Article Figures & Data
Figures
- FIGURE 1.
Correlation of 18F-FDG influx as calculated by 2-compartment model (influx [observed]) and predicted influx (influx [predicted]) using short acquisition protocol. Dynamic input data from 1 to 10 min (10 frames) and target area from 1 to 10 min (10 frames) and from 56 to 60 min (1 frame) were used for prediction, based on polynomial function (degree 2) for data fitting. Correlation coefficient of 0.9028 was obtained.
- FIGURE 2.
Correlation of VB as calculated by 2-compartment model (VB [observed) and predicted VB (VB [predicted]) using short acquisition protocol. Input data from 1 to 10 min (10 frames) and target area from 1 to 10 min (10 frames) and from 56 to 60 min (1 frame) were used for prediction, based on polynomial function (degree 2) for data fitting. Correlation coefficient of 0.9195 was obtained.
- FIGURE 3.
Correlation of k1 as calculated by 2-compartment model (k1 [observed) and predicted k1 (k1 [predicted]) using short acquisition protocol. Input data from 1 to 10 min (10 frames) and target area from 1 to 10 min (10 frames) and from 56 to 60 min (1 frame) were used for prediction, based on polynomial function (degree 2) for data fitting. Correlation coefficient of 0.9305 was obtained.
Tables
Minutes VB k1 k2 k3 k4 Influx 1 0.63 0.46 0.13 −0.06 −0.18 0.13 2 0.65 0.63 0.13 −0.06 −0.15 0.32 3 0.67 0.69 0.14 −0.06 −0.15 0.32 4 0.66 0.68 0.12 −0.05 −0.16 0.36 6 0.66 0.66 0.07 −0.02 −0.15 0.41 6 0.66 0.68 0.05 −0.03 −0.15 0.45 7 0.62 0.63 0.03 −0.03 −0.15 0.46 8 0.60 0.61 0.01 −0.01 −0.15 0.50 9 0.59 0.59 −0.02 −0.01 −0.16 0.53 10 0.59 0.57 −0.04 0.00 −0.14 0.54 11–12 0.60 0.57 −0.07 0.01 −0.15 0.58 13–14 0.59 0.57 −0.04 0.03 −0.15 0.62 15–16 0.56 0.55 −0.05 0.04 −0.15 0.65 17–18 0.56 0.55 −0.02 0.06 −0.16 0.66 19–20 0.53 0.51 −0.06 0.07 −0.16 0.68 21–25 0.52 0.50 −0.07 0.09 −0.16 0.71 26–30 0.49 0.51 −0.02 0.11 −0.17 0.72 31–35 0.48 0.48 −0.03 0.12 −0.18 0.74 36–40 0.47 0.49 −0.01 0.14 −0.18 0.75 41–45 0.46 0.46 −0.03 0.14 −0.18 0.75 46–50 0.44 0.44 −0.03 0.13 −0.19 0.75 51–55 0.43 0.45 −0.02 0.13 −0.19 0.75 56–60 0.43 0.45 −0.02 0.14 −0.21 0.75 The following kinetic parameters were assessed: VB, k1–k4, and influx for each individual acquisition interval. All correlation coefficients for VB and k1 are significant on P < 0.01 level. Correlation coefficients for influx are significant for 2–60 min. Although influx was correlated mainly with late acquisition times, VB and k1 were correlated with early acquisition times.
Parameter Input + target Target Summed image Influx 0.9028 0.8520 0.8302 VB 0.9195 0.7484 0.7974 k1 0.9305 0.8084 0.8424 Data are correlation coefficients for polynomial regression function (second order) using SUV data as independent variables and influx, VB, or k1 as dependent variable. All correlation coefficients are significant for P < 0.01. Best results were obtained for influx, VB, and k1 when both dynamic data of input region and target area were used to estimate parameters. Input + target = SUVs 1–10 min for input area and 1–10 min as well as 56–60 min for target area; target = SUVs 1–10 and 56–60 min for target area; summed image = summed 10-min image (1–10 min) for input area and summed 10-min image (1–10 min) as well as 56–60 min for target area.
