Turkheimer F, Moresco R M, Lucignani G, Sokoloff L, Fazio F, Schmidt K
Department of Nuclear Medicine, University of Milan, Institute H. San Raffaele, Italy.
J Cereb Blood Flow Metab. 1994 May;14(3):406-22. doi: 10.1038/jcbfm.1994.52.
A method for kinetic analysis of dynamic positron emission tomography (PET) data by linear programming that allows identification of the components of a measured PET signal without predefining a compartmental model has recently been proposed by Cunningham and co-workers. The method identifies a small subset of functions from a large input set of feasible functions that best fits the time course of total radioactivity measured by PET. To investigate in detail the properties of this technique, we applied it to PET studies with [18F]fluorodeoxyglucose, a tracer with well-characterized kinetic properties. We examined dynamically acquired data over various time intervals in many brain regions and found that the number of components identified by the method is stable and consistent with the presence of kinetic heterogeneity in every region. We optimized the method for determination of regional rates of glucose utilization; calculated rates were found to be somewhat dependent upon the treatment of noise in the measured tissue data and upon the time interval in which the data were collected. The application of a numerical filter to remove noise in the data resulted in values for regional cerebral glucose utilization that were stable with time and consistent with rates determined by the other established techniques. Based on the results of the current study, we expect that the spectral analysis technique will prove to be a highly flexible tool for kinetic analysis of other tracer compounds; it is capable of producing low-variance, time-stable estimates of physiological parameters when optimized for time interval of application, input spectrum of components, and processing of noise in the data.
坎宁安及其同事最近提出了一种通过线性规划对动态正电子发射断层扫描（PET）数据进行动力学分析的方法，该方法无需预先定义房室模型就能识别测量到的PET信号的成分。该方法从大量可行函数的输入集中识别出一小部分函数，这些函数最能拟合PET测量的总放射性的时间进程。为了详细研究该技术的特性，我们将其应用于使用[18F]氟脱氧葡萄糖的PET研究，[18F]氟脱氧葡萄糖是一种具有明确动力学特性的示踪剂。我们在许多脑区的不同时间间隔内检查了动态采集的数据，发现该方法识别出的成分数量是稳定的，并且与每个区域存在的动力学异质性一致。我们对测定区域葡萄糖利用率的方法进行了优化；发现计算出的速率在一定程度上取决于测量组织数据中的噪声处理以及数据收集的时间间隔。应用数字滤波器去除数据中的噪声后，区域脑葡萄糖利用率的值随时间稳定，并且与其他既定技术确定的速率一致。基于当前研究的结果，我们预计光谱分析技术将被证明是用于其他示踪化合物动力学分析的高度灵活的工具；当针对应用的时间间隔、成分的输入光谱以及数据中的噪声处理进行优化时，它能够产生生理参数的低方差、时间稳定估计值。
