Table 2.
Grade | SUVmax | SUR | s1TCM | 1TCM | 2TCM | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
K1[10−2 min−1] | k2[10−2 min−1] | K1[10−2 min−1] | k2[10−2 min−1] | VB[ml/100 ml] | K1[10−2 min−1] | k2[10−2 min−1] | k3[10−2 min−1] | k4[10−2 min−1] | VB [ml/100 ml] |
|||
III | 1.7 | 2.1 | 6.6 | 10.4 | 4.9 | 6.8 | 4.2 | 9.3 | 59.2 | 18.4 | 4.1 | 2.2 |
3.9 | 3.4 | 12.4 | 9.2 | 10.3 | 6.8 | 6.7 | 13.7 | 33.1 | 23.0 | 7.0 | 5.1 | |
1.9 | 1.6 | 7.3 | 11.8 | 4.5 | 6.2 | 7.9 | 7.4 | 34.4 | 15.2 | 3.7 | 6.6 | |
1.1 | 1.2 | 15.3 | 39.2 | 4.3 | 7.3 | 8.6 | 7.2 | 45.8 | 28.8 | 7.7 | 7.3 | |
3.3 | 2.7 | 18.9 | 15.1 | 14.7 | 10.7 | 9.3 | 22.2 | 47.2 | 19.0 | 8.7 | 5.9 | |
3.0 | 3.1 | 27.2 | 26.5 | 16.4 | 13.4 | 10.3 | 19.4 | 32.8 | 28.3 | 17.2 | 9.4 | |
1.2 | 1.4 | 8.9 | 20.5 | 4.6 | 9.3 | 5.7 | 9.6 | 100 | 30.6 | 6.7 | 4.8 | |
IV | 1.9 | 1.7 | 23.9 | 43.3 | 7.2 | 8.7 | 12.6 | 10.1 | 51.8 | 35.1 | 7.5 | 11.4 |
2.9 | 3.7 | 42.7 | 33.8 | 33.3 | 22.5 | 14.7 | 47.9 | 100 | 22.1 | 13.7 | 11.5 | |
4.6 | 3.2 | 23.2 | 15.6 | 14.8 | 7.5 | 15.9 | 21.8 | 49.8 | 44.8 | 8.4 | 13.4 | |
3.3 | 2.9 | 22.6 | 18.0 | 18.5 | 13.5 | 9.8 | 38.7 | 100 | 17.5 | 6.7 | 6.9 | |
3.4 | 4.1 | 17.1 | 15.3 | 12.6 | 9.6 | 11.5 | 20.4 | 46.7 | 18.5 | 7.4 | 8.8 | |
2.9 | 3.0 | 11.7 | 7.5 | 9.0 | 4.5 | 11.2 | 17.5 | 63.4 | 26.3 | 2.2 | 7.2 | |
3.4 | 3.4 | 13.9 | 11.0 | 9.9 | 6.5 | 8.7 | 20.0 | 97.9 | 30.6 | 5.9 | 5.8 | |
1.9 | 2.6 | 12.7 | 17.5 | 5.8 | 5.0 | 9.4 | 8.5 | 56.2 | 56.3 | 6.6 | 9.1 | |
2.8 | 2.6 | 14.7 | 14.7 | 10.4 | 7.4 | 10.4 | 16.2 | 42.3 | 23.8 | 7.5 | 7.7 |
Note: Median Parameter estimates were used instead of averages, due to several voxels with failed fits, which yielded outliers towards high transfer rates (~100 10−2 min−1 was set as upper constraint for all parameters).