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Estimation of the ¹⁸F-FDG Input Function in Mice by Use of Dynamic Small-Animal PET and Minimal Blood Sample Data

Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, University of California, Los Angeles, California

View larger version (23K): [in this window] [in a new window]   FIGURE 1.  (A) 4K open-loop model describing 18F-FDG tissue uptake and metabolism. Left-hand compartment (q1) represents input (forcing) function described by sum of 4 exponential terms, center compartment (q2) represents extravascular 18F-FDG in organ space, and right-hand compartment (q3) represents phosphorylated 18F-FDG in organ space. Measurement model (Eq. 5) is defined as total amount of 18F-FDG and 18F-FDG-6-phosphate (18F-FDG-6-p) in organ space. (B) Compartmental model for partial-volume, delay, and dispersion corrections of early portion (t < 60 s) of LV time–activity curve. Left-hand compartment () represents forcing function defined by LV time–activity curve obtained from reconstructed PET image at early times (<1 min), and center () and right-hand () compartments represent arterial concentrations of 18F-FDG in organ (o) and femoral artery catheter (c) sites, respectively. Two delay components, modeled as string of 5 compartments with fast mass transfer rates, are included to account for time it takes drug to travel from LV to organ and catheter sites. Superscript c indicates that these are model equations used for correction of early-time LV data.

View larger version (24K): [in this window] [in a new window]   FIGURE 2.  Partial-volume, delay, and dispersion corrections of LV time–activity curve. Results of study m17464 are shown. Gray line represents (interpolated) small-animal PET LV time–activity curve used for forcing function in LV correction model (Fig. 1B). Circles represent 18F-FDG concentrations measured from serial femoral artery blood samples to which LV correction model was fitted (Eq. 10). Dashed black line represents predicted blood 18F-FDG concentration in organ of interest (e.g., muscle or liver), which is equal to amount of tracer in compartment (Fig. 1B).

View larger version (35K): [in this window] [in a new window]   FIGURE 3.  (A and B) 4K 18F-FDG model fitted to corrected PET time–activity curves and 2 blood samples corrected for RBC uptake for studies m11082 (A) and m17156 (B). Black, gray, and dashed lines represent best-fit model simulations of 18F-FDG time–activity curves in plasma, liver, and muscle, respectively. Liver and muscle PET time–activity curves are shown as solid gray and open black circles, respectively; plasma data are shown as solid black circles. (C and D) Predicted input function with all plasma data shown for studies m11082 (C) and m17156 (D). Simulated plasma time–activity curve (black line) and measured data points (solid black circles) from Figures 3A and 3B are shown along with remaining serial blood sample data (open gray circles) that were excluded from input function prediction process.

View larger version (11K): [in this window] [in a new window]   FIGURE 4.  Comparison of AUCP and AUCGS for all 16 studies (A) and predicted area under plasma time–activity curve vs. area under liver time–activity curve (B).

View larger version (16K): [in this window] [in a new window]   FIGURE 5.  Comparison of predicted (P) and gold standard (GS) Patlak regression coefficients (Ki) for selected tissues.

View larger version (11K): [in this window] [in a new window]   FIGURE 6.  Plasma time–activity curve simulations for all 16 studies (mean ± SD). 18F-FDG kinetics in plasma exhibited 2 distinct profiles in these studies and could be described as fast (dashed line) and slow (solid line) removal of 18F-FDG from plasma. Fast curve was average plasma time–activity curve for studies m09940, m10610, m10911, m11043, m11082, m11122, and m11467; slow curve was average for remaining 9 studies. Two-way ANOVA was performed with GraphPad Prism (23); P value of 0.0156 was calculated, demonstrating statistically significant difference between 2 groups of plasma time–activity curves.

View larger version (20K): [in this window] [in a new window]   FIGURE 7.  Algorithm for estimation of input function. Same Bayesian constraints were applied to animals exhibiting fast clearance and slow clearance of 18F-FDG from plasma. TAC = time–activity curve.