Carson R E
Department of Nuclear Medicine, National Institutes of Health, Bethesda, MD 20892.
J Cereb Blood Flow Metab. 1991 Mar;11(2):A45-50. doi: 10.1038/jcbfm.1991.36.
The ability to differentiate regional patterns of flow and metabolism between various patient populations depends upon the signal-to-noise characteristics of the data. The approach chosen for producing quantitative data will affect the detection sensitivity of a method. Methods based on mathematical models can reduce intersubject variability by accounting for factors unrelated to the physiological measure of interest, in particular, differences in the input function. However, errors in the model and in the implementation of a model-based method can increase variability compared to simpler, empirical methods. Normalization of physiological measures can significantly reduce intersubject variation; however, interpretation of normalized results can be more complex. The advantages and disadvantages of various approaches for physiological quantitation are considered.
区分不同患者群体之间血流和代谢区域模式的能力取决于数据的信噪比特征。用于生成定量数据的方法会影响一种方法的检测灵敏度。基于数学模型的方法可以通过考虑与感兴趣的生理测量无关的因素，特别是输入函数的差异，来减少个体间的变异性。然而，与更简单的经验方法相比，模型以及基于模型的方法实施过程中的误差会增加变异性。生理测量的标准化可以显著降低个体间的差异；然而，标准化结果的解释可能会更复杂。本文考虑了各种生理定量方法的优缺点。
