Quantification of metabolic rate of glucose uptake with [18F]FDG
Figure 1. [18F]2-fluoro-2-deoxy-D-glucose ([18F]FDG) is glucose analogue, where fluorine-18 (halflife 109.8 min) substitutes the hydroxyl group at the second position in the glucose molecule.
As a glucose analogue, [18F]FDG is commonly used to measure tissue glucose consumption in vivo. [18F]FDG enters the tissue via glucose transporters, and can then be either metabolized to [18F]FDG-6-phosphate, or transported from tissue back to blood. [18F]FDG-6-phosphate cannot be transported out of the tissue, and in most tissues further metabolism and dephosphorylation of [18F]FDG-6-phosphate is slow; phosphorylated [18F]FDG is therefore often assumed to be trapped inside the cells. However, depending on the tissue and animal species, further metabolism of [18F]FDG-6-P may be significant (Rokka et al., 2017).
The autoradiographic method for measuring regional metabolic rate of glucose in the brain of rat using [14C]deoxyglucose (Sokoloff et al. 1977) has been modified for human studies using positron emission tomography (PET) and [18F]2-fluoro-2-deoxy-D-glucose ([18F]FDG) by Phelps et al. (1979) and Reivich et al. (1979).
The three-compartment model with four rate constants K1*, k2*, k3* and k4* is often simplified by assuming that the dephosphorylation rate of FDG-6-phosphate in brain tissue is small enough that it can be ignored (k4*=0). At least a part of the observed k4* may be explained by tissue heterogeneity (Schmidt et al., 1992).
Metabolic rate of glucose (MRglu) can be calculated from equation
, where Cglu is the concentration of glucose in plasma, and LC is the lumped constant.
Originally FDG studies were always analyzed using compartment model with the three or four rate constants. Only later it was found out that a graphical method, Patlak plot (Patlak and Blasberg, 1985) can be used to directly estimate the combination of the model rate constants. This combined term, net uptake rate for FDG (Ki*) is robust and very fast to calculate, and is therefore also suitable for computation of parametric images.
Estimation of metabolic rate of glucose
If dynamic PET data is collected, then the Patlak graphical analysis is recommended for calculation of metabolic rate of glucose:
If only static PET scan is available, then Patlak plot can not be used; calculate FUR as a substitute for Ki:
Manual blood samples are collected for the measurement of concentration of FDG in plasma, to enable absolute quantification of glucose uptake. Semiquantitative methods (for example SUV), which do not require blood sampling, are be affected by differences in plasma clearance, that is, uptake to other organs, or excretion of FDG: for example, medication for hypertension may slow down the renal excretion of FDG (Zhao et la., 2013).
PET data is processed as usual.
Blood samples are processed in the PET blood laboratory to time-activity curves, which can be used as such. With high-resolution PET scanner (HRRT) and careful optimization of image reconstruction it is possible to avoid arterial blood sampling by deriving blood TAC from carotid artery in the dynamic image (Huisman et al., 2013). Three manual blood samples are needed to calibrate the image-derived input curve.
For Patlak analysis, correction for time delay is not required, but the estimates of compartmental model parameters may be very sensitive to the effect of time delay.
If LC at different study conditions is to be estimated, too, then use fcmrglu, for example:
fcmrglu ua2826ap.delay.kbq ua2826dy1.dft 5.2 ua2826fcmrglu.res
Make sure that you are using appropriate values for τ and φ. Note that the brain FDG model must not be applied to skeletal muscle!
Please read MET5731. Recommended LC for Patlak analysis of heart FDG studies is 1.
Hepatic glucose uptake can be estimated using FDG, if the dual input (arterial and portal vein input) is taken into consideration in the model, as validated in pig studies (Brix et al., 2001; Munk et al., 2001; Iozzo et al., 2007; Kudomi et al., 2009; Winterdahl et al., 2011; Rani et al., 2013). Alternatively the gut could be included as a compartment in the model (Vivaldi et al., 2013; Garbarino et al., 2015). Notice that liver and intestine are gluconeogenetic organs, expressing glucose-6-phosphatase, and therefore we cannot assume that FDG uptake is irreversible.
Arterial plasma curve alone can be used as model input especially for liver tumours which are mainly fed by the hepatic artery (Choi et al., 1994; Keiding et al., 2000; Brix et al., 2001; Fukuda et al., 2004).
Liver is often used as a reference tissue in diagnostic imaging when calculating SUV ratio (SUR) (Laffon et al., 2011; Boktor et al., 2013; Keramida et al., 2015). Liver has also been used as reference tissue in mice BAT studies after intraperitoneal FDG injection (Wu et al., 2014).
Note however that SUV in liver correlates positively with serum glucose levels, even when patients have been fasting and serum glucose concentrations are considered to be at acceptable levels (Webb et al., 2015), has high interindividual variance and is dependent on BMI and sex (Rubello et al., 2015).
Please read MET5736 and the publication by Bertoldo et al., 2001. Recommended LC for Patlak analysis of skeletal muscle FDG studies is 1.2 (Peltoniemi et al., 2000).
2-tissue compartmental model for irreversible uptake has been applied to gastrocnemius muscles of mice, using image-derived input function from inferior vena cava (Cochran et al, 2016).
Lungs have been studied using FDG PET, but a lung-specific model may be required (Schroeder et al., 2008).
FDG can be used to study bone infections and to separate normal bone healing from bone infections (Koort et al. 2004 and 2005; Lankinen et al., 2012; Odekerken et al., 2014).
Metabolism of glucose in bone marrow can be measured using FDG PET (Huovinen et al., 2014). Measurement of FDG uptake in oncological studies has shown some promise.
Clinical oncology studies should follow the published recommendations and guidelines (Shankar et al., 2006; Boellaard et al., 2015), even though it may be difficult to do so (de Jong et al., 2017).
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Created at: 2009-01-09
Updated at: 2017-09-15
Written by: Vesa Oikonen