Spillover and partial-volume correction for image-derived input functions for small-animal 18F-FDG PET studies
Spillover and partial-volume correction for image-derived input functions for small-animal 18F-FDG PET studies
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DOI：
10.2967/jnumed.107.047613
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发表时间：
2008-04-01
影响因子：
9.3
通讯作者：
Muzic, Raymond F., Jr.
中科院分区：
文献类型：
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作者：
Fang, Yu-Hua Dean;Muzic, Raymond F., Jr.
我们提出并验证了一种从动态图像数据和0或1个血液样本中获取输入函数的方法，用于小动物F-18-FDG PET研究。该方法通过生理模型考虑溢出和部分体积效应，以产生模型校正输入函数(MCIF)。方法：获得14只SD大鼠和17只C57BL/6小鼠的心室图像输入函数(IDIF)和心肌时间-活动曲线。每个MCIF被表示为一个包含7个参数的数学方程，通过将IDIF和心肌曲线拟合为双输出房室模型，与心肌模型参数同时估计。同时估计采用0份或1份晚期血样。通过与从血液样本测量的输入进行比较来验证MCIF。验证包括计算3种组织中曲线下面积(AUC)和F-18-FDG流入常数KI的误差。结果：对于RAT数据，0样本MCIF的AUC误差为5.3%+/-19.0%，1样本MCIF的AUC误差为-2.3%+/-14.8%。用该方法计算心肌、脑和肌肉的KI，0样本法的总误差为-6.3%+/-27.0%(相关系数r=0.967)，1样本法的总误差为3.1%+/-20.6%(r=0.970)。T检验未能检测到0样本和1样本MCIF的KI估计值之间的显著差异(P>；0.05)。对于小鼠数据，0样本MCIF的AUC误差为4.3%+/-25.5%，1样本MCIF的AUC误差为-1.7%+/-20.9%。0样品法的KI平均误差为-8.0%+/-27.6%(r=0.955)，1样品法的KI误差为-2.8%+/-22.7%(r=0.971)。T检验发现，0样本法与1样本法的KI在大脑和肌肉中的差异显著，而与1样本法的差异不显著。在大鼠和小鼠中，0样本和1样本MCIF都显示出与未校正的IDIF相比，AUC和KI错误至少减少了10倍。结论：在小动物F-18-FDG PET研究中，MCIF提供了一种可靠的、非侵入性的输入函数估计，可用于准确量化葡萄糖代谢率。
We present and validate a method to obtain an input function from dynamic image data and 0 or 1 blood sample for small-animal F-18-FDG PET studies. The method accounts for spillover and partial-volume effects via a physiologic model to yield a model-corrected input function (MCIF). Methods: Image-derived input functions (IDIFs) from heart ventricles and myocardial time-activity curves were obtained from 14 Sprague-Dawley rats and 17 C57BL/6 mice. Each MCIF was expressed as a mathematic equation with 7 parameters, which were estimated simultaneously with the myocardial model parameters by fitting the IDIFs and myocardium curves to a dual-output compartment model. Zero or 1 late blood sample was used in the simultaneous estimation. MCIF was validated by comparison with input measured from blood samples. Validation included computing errors in the areas under the curves (AUCs) and in the F-18-FDG influx constant Ki in 3 types of tissue. Results: For the rat data, the AUC error was 5.3% +/- 19.0% in the 0-sample MCIF and -2.3% +/- 14.8% in the 1 -sample MCIF. When the MCIF was used to calculate the Ki of the myocardium, brain, and muscle, the overall errors were -6.3% +/- 27.0% in the 0-sample method (correlation coefficient r = 0.967) and 3.1 % +/- 20.6% in the 1 -sample method (r = 0.970). The t test failed to detect a significant difference (P > 0.05) in the Ki estimates from both the 0-sample and the 1-sample MCIF. For the mouse data, AUC errors were 4.3% +/- 25.5% in the 0-sample MCIF and -1.7% +/- 20.9% in the 1-sample MCIF. Ki errors averaged -8.0% +/- 27.6% for the 0-sample method (r = 0.955) and -2.8% +/- 22.7% for the 1-sample method (r = 0.971). The t test detected significant differences in the brain and muscle in the Ki for the 0-sample method but no significant differences with the 1 -sample method. In both rat and mouse, 0-sample and 1 -sample MCIFs both showed at least a 10-fold reduction in AUC and Ki errors compared with uncorrected IDIFs. Conclusion: MCIF provides a reliable, noninvasive estimate of the input function that can be used to accurately quantify the glucose metabolic rate in small-animal F-18-FDG PET studies.