Molecular Imaging Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
Neuroimage. 2012 Nov 15;63(3):1532-41. doi: 10.1016/j.neuroimage.2012.08.007. Epub 2012 Aug 10.
Quantitative PET studies of neuroreceptor tracers typically require that arterial input function be measured. The aim of this study was to explore the use of a population-based input function (PBIF) and an image-derived input function (IDIF) for (11)C-rolipram kinetic analysis, with the goal of reducing - and possibly eliminating - the number of arterial blood samples needed to measure parent radioligand concentrations.
A PBIF was first generated using (11)C-rolipram parent time-activity curves from 12 healthy volunteers (Group 1). Both invasive (blood samples) and non-invasive (body weight, body surface area, and lean body mass) scaling methods for PBIF were tested. The scaling method that gave the best estimate of the Logan-V(T) values was then used to determine the test-retest variability of PBIF in Group 1 and then prospectively applied to another population of 25 healthy subjects (Group 2), as well as to a population of 26 patients with major depressive disorder (Group 3). Results were also compared to those obtained with an image-derived input function (IDIF) from the internal carotid artery. In some subjects, we measured arteriovenous differences in (11)C-rolipram concentration to see whether venous samples could be used instead of arterial samples. Finally, we assessed the ability of IDIF and PBIF to discriminate depressed patients (MDD) and healthy subjects.
Arterial blood-scaled PBIF gave better results than any non-invasive scaling technique. Excellent results were obtained when the blood-scaled PBIF was prospectively applied to the subjects in Group 2 (V(T) ratio 1.02±0.05; mean±SD) and Group 3 (V(T) ratio 1.03±0.04). Equally accurate results were obtained for two subpopulations of subjects drawn from Groups 2 and 3 who had very differently shaped (i.e. "flatter" or "steeper") input functions compared to PBIF (V(T) ratio 1.07±0.04 and 0.99±0.04, respectively). Results obtained via PBIF were equivalent to those obtained via IDIF (V(T) ratio 0.99±0.05 and 1.00±0.04 for healthy subjects and MDD patients, respectively). Retest variability of PBIF was equivalent to that obtained with full input function and IDIF (14.5%, 15.2%, and 14.1%, respectively). Due to (11)C-rolipram arteriovenous differences, venous samples could not be substituted for arterial samples. With both IDIF and PBIF, depressed patients had a 20% reduction in (11)C-rolipram binding as compared to control (two-way ANOVA: p=0.008 and 0.005, respectively). These results were almost equivalent to those obtained using 23 arterial samples.
Although some arterial samples are still necessary, both PBIF and IDIF are accurate and precise alternatives to full arterial input function for (11)C-rolipram PET studies. Both techniques give accurate results with low variability, even for clinically different groups of subjects and those with very differently shaped input functions.
探讨使用基于人群的输入函数(PBIF)和图像衍生的输入函数(IDIF)进行(11)C-rolipram 动力学分析,以减少(并可能消除)测量母体放射性配体浓度所需的动脉血样数量。
首先使用 12 名健康志愿者(第 1 组)的 (11)C-rolipram 母体时间-活性曲线生成 PBIF。测试了基于人群的输入函数的两种方法:(1)侵入性(血液样本)和(2)非侵入性(体重、体表面积和瘦体重)。使用最能估计 Logan-V(T)值的方法来确定 PBIF 在第 1 组中的测试-重测变异性,然后前瞻性地应用于另一组 25 名健康受试者(第 2 组),以及 26 名患有重性抑郁症的患者(第 3 组)。结果也与颈动脉内的图像衍生输入函数(IDIF)进行了比较。在一些受试者中,我们测量了(11)C-rolipram 浓度的动静脉差异,以确定是否可以用静脉血样代替动脉血样。最后,我们评估了 IDIF 和 PBIF 区分抑郁患者(MDD)和健康受试者的能力。
动脉血样缩放的 PBIF 比任何非侵入性缩放技术都能得到更好的结果。当将血液缩放的 PBIF 前瞻性地应用于第 2 组(V(T)比 1.02±0.05;平均值±标准差)和第 3 组(V(T)比 1.03±0.04)的受试者时,得到了非常好的结果。对于从第 2 组和第 3 组中提取的两个亚组的受试者,使用相同的方法得到了非常相似的结果,他们的输入函数形状非常不同(即“更平坦”或“更陡峭”)(V(T)比分别为 1.07±0.04 和 0.99±0.04)。通过 PBIF 获得的结果与通过 IDIF 获得的结果相同(健康受试者和 MDD 患者的 V(T)比分别为 0.99±0.05 和 1.00±0.04)。PBIF 的重测变异性与完整输入函数和 IDIF 的重测变异性相同(分别为 14.5%、15.2%和 14.1%)。由于 (11)C-rolipram 的动静脉差异,不能用静脉血样代替动脉血样。使用 IDIF 和 PBIF,与对照组相比,抑郁患者的 (11)C-rolipram 结合减少了 20%(双向方差分析:p=0.008 和 0.005)。这些结果几乎与使用 23 个动脉血样获得的结果相同。
尽管仍需要一些动脉血样,但 PBIF 和 IDIF 都是 (11)C-rolipram PET 研究中全动脉输入函数的准确且精确的替代方法。这两种技术都能得到准确的结果,并且具有低变异性,即使是在临床不同的受试者群体和输入函数形状非常不同的情况下也是如此。