Tracer disposition kinetics in the determination of local cerebral blood flow by a venous equilibrium model, tube model, and distributed model.

Tracer distribution kinetics in the determination of local cerebral blood flow (LCBF) were examined by using three models, i.e., venous equilibrium, tube, and distributed models. The technique most commonly used for measuring LCBF is the tissue uptake method, which was first developed and applied by Kety (1951). The measurement of LCBF with the 14C-iodoantipyrine (IAP) method is calculated by using an equation derived by Kety based on the Fick's principle and a two-compartment model of blood-tissue exchange and tissue concentration at a single data point (Sakurada et al., 1978). The procedure, in which the tissue is to be in equilibrium with venous blood, will be referred to as the tissue equilibration model. In this article, effects of the concentration gradient of tracer along the length of the capillary (tube model) and the transverse heterogeneity in the capillary transit time (distributed model) on the determination of LCBF were theoretically analyzed for the tissue sampling method. Similarities and differences among these models are explored. The rank order of the LCBF calculated by using arterial blood concentration time courses and the tissue concentration of tracer based on each model were tube model (model II) less than distributed model (model III) less than venous equilibrium model (model I). Data on 14C-IAP kinetics reported by Ohno et al. (1979) were employed. The LCBFs calculated based on model I were 45-260% larger than those in models II or III. To discriminate among three models, we propose to examine the effect of altering the venous infusion time of tracer on the apparent tissue-to-blood concentration ratio (lambda app). A range of the ratio of the predicted lambda app in models II or III to that in model I was from 0.6 to 1.3. In the future, there may be a need to determine which model should be used to calculate the LCBF based on this discriminator and to develop another discriminator by using multiple data points based on positron emission tomography.