Researchers use dynamic PET imaging with target-selective tracer molecules to probe molecular processes. Kinetic models have been developed to describe these processes. The models are typically fitted to the measured PET data with the assumption that the brain is in a steady-state condition for the duration of the scan. The end results are quantitative parameters that characterize the molecular processes. The most common kinetic modeling endpoints are estimates of volume of distribution or the binding potential of a tracer. If the steady state is violated during the scanning period, the standard kinetic models may not apply. To address this issue, time-variant kinetic models have been developed for the characterization of dynamic PET data acquired while significant changes (e.g., short-lived neurotransmitter changes) are occurring in brain processes. These models are intended to extract a transient signal from data. This work in the PET field dates back at least to the 1990s. As interest has grown in imaging nonsteady events, development and refinement of time-variant models has accelerated. These new models, which we classify as belonging to the first, second, or third generation according to their innovation, have used the latest progress in mathematics, image processing, artificial intelligence, and statistics to improve the sensitivity and performance of the earliest practical time-variant models to detect and describe nonsteady phenomena. This review provides a detailed overview of the history of time-variant models in PET. It puts key advancements in the field into historical and scientific context. The sum total of the methods is an ongoing attempt to better understand the nature and implications of neurotransmitter fluctuations and other brief neurochemical phenomena.

研究人员使用具有靶标选择性示踪分子的动态 PET 成像来探测分子过程。已经开发了动力学模型来描述这些过程。这些模型通常拟合到测量的 PET 数据，并假设大脑在扫描期间处于稳态状态。最终结果是表征分子过程的定量参数。最常见的动力学建模终点是分布体积或示踪剂结合电位的估计值。如果在扫描期间违反了稳态，则标准动力学模型可能不适用。为了解决这个问题，已经开发了时变动力学模型，用于表征在大脑过程中发生重大变化（例如，短暂的神经递质变化）时获得的动态 PET 数据。这些模型旨在从数据中提取瞬态信号。PET 领域的这项工作至少可以追溯到 1990 年代。随着人们对非稳态事件成像的兴趣日益浓厚，时变模型的开发和改进也加快了。这些新模型，我们根据它们的创新分为属于第一代、第二代或第三代，它们利用数学、图像处理、人工智能和统计学方面的最新进展，提高了最早的实用时变模型的灵敏度和性能，以检测和描述非定常现象。本综述详细概述了 PET 中时变模型的历史。它将该领域的关键进展置于历史和科学背景下。 这些方法的总和是一次持续的尝试，以更好地理解神经递质波动和其他短暂神经化学现象的性质和影响。