Quantification of metabolic rate of glucose uptake with [18F]FDG
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).
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).
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.
Bertoldo A, Peltoniemi P, Oikonen V, Knuuti J, Nuutila P, Cobelli C. Kinetic modeling of [18F]FDG in skeletal muscle by PET: a four-compartment five-rate-constant model. Am J Physiol Endocrinol Metab. 2001; 281:E524-E536.
Boktor RR, Walker G, Stacey R, Gledhill S, Pitman AG. Reference range for intrapatient variability in blood-pool and liver SUV for 18F-FDG PET. J Nucl Med. 2013; 54: 677-682.
Brix G, Ziegler SI, Bellemann ME, Doll J, Schosser R, Lucht R, Krieter H, Nosske D, Haberkorn U. Quantification of [18F]FDG uptake in the normal liver using dynamic PET: impact and modeling of the dual hepatic blood supply. J Nucl Med. 2001; 42: 1265-1273.
Cochran BJ, Ryder WJ, Parmar A, Tang S, Reilhac A, Arthur A, Charil A, Hamze H, Barter PJ, Kritharides L, Meikle SR, Gregoire MC, Rye KA. In vivo PET imaging with [18F]FDG to explain improved glucose uptake in an apolipoprotein A-I treated mouse model of diabetes. Diabetologia 2016 (in press).
Dai X, Chen Z, Tian J. Performance evaluation of kinetic parameter estimation methods in dynamic FDG-PET studies. Nucl Med Commun. 2011; 32(1): 4-16.
Fukuda K, Taniguchi H, Koh T, Kunishima S, Yamagishi H. Relationships between oxygen and glucose metabolism in human liver tumours: positron emission tomography using 15O and 18F-deoxyglucose. Nucl Med Commun. 2004; 25: 577-583.
Garbarino S, Caviglia G, Brignone M, Massollo M, Sambuceti G, Piana M. Estimate of FDG excretion by means of compartmental analysis and ant colony optimization of nuclear medicine data. Comput Math Methods Med. 2013; 793142.
Garbarino S, Caviglia G, Sambuceti G, Benvenuto F, Piana M. A novel description of FDG excretion in the renal system: application to metformin-treated models. Phys Med Biol. 2014; 59(10): 2469-2484.
Garbarino S, Vivaldi S, Delbary F, Caviglia G, Piana M, Marini C, Capitanio S, Calamia I, Buschiazzo A, Sambuceti G. A new compartmental method for the analysis of liver FDG kinetics in small animal models. EJNMMI Res. 2015 (in press).
Gjedde A. Positron emission tomography of brain glucose metabolism with [18F]fluorodeoxyglucose in humans. In: Hirrlinger J, Waagepetersen HS (eds.), Brain Energy Metabolism, Neuromethods, vol 90, Springer, 2014, p 341-364.
Hahn A, Gryglewski G, Nics L, Hienert M, Rischka L, Vraka C, Sigurdardottir H, Vanicek T, James GM, Seiger R, Kautzky A, Silberbauer L, Wadsak W, Mitterhauser M, Hacker M, Kasper S, Lanzenberger R. Quantification of task-specific glucose metabolism with constant infusion of 18F-FDG. J Nucl Med. 2016; 57(12): 1933-1940.
Hawkins RA, Phelps ME, Huang S-C, Kuhl DE. Effect of ischemia on quantitation of local cerebral glucose metabolic rate in man. J Cereb Blood Flow Metab 1981; 1: 37-51.
Honka H, Hannukainen JC, Tarkia M, Karlsson H, Saunavaara V, Salminen P, Soinio M, Mikkola K, Kudomi N, Oikonen V, Haaparanta-Solin M, Roivainen A, Parkkola R, Iozzo P, Nuutila P. Pancreatic metabolism, blood flow, and β-cell function in obese humans. J Clin Endocrinol Metab. 2014; 99(6): E981-E990.
Huang X, Bao S, Huang S-C. Clustering-based linear least square fitting method for generation of parametric images in dynamic FDG PET studies. Int J Biomed Imaging. 2007:65641.
Huisman MC, van Golen LW, Hoetjes NJ, Greuter HN, Schober P, Ijzerman RG, Diamant M, Lammertsma AA. Cerebral blood flow and glucose metabolism in healthy volunteers measured using a high-resolution PET scanner. EJNMMI Research 2012; 2:63.
Huovinen V, Saunavaara V, Kiviranta R, Tarkia M, Honka H, Stark C, Laine J, Linderborg K, Tuomikoski P, Badeau RM, Knuuti J, Nuutila P, Parkkola R. Vertebral bone marrow glucose uptake is inversely associated with bone marrow fat in diabetic and healthy pigs: [18F]FDG-PET and MRI study. Bone 2014; 61: 33-38.
