Positron Emission Tomography studies with [(11)C]PBR28 in the Healthy Rodent Brain: Validating SUV as an Outcome Measure of Neuroinflammation

Molecular imaging of the 18 kD Translocator protein (TSPO) with positron emission tomography (PET) is of great value for studying neuroinflammation in rodents longitudinally. Quantification of the TSPO in rodents is, however, quite challenging. There is no suitable reference region and the use of plasma-derived input is not an option for longitudinal studies. The aim of this study was therefore to evaluate the use of the standardized uptake value (SUV) as an outcome measure for TSPO imaging in rodent brain PET studies, using [(11)C]PBR28. In the first part of the study, healthy male Wistar rats (n = 4) were used to determine the correlation between the distribution volume (V(T), calculated with Logan graphical analysis) and the SUV. In the second part, healthy male Wistar rats (n = 4) and healthy male C57BL/6J mice (n = 4), were used to determine the test-retest variability of the SUV, with a 7-day interval between measurements. Dynamic PET scans of 63 minutes were acquired with a nanoScan PET/MRI and nanoScan PET/CT. An MRI scan was made for anatomical reference with each measurement. The whole brain V(T) of [(11)C]PBR28 in rats was 42.9 ± 1.7. A statistically significant correlation (r(2) = 0.96; p < 0.01) was found between the V(T) and the SUV. The test-retest variability in 8 brain region ranged from 8 to 20% in rats and from 7 to 23% in mice. The interclass correlation coefficient (ICC) was acceptable to excellent for rats, but poor to acceptable for mice. In conclusion: The SUV of [(11)C]PBR28 showed a high correlation with V(T) as well as good test-retest variability. For future longitudinal small animal PET studies the SUV can thus be used to describe [(11)C]PBR28 uptake in healthy brain tissue. Based on the present observations, further studies are needed to explore the applicability of this approach in small animal disease models, with special regard to neuroinflammatory models.