Kinetic analysis of FDG in rat liver: Effect of dietary intervention on arterial and portal vein input

Abstract

Introduction Dietary conditions may affect liver [18F]FDG kinetics due to arterial and portal vein (PV) input. The purpose of this study was to evaluate kinetic models of [18F]FDG metabolism under a wide range of dietary interventions taking into account variations in arterial (HA) and portal vein (PV) input. Methods The study consisted of three groups of rats maintained under different diet interventions: 12 h fasted, 24 h fasted and those fed with high fructose diet. [15O]H2O PET imaging was used to characterize liver flow contribution from HA and PV to the liver's dual input function (DIF). [18F]FDG PET imaging was used to characterize liver metabolism. Differences in [18F]FDG kinetics in HA, PV and liver under different diet interventions were investigated. An arterial to PV Transfer Function (TF) was optimized in all three dietary states to noninvasively estimate PV activity. Finally, two compartment 3-parameter (2C3P), two compartment 4-parameter (2C4P), two compartment 5-parameter (2C5P), and three compartment 5-parameter (3C5P) models were evaluated and compared to describe the kinetics of [18F]FDG in the liver across diet interventions. Sensitivity of the compartmental models to ratios of HA to PV flow fractions was further investigated. Results Differences were found in HA and PV [18F]FDG kinetics across 12 h fasted, 24 h fasted and high fructose fed diet interventions. A two exponential TF model was able to estimate portal activity in all the three diet interventions. Statistical analysis suggests that a 2C3P model configuration was adequate to describe the kinetics of [18F]FDG in the liver under wide ranging dietary interventions. The net influx of [18F]FDG was lowest in the 12 h fasted group, followed by 24 h fasted group, and high fructose diet. Conclusions A TF was optimized to non-invasively estimate PV time activity curve in different dietary states. Several kinetic models were assessed and a 2C3P model was sufficient to describe [18F]FDG liver kinetics despite differences in HA and PV kinetics across wide ranging dietary interventions. The observations have broader implications for the quantification of liver metabolism in metabolic disorders and cancer, among others.

A new compartmental method for the analysis of liver FDG kinetics in small animal models

“…According to standard results [ 17 ], also the liver can be described as a two-compartment model, one consisting of non-metabolized, free, tracer (compartment f ), and one consisting of phosphorylated, metabolized tracer (compartment m ), where, as in the case of the gut, dephosphorylation is explicitly allowed. Tracer inputs through the HA and PV (compartments a and p , respectively) are modeled in an independent manner, i.e., there is no mixing of blood from the two vessels before entrance into the liver.…”

“…Micro-PET data provide information on the overall concentrations in ROIs drawn on the gut and liver throughout the whole acquisition. Therefore, denoting with and such experimental concentrations, we can write the following two equations for the micro-PET data: where the numerical coefficients 0.11 and 0.89 indicate the rate of arterial and venous contributions to the hepatic blood content V per unit volume [ 8 , 17 ]. Further, we assumed for V the physiologically sound value of 0.3 [ 9 ].…”

Fluid dynamics analyses of the intrahepatic portal vein tributaries using 7-T MRI

“…An increase in PV flow after oral intake has been demonstrated in humans, [23][24][25] but, to date, we have lacked the ability to quantify this increase with precision in small animals. 26 This study detected small flow changes in the narrow PV branches after a meal challenge, indicating that, for the first time, small flow changes in the narrow intrahepatic PV branches of small animals are available for study. PV flow dynamics can now be accurately and serially monitored with minimal harm to animals, and complex flow characteristics can be quantified in terms of previouslyunmeasurable parameters.…”

Compartmental analysis of dynamic nuclear medicine data: models and identifiability

“…where V is a fraction in 0, 1 ( ), represent the blood fraction in the tissue under examination, and in this analysis, as already observed, it is assumed to be known. Since C 1e is the concentration of tracer in blood, it is directly measurable [19,27]. It is therefore possible to rewrite the previous equation as…”