IEEE Trans Med Imaging. 2014 May;33(5):1093-108. doi: 10.1109/TMI.2014.2305113.
In a positron emission tomography (PET) study, the local uptake of the tracer is dependent on vascular delivery and retention. For dynamic studies the measured uptake time-course information can be best interpreted when knowledge of the time-course of tracer in the blood is available. This is certainly true for the most established tracers such as 18F-Fluorodeoxyglucose (FDG) and 15O-Water (H2O). Since direct sampling of blood as part of PET studies is increasingly impractical, there is ongoing interest in image-extraction of blood time-course information. But analysis of PET-measured blood pool signals is complicated because they will typically involve a combination of arterial, venous and tissue information. Thus, a careful appreciation of these components is needed to interpret the available data. To facilitate this process, we propose a novel Markov chain model for representation of the circulation of a tracer atom in the body. The model represents both arterial and venous time-course patterns. Under reasonable conditions equilibration of tracer activity in arterial and venous blood is achieved by the end of the PET study-consistent with empirical measurement. Statistical inference for Markov model parameters is a challenge. A penalized nonlinear least squares process, incorporating a generalized cross-validation score, is proposed. Random effects analysis is used to adaptively specify the structure of the penalty function based on historical samples of directly measured blood data. A collection of arterially sampled data from PET studies with FDG and H2O is used to illustrate the methodology. These data analyses are highly supportive of the overall modeling approach. An adaptation of the model to the problem of extraction of arterial blood signals from imaging data is also developed and promising preliminary results for cerebral and thoracic imaging studies with FDG and H2O are obtained.
在正电子发射断层扫描（PET）研究中，示踪剂的局部摄取取决于血管输送和保留。对于动态研究，当获得示踪剂在血液中的时间过程信息时，可以最好地解释测量的摄取时间过程信息。对于最成熟的示踪剂，如 18F-氟脱氧葡萄糖（FDG）和 15O-水（H2O），这是肯定的。由于作为 PET 研究一部分的直接血液采样越来越不切实际，因此人们对从图像中提取血液时间过程信息的方法越来越感兴趣。但是，由于 PET 测量的血池信号的分析涉及动脉、静脉和组织信息的组合，因此需要仔细了解这些成分才能解释可用数据。为了促进这个过程，我们提出了一种新的马尔可夫链模型，用于表示体内示踪原子的循环。该模型表示动脉和静脉时间过程模式。在合理的条件下，通过 PET 研究结束时，动脉和静脉血液中的示踪剂活性达到平衡，这与经验测量一致。马尔可夫模型参数的统计推断是一个挑战。提出了一种惩罚非线性最小二乘过程，该过程结合了广义交叉验证评分。使用随机效应分析，根据直接测量血液数据的历史样本自适应指定惩罚函数的结构。使用来自 FDG 和 H2O 的 PET 研究中采集的一组动脉采样数据来说明该方法。这些数据分析非常支持整体建模方法。还开发了一种将模型改编为从成像数据中提取动脉血液信号的问题的方法，并获得了 FDG 和 H2O 的脑和胸部成像研究的有希望的初步结果。