Iozzo P, Jarvisalo MJ, Kiss J, Borra R, Naum GA, Viljanen A, Viljanen T, Gastaldelli A, Buzzigoli E, Guiducci L, Barsotti E, Savunen T, Knuuti J, Haaparanta-Solin M, Ferrannini E, Nuutila P. Quantification of liver glucose metabolism by positron emission tomography: validation study in pigs. Gastroenterology 2007; 132: 531-542.
Kalliokoski KK, Bojsen-Møller J, Seppänen M, Johansson J, Kjaer M, Teräs M, Magnusson SP. Contraction-induced [18F]-fluoro-deoxy-glucose uptake can be measured in human calf muscle using high-resolution PET. Clin Physiol Functional Imag. 2007; 27(4): 239-241.
Keiding S, Munk OL, Schiøtt KM, Hansen SB. Dynamic 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography of liver tumours without blood sampling. Eur J Nucl Med. 2000; 27: 407-412.
Keramida G, Dizdarevic S, Bush J, Peters AM. Quantification of tumour 18F-FDG uptake: Normalize to blood glucose or scale to liver uptake? Eur Radiol. 2015; 25: 2701-2708.
Koort JK, Mäkinen TJ, Knuuti J, Jalava J, Aro HT. Comparative 18F-FDG PET of experimental Staphylococcus aureus osteomyelitis and normal bone healing. J Nucl Med. 2004; 45(8): 1406-1411.
Koort JK, Mäkinen TJ, Suokas E, Veiranto M, Jalava J, Knuuti J, Törmälä P, Aro HT. Efficacy of ciprofloxacin-releasing bioabsorbable osteoconductive bone defect filler for treatment of experimental osteomyelitis due to Staphylococcus aureus. Antimicrob Agents Chemother. 2005; 49(4): 1502-1508.
Kudomi N, Järvisalo MJ, Kiss J, Borra R, Viljanen A, Viljanen T, Savunen T, Knuuti J, Iida H, Nuutila P, Iozzo P. Non-invasive estimation of hepatic glucose uptake from [18F]FDG PET images using tissue-derived input functions. Eur J Nucl Med Mol Imaging 2009; 36(12): 2014-2026.
Laffon E, Adhoute X, de Clermont H, Marthan R. Is liver SUV stable over time in 18F-FDG PET imaging? J Nucl Med Technol. 2011; 39: 258-263.
Lankinen P, Lehtimäki K, Hakanen AJ, Roivainen A, Aro HT. A comparative 18F-FDG PET/CT imaging of experimental Staphylococcus aureus osteomyelitis and Staphylococcus epidermis foreign-body-associated infection in the rabbit tibia. EJNMMI Res. 2012; 2(1): 41.
Munk OL, Bass L, Roelsgaard K, Bender D, Hansen SB, Keiding S. Liver kinetics of glucose analogs measured in pigs by PET: importance of dual-input blood sampling. J Nucl Med. 2001; 42(5): 795-801.
Nuutila P, Peltoniemi P, Oikonen V, Larmola K, Kemppainen J, Takala T, Sipilä H, Oksanen A, Ruotsalainen U, Bolli GB, Yki-Järvinen H. Enhanced stimulation of glucose uptake by insulin increases exercise-stimulated glucose uptake in skeletal muscle in humans: studies using [15O]O2, [15O]H2O, [18F]fluoro-deoxy-glucose, and positron emission tomography. Diabetes 2000; 49:1084-1091.
Odekerken JC, Walenkamp GH, Brans BT, Welting TJ, Arts JJ. The longitudinal assessment of osteomyelitis development by molecular imaging in a rabbit model. Biomed Res Int. 2014; 424652.
Patlak CS, Blasberg RG. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Generalizations. J Cereb Blood Flow Metab 1985; 5:584-590.
Peltoniemi P, Lönnroth P, Laine H, Oikonen V, Tolvanen T, Grönroos T, Strindberg L, Knuuti J, Nuutila P. Lumped constant for [18F]fluorodeoxyglucose in skeletal muscles of obese and nonobese humans. Am J Physiol Endocrinol Metab. 2000; 279(5): E1122-E1130.
Phelps ME, Huang S-C, Hoffman EJ, Selin C, Sokoloff L, Kuhl DE. Tomographic measurement of local cerebral glucose metabolic rate in humans with [F-18]2-fluoro-2-deoxy-D-glucose: validation of method. Ann Neurol. 1979; 6: 371-388.
Phelps ME, Huang S-C, Mazziotta JC, Hawkins RA. Alternate approach for examining stability of the deoxyglucose model lumped constant. J Cereb Blood Flow Metab. 1983; 3(Suppl 1): S13-S14.
Piert M, Koeppe RA, Giordani B, Berent S, Kuhl DE. Diminished glucose transport and phosphorylation in Alzheimer’s disease determined by dynamic FDG-PET. J Nucl Med. 1996; 37: 201-208.
