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An input function estimation method for dynamic mouse 18F-FDG microPET studies

机译:动态鼠标 18 F-FDG MicroPet研究的输入功能估计方法

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We present and validate a method to estimate the input function (IF) from dynamic mouse 18F-FDG microPET images and 1 late blood sample. The proposed method is almost entirely noninvasive, and accounts for the spillover, partial-volume, delay and dispersion effects. First, the time-activity curves (TACs) of the left ventricle (LV), the myocardium and the liver were extracted from their respective volumes of interest (VOIs). The liver TAC data points between 35 seconds and 1500 seconds were used to substitute for blood samples since we found they were highly correlated (average correlation coefficient r = 0.989 ± 0.012). The IF to be estimated (EIF) was expressed as a mathematical model in which the parameters were simultaneously estimated by fitting the modeled LV and myocardium TACs to the mouse PET data for these organs. Twenty normal mice data sets from the Mouse Quantitation Program database, which were shared by UCLA, were used to verify our method. The differences of the area under the curves between the EIF and the true IF (blood samples) was 6.9% ± 13.2%, and the difference of the 18F-FDG influx constant Ki in myocardium was 4.8% ± 16.7% (r = 0.931). The experimental results demonstrate the effectiveness of the proposed method.
机译:我们展示并验证了一种估算动态鼠标 18 f-f-f-f-fdg micropet图像和1晚血液样本的输入功能(if)的方法。该方法几乎完全是非侵入性的,并且占溢出,部分体积,延迟和分散效应。首先,从它们各自的感兴趣体积(VoIS)中提取左心室(LV),心肌和肝脏的时活性曲线(TAC)。肝脏TAC数据点在35秒和1500秒之间用于替代血液样本,因为我们发现它们高度相关性(平均相关系数R = 0.989±0.012)。如果要估计(EIF)表示为数学模型,其中通过将模拟的LV和心肌TAC与这些器官的小鼠PET数据拟合,同时估计参数。来自鼠标定量程序数据库的二十个正常小鼠数据集,由UCLA共享,用于验证我们的方法。 EIF之间的曲线下的区域的差异和真实(血液样本)为6.9%±13.2%,18F-FDG流入常数K I 在心肌中的差异为4.8% ±16.7%(r = 0.931)。实验结果表明了所提出的方法的有效性。

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