Background: Population-based input function (PBIF) may be a valid alternative to full blood sampling for quantitative PET imaging. PBIF is typically validated by comparing its quantification results with those obtained via arterial sampling. However, for PBIF to be employed in actual clinical research studies, its ability to faithfully capture the whole spectrum of results must be assessed. The present study validated a PBIF for [18F]FMPEP-d2, a cannabinoid CB1 receptor radioligand, in healthy volunteers, and also attempted to utilize PBIF to replicate three previously published clinical studies in which the input function was acquired with arterial sampling. Methods: The PBIF was first created and validated with data from 42 healthy volunteers. This PBIF was used to assess the retest variability of [18F]FMPEP-d2, and then to quantify CB1 receptors in alcoholic patients (n = 18) and chronic daily cannabis smokers (n = 29). Both groups were scanned at baseline and after 2-4 weeks of monitored drug abstinence. Results: PBIF yielded accurate results in the 42 healthy subjects (average Logan-distribution volume (VT) was 13.3±3.8 mL/cm3 for full sampling and 13.2±3.8 mL/cm3 for PBIF; R2 = 0.8765, p<0.0001) and test-retest results were comparable to those obtained with full sampling (variability: 16%; intraclass correlation coefficient: 0.89). PBIF accurately replicated the alcoholism study, showing a widespread ~20% reduction of CB1 receptors in alcoholic subjects, without significant change after abstinence. However, a small PBIF-VT bias of -9% was unexpectedly observed in cannabis smokers. This bias led to substantial errors, including a VT decrease in regions that had shown no downregulation in the full input function. Simulated data showed that the original findings could only have been replicated with a PBIF bias between -6% and +4%. Conclusions: Despite being initially well validated in healthy subjects, PBIF may misrepresent clinical protocol results and be a source of variability between different studies and institutions.
All Science Journal Classification (ASJC) codes
- Biochemistry, Genetics and Molecular Biology(all)
- Agricultural and Biological Sciences(all)