Kinetic modeling of 18F-PI-2620 binding in the brain using an image-derived input function with total-body PET

• Published in: EJNMMI research Vol. 15; no. 1; pp. 62 - 13
• Main Authors: Bhattarai, Anjan, Holy, Emily Nicole, Wang, Yiran, Spencer, Benjamin A., Wang, Guobao, DeCarli, Charles, Fan, Audrey P.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 30.05.2025; Springer Nature B.V; SpringerOpen
• Subjects: 18F-PI-2620; Alzheimer's disease; Anniversaries; Aorta; Binding; Blood; Brain; Brain research; Cardiac Imaging; Clinical trials; Computed tomography; Coronary vessels; Fluorine isotopes; Grey matter; Image-derived input function; Imaging; Kinetic modeling; Kinetics; Medical imaging; Medicine; Medicine & Public Health; Metabolites; Modelling; Nuclear Medicine; Oncology; Original Research; Orthopedics; Parameter estimation; Pharmacokinetics; Positron emission; Radiology; Rate constants; Scanners; Tau; Total-body PET; Tau; