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Quantification of [18F]FDG Uptake in the Normal Liver Using Dynamic PET: Impact and Modeling of the Dual Hepatic Blood Supply

Gunnar Brix, Sibylle I. Ziegler, Matthias E. Bellemann, Josef Doll, Rudolph Schosser, Robert Lucht, Heiner Krieter, Dietmar Nosske and Uwe Haberkorn

Research Program Radiological Diagnostics and Therapy, German Cancer Research Center, Heidelberg; Department of Experimental Surgery, University of Heidelberg, Heidelberg; Department of Medical Radiation Hygiene, Institute of Radiation Hygiene, Federal Office for Radiation Protection, Neuherberg; Department of Nuclear Medicine, Technische Universitaet Muenchen, Munich; and Department of Biomedical Engineering, University of Applied Sciences, Jena, Germany



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FIGURE 1. Representative PET scans show FDG distribution in a foxhound 0.25 min (frame 1, left) and 12.5 min (frame 15, right) after intravenous FDG administration. Time–activity courses were derived from ROIs defined over aorta and over right and left side of liver.

 


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FIGURE 2. (A) Typical blood activity curves determined in aorta and portal vein of foxhound 4. Data were determined simultaneously by 2 independent detector systems (fluid monitors) with temporal resolution of 1 s and directly from aorta visualized on PET scans (Fig. 1, left). For better comparison, PET curve was shifted so that its maximum aligns with maximum of arterial blood curve measured by fluid monitor. (B) Detail of A, which shows that initial part of portal input is characterized by much lower maximum activity concentration and later moment of maximum concentration relative to arterial input.

 


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FIGURE 3. Fast and slow component of average system function approximating dispersion of arterial input on its way through gastrointestinal tract to portal vein in 5 foxhounds. Corresponding fit parameters estimated according to Equation 1 are summarized in Table 1.

 


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FIGURE 4. Approximation of venous input function measured directly in portal vein of foxhound 4 (Table 1) by convolution of arterial input measured by external detector with individual system function (#1), arterial input measured by external detector with average system function (#2), and arterial input determined by PET with average system function (#3). Only first 20 min are shown.

 


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FIGURE 5. Comparison of 5 hepatic input functions defined by Equations 2–6 for foxhound 4 (Table 1). Input function #1 is weighted sum of arterial and portal venous blood activities measured by 2 independent external detector systems. Other functions approximate hepatic blood supply by means of arterial activity values obtained from external detector system (A) and from dynamically acquired PET scans (B). Input functions #2 and #4 correspond to single-input model in which hepatic blood supply is described solely by arterial input, whereas input functions #3 and #5 pertain to dual-input scenario in which hepatic input is weighted sum of arterial input and portal venous input estimated by convolving measured arterial time–activity course with average system function (Fig. 4). Only first 20 min are shown.

 


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FIGURE 6. SUVs measured dynamically by PET from normal liver tissue of foxhound 4. Curves show 3-compartment model fits computed for input models #1–#3 estimated from external blood activity measurements (A) and input models #4 and #5 estimated from blood activity measurements with PET (B)

 


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FIGURE 7. Effect of 5 different input models defined by Equations 2–6 on pharmacokinetic parameter estimates for 3-compartment model characterizing transport and metabolism of FDG in normal liver. (A) K1. (B) k2. (C) k3. (D) k4. Significant differences (P < 0.05) between pairs of parameter distributions are marked by asterisk. (E) Numeric scores computed in accordance with Schwarz criterion to compare adequacy of model fits to liver data using different input models. Symbols show individual fit values; horizontal lines indicate corresponding mean values.

 





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