124~1129
一种11C-acetate肝脏PET动态成像的逐像素参数估计算法
A pixel based parameters estimation method for 11C-acetate applied in liver with dynamic PET
投稿时间:2016-01-04  修订日期:2016-03-31
DOI:10.13929/j.1003-3289.2016.07.035
中文关键词:  肝肿瘤  醋酸盐  体层摄影术  发射型计算机  房室模型
英文关键词:Liver neoplasms  Acetate  Tomography  emission-computed  Compartment model
基金项目:
作者单位E-mail
何校栋 清华大学医学院生物医学工程系, 北京 100084  
朱文佳 清华大学医学院生物医学工程系, 北京 100084  
党永红 中国医学科学院 北京协和医学院北京协和医院核医学科, 北京 100730  
霍力 中国医学科学院 北京协和医学院北京协和医院核医学科, 北京 100730  
李方 中国医学科学院 北京协和医学院北京协和医院核医学科, 北京 100730  
张辉 中国医学科学院 北京协和医学院北京协和医院核医学科, 北京 100730 hzhang@tsinghua.com 
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中文摘要:
      目的 针对11C-acetate在肝脏中的PET动态图像,提出逐像素的动力学参数估计方法。方法11C-acetate在肝脏中的PET动态图像进行分块;针对每一图像分块,使用三房室模型建模,并采用图形化非线性最小二乘法(GNLS)方法估计动力学参数;缩小肝脏中PET动态图像分块的大小,以三房室模型估计的参数值为基准设定新的参数约束条件。重复以上步骤,当PET图像分块大小为单个像素时,终止算法。将以上方法用于仿真及临床验证,评价效果。结果 仿真结果表明,相对于传统的GNLS方法,逐像素参数估计方法在保证参数估计准确度的同时,提升了参数估计的可靠性。临床研究显示,通过该方法获得的肿瘤组织与正常组织的对比度相对于静态PET图像有显著提升。结论 采用逐像素参数估计方法可获得更加准确、可靠的参数图谱,可显示肿瘤的细节信息,有助于临床对肝癌的诊断与评估。
英文摘要:
      Objective To propose a pixel based parameters estimation method for 11C-acetate applied in the liver with dynamic PET. Methods The dynamic PET images of 11C-acetate was divided into blocks. The three-compartment model for every image blocks were built, and the graphed nonlinear least squares algorithm (GNLS) to estimate the kinetic parameters was used. Then the size of image blocks were reduced, and the constraint condition according to the estimated kinetic parameters was updated. The process above was repeated and the algorithm was stopped when the size of image blocks was the same as a single pixel. Simulation and clinical experiment was conducted to evaluate this algorithm. Results The simulation results showed that the pixel based GNLS algorithm ensured the accuracy of the estimated kinetic parameters and improved the estimation reliability when compared with the conventional GNLS algorithm. In clinical data test, the proposed method substantially improved the contract between tumor and normal tissue. Conclusion The proposed method can generate a more accurate and reliable parameter maps which can show the detail information of tumor and contribute to the diagnose and evaluate of liver tumor.
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