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). [18F]FDG does not distribute into the intracellular lipid droplets, which will decrease the apparent tracer uptake measured per tissue volume; this should be considered when studying organs with high and variable fat content.
[18F]FDG study may be combined with hyperinsulinemic euglycemic clamp (DeFronzo et al., 1979) to assess the insulin sensitivity of specific organs (Nuutila et al., 1993; Johansson et al., 2017). The whole body insulin sensitivity can be measured simultaneously as the M value by dividing the mean glucose infusion rate by the lean body mass.
[18F]2-fluoro-2-deoxy-D-glucose has high affinity for GLUTs, and low affinity for ATP-dependent sodium-glucose transporters SGLTs. The relative role of GLUTs and SGLTs can be studied with related tracers, for example with α-methyl-4-[18F]-fluoro-4-deoxy-D-glucopyranoside (Me-4FDG) and 4-deoxy-4-[18F]-fluoro-D-glucose (4-FDG); Me-4FDG has high affinity for SGLT1, medium affinity for SGLT2, and very low affinity for GLUTs; 4-FDG is a substrate for both GLUTs and SGLTs (Sala-Rabanal et al., 2016).
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.
Patlak plot provides not only Ki*, but also an index of FDG distribution volume in the tissue as the plots intercept with y axis. In tissues with high fat content both the distribution volume and Ki* will be reduced. Metabolic rate of glucose can be corrected for this effect by dividing Ki* with the Patlak plot intercept value (Keranida et al., 2016).
Estimation 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:
Input function for FDG
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). Correction for plasma glucose concentration is straightforward for Ki* but not for SUV.
Arterialized venous blood sampling is often used instead of arterial sampling in FDG studies, although it increases the variability and larger sample size is needed. Blood samples are processed in the PET blood laboratory to time-activity curves, which can be used as such. Image-derived input function (Christensen et al., 2014), Model-based input function, or population-based input function may be used as an alternative to blood sampling in some cases. When necessary, blood TAC can be converted to plasma TAC, or vice versa, using conversion functions (Figure 2) that are available in programs b2plasma and p2blood.
Figure 2. Plasma-to-blood ratio functions for FDG in human subjects, rats, and mice. The different conversion functions for mice, based on separate publications, may indicate differences between mice populations.
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.
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.
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.
For precise quantification the analysis methods should account for the spillover and partial volume effects caused by respiratory motion and beating of the heart, especially in small animal studies.
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).
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. Yet, desphophorylation in the liver is relatively slow compared with phosphorylation, even during fasting.
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). Glucose uptake in liver may be higher in men than in women, even when corrected for the different fat contents (Keranida and Peters, 2017).
Liver is often used as a reference tissue in diagnostic imaging when calculating SUV ratio (SUR, or tumour-liver ratio, TLR) (Laffon et al., 2011; Boktor et al., 2013; Keramida et al., 2015; Hofheinz et al., 2016). 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 inter-individual variance and is dependent on BMI and sex (Rubello et al., 2015). Although tumour-to-blood ratio is preferable to tumour-to-liver ratio, both are clearly better than SUV in oncological FDG PET (Hofheinz et al., 2016).
Metabolic rate of glucose in intestine (duodenum and jejunum) and colon has been measured using FDG PET (Honka et al., 2013; Bahler et al., 2017; Kang et al., 2017; Koffert et al., 2017; Motiani et al., 2017). Honka et al. (2013) and Motiani et al. (2017) applied LC=1.15, and a recovery coefficient of 2.5 for the colon.
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.
Glucose consumption in tumours is increased because of increased glycolysis (Warburg effect). GLUT1 and GLUT3 are commonly overexpressed by tumour cells, and the level of hexokinase 2 is usually high. The enhanced glucose uptake can be quantitated or semi-quantitated using [18F]FDG. Part of the increase may be due to inflammation.
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).
- Net influx rate
- Multiple-time graphical analysis (MTGA)
- Fitting compartmental models
- Metabolic tumour volume (MTV)
Basu S, Zaidi H, Holm S, Alavi A. Quantitative techniques in PET-CT imaging. Curr Med Imaging Rev. 2011; 7: 216-233.
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.
Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, Verzijlbergen FJ, Barrington SF, Pike LC, Weber WA, Stroobants S, Delbeke D, Donohoe KJ, Holbrook S, Graham MM, Testanera G, Hoekstra OS, Zijlstra J, Visser E, Hoekstra CJ, Pruim J, Willemsen A, Arends B, Kotzerke J, Bockisch A, Beyer T, Chiti A, Krause BJ. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging 2015; 42(2): 328-354.
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.
Christensen AN, Reichkendler MH, Larsen R, Auerbach P, Højgaard L, Nielsen HB, Ploug T, Stallknecht B, Holm S. Calibrated image-derived input functions for the determination of the metabolic uptake rate of glucose with [18F]-FDG. Nucl Med Commun. 2014; 35: 353-361.
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; 59(9): 1977-1984. doi: 10.1007/s00125-016-3993-5.
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.
DeFronzo RA, Tobin JD, Andres R. Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am J Physiol. 1979; 237(3): E214-E223.
Hofheinz F, Bütof R, Apostolova I, Zöphel K, Steffen IG, Amthauer H, Kotzerke J, Baumann M, van den Hoff J. An investigation of the relation between tumor-to-liver ratio (TLR) and tumor-to-blood standard uptake ratio (SUR) in oncological FDG PET. EJNMMI Res. 2016; 6: 19.
de Jong EEC, van Elmpt W, Hoekstra OS, Groen HJM, Smit EF, Boellaard R, Lambin P, Dingemans A-MC. Quality assessment of positron emission tomography scans: recommendations for future multicentre trials. Acta Oncol. 2017;56(11): 1459-1464. doi: 10.1080/0284186X.2017.1346824.
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; 5(1): 107. doi: 10.1186/s13550-015-0107-1.
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 S-C, Phelps ME, Hoffman EJ, Sideris K, Selin CJ, Kuhl DE. Noninvasive determination of local cerebral metabolic rate of glucose in man. Am J Physiol. 1980; 238: E69-E82.
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, Knuuti J, Ruotsalainen U, Koivisto VA, Eronen E, Teräs M, Bergman J, Haaparanta M, Voipio-Pulkki L-M, Viikari J, Rönnemaa T, Wegelius U, Yki-Järvinen H. Insulin resistance is localized to skeletal but not heart muscle in type 1 diabetes. Am J Physiol. 1993; 264: E756-E762.
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.
Rokka J, Grönroos TJ, Viljanen T, Solin O, Haaparanta-Solin M. HPLC and TLC methods for analysis of [18F]FDG and its metabolites from biological samples. J Chromatogr B 2017; 1048: 140-149.
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; 40(8): e405-e410.
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.
Shankar LK, Hoffman JM, Bacharach S, Graham MM, Karp J, Lammertsma AA, Larson S, Mankoff DA, Siegel BA, Van den Abbeele A, Yap J, Sullivan D; National Cancer Institute. Consensus recommendations for the use of 18F-FDG PET as an indicator of therapeutic response in patients in National Cancer Institute Trials. J Nucl Med. 2006; 47(6): 1059-1066.
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. The [14C]deoxyglucose method for the measurement of local cerebral glucose utilization: theory, procedure, and normal values in the conscious and anesthetized albino rat. J Neurochem. 1977; 28(5): 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: 2017-12-17
Written by: Vesa Oikonen