Aims. We describe an intuitive, easy to use method called PET-ABC that enables full Bayesian statistical inference from single subject dynamic PET data. The performance of PET-ABC was compared with weighted non-linear least squares (WNLS) in terms of reliability of kinetic parameter estimation and statistical power for model selection. Methods. Dynamic PET data based on 1-tissue and 2-tissue compartmental models were simulated with 2 noise models and 3 noise levels. PET-ABC was used to evaluate the reliability of parameter estimates under each condition. It was also used to perform model selection for a simulated noisy dataset composed of a mixture of 1- and 2-tissue compartment kinetics. Finally, PET-ABC was used to analyze a non-steady state dynamic [¹¹C] raclopride study performed on a fully conscious rat administered either 2 mg.kg⁻¹ amphetamine or saline 20 min after tracer injection. Results. PET-ABC yielded posterior point estimates for model parameters with smaller variance than WNLS, as well as probability density functions indicating confidence intervals for those estimates. It successfully identified the superiority of a 2-tissue compartment model to fit the simulated mixed model data. For the drug challenge study, the post observation probability of striatal displacement of the PET signal was 0.9 for amphetamine and approximately 0 for saline, indicating a high probability of amphetamine-induced endogenous dopamine release in the striatum. PET-ABC also demonstrated superior statistical power to WNLS (0.87 versus 0.09) for selecting the correct model in a simulated ligand displacement study. Conclusions. PET-ABC is a simple and intuitive method that provides complete Bayesian statistical analysis of single subject dynamic PET data, including the extent to which model parameter estimates and model choice are supported by the data. Software for PET-ABC is freely available as part of the PETabc package https://github.com/cgrazian/PETabc.

目标。我们描述了一种直观、易于使用的方法，称为 PET-ABC，它可以从单个主题动态 PET 数据中进行完整的贝叶斯统计推断。在动力学参数估计的可靠性和模型选择的统计功效方面，将 PET-ABC 的性能与加权非线性最小二乘法 (WNLS) 进行了比较。方法。基于 1 组织和 2 组织隔室模型的动态 PET 数据使用 2 个噪声模型和 3 个噪声级别进行模拟。PET-ABC 用于评估每种条件下参数估计的可靠性。它还用于为模拟噪声数据集执行模型选择，该数据集由 1 和 2 组织隔室动力学的混合物组成。最后，PET-ABC 被用来分析一个非稳态动态 [ ¹¹C] raclopride 研究对完全清醒的大鼠进行，在示踪剂注射后 20 分钟给予 2 mg.kg ^-1苯丙胺或盐水。结果。PET-ABC 产生了方差比 WNLS 小的模型参数的后点估计，以及指示这些估计置信区间的概率密度函数。它成功地确定了 2 组织隔室模型在拟合模拟混合模型数据方面的优越性。对于药物激发研究，PET 信号纹状体位移的观察后概率对于安非他明为 0.9，对于盐水约为 0，表明在纹状体中安非他明诱导的内源性多巴胺释放的概率很高。在模拟配体置换研究中，PET-ABC 在选择正确模型方面也表现出优于 WNLS（0.87 对 0.09）的统计功效。结论。PET-ABC 是一种简单直观的方法，它提供了对单个受试者动态 PET 数据的完整贝叶斯统计分析，包括数据支持模型参数估计和模型选择的程度。PET-ABC 软件可作为PETabc软件包的一部分免费提供https://github.com/cgrazian/PETabc。