Raitakari M, Nuutila P, Ruotsalainen U, Laine H, Teräs M, Iida H, Mäkimattila S, Utriainen T, Oikonen V, Sipilä H, Haaparanta M, Solin O, Wegelius U, Knuuti J, Yki-Järvinen H. Evidence for dissociation of insulin stimulation of blood flow and glucose uptake in human skeletal muscle. Studies using [15O]H2O, [18F]fluoro-2-deoxy-D-glucose, and positron emission tomography. Diabetes 1996; 45: 1471-1477.
Rani SD, Nemanich ST, Fettig N, Shoghi KI. Kinetic analysis of FDG in rat liver: effect of dietary intervention on arterial and portal vein input. Nucl Med Biol. 2013; 40: 537-546.
Reivich M, Kuhl D, Wolf A, Greenberg J, Phelps M, Ido T, Casella V, Fowler J, Hoffman E, Alavi A, Som P, Sokoloff L. The [18F]fluorodeoxyglucose method for the measurement of local cerebral glucose utilization in man. Circ Res. 1979; 44: 127-137.
Rubello D, Gordien P, Morliere C, Guyot M, Bordenave L, Colletti PM, Hindié E. Variability of hepatic 18F-FDG uptake at interim PET in patients with Hodgin lymphoma. Clin Nucl Med. 2015 (in press).
Sasaki H, Kanno I, Murakami M, Shishido F, Uemura K. Tomographic mapping of kinetic rate constants in the fluorodeoxyglucose model using dynamic positron emission tomography. J Cereb Blood Flow Metab. 1986; 6: 447-454.
Schmidt K, Lucignani G, Moresco RM, Rizzo G, Gilardi MC, Messa C, Colombo F, Fazio F, Sokoloff L. Errors introduced by tissue heterogeneity in estimation of local cerebral glucose utilization with current kinetic models of the [18F]fluorodeoxyglucose method. J Cereb Blood Flow Metab. 1992; 12: 823-834.
Schroeder T, Melo MFV, Musch G, Harris RS, Venegas JG, Winkler T. Modeling Pulmonary kinetics of 2-deoxy-2-[18F]fluoro-D-glucose during acute lung injury. Acad Radiol. 2008; 15(6): 763-775.
Slimani L, Oikonen V, Hällsten K, Savisto N, Knuuti J, Nuutila P, Iozzo P. Exercise restores skeletal muscle glucose delivery but not insulin-mediated glucose transport and phosphorylation in obese subjects. J Clin Endocrinol Metab 2006; 91(9): 3394-3403.
Sokoloff L, Reivich M, Kennedy C, Des Rosiers MH, Patlak CS, Pettigrew KD, Sakurada O, Shinohara M. J Neurochem. 1977; 28: 897-916.
Varrone A, Asenbaum S, Borght T, Booij J, Nobili F, Någren K, Darcourt J, Kapucu ÖL, Tatsch K, Bartenstein P, Laere K. EANM procedure guidelines for PET brain imaging using [18F]FDG, version 2. Eur J Nucl Med Mol Imaging 2009; 36: 21032110.
Villien M, Wey H-Y, Mandeville JB, Catana C, Polimeni JR, Sander CY, Zürcher NR, Fowler JS, Rosen BR, Hooker JM. Dynamic functional imaging of brain glucose utilization using fPET-FDG. NeuroImage 2014; 100: 192-199.
Vivaldi V, Garbarino S, Caviglia G, Piana M, Sambuceti G. Compartmental analysis of nuclear imaging data for the quantification of FDG liver metabolism. arXiv: 1305.7435 [q-bio.TO] 2013.
Webb RL, Landau E, Klein D, DiPoce J, Volkin D, Belman J, Voutsinas N, Brenner A. Effects of varying serum glucose levels on 18F-FDG biodistribution. Nucl Med Commun. 2015 (in press).
Winterdahl M, Keiding S, Sørensen M, Mortensen F, Viborg F, Alstrup AKO, Munk OL. Tracer input for kinetic modelling of liver physiology determined without sampling portal venous blood in pigs. Eur J Nucl Med Mol Imaging 2011; 38: 263-270.
Wu C, Cheng W, Sun Y, Dang Y, Gong F, Zhu H, Li N, Zhu Z. Activating brown adipose tissue for weight loss and lowering of blood glucose levels: a microPET study using obese and diabetic model mice. PLoS One 2014; 9(12): e113742.
Zhao Y, Zhao S, Kuge Y, Tamaki N. Elevated 18F-FDG levels in blood and organs after angiotensin II receptor blocker administration: experiment in mice administered Telmisartan. J Nucl Med. 2013; 54: 1384-1388.
Zhuang H, Codreanu I. Growing applications of FDG PET-CT imaging in non-oncologic conditions. J Biomed Res. 2015; 29(3): 189-202.
Created at: 2009-01-09
Updated at: 2016-12-08
Written by: Vesa Oikonen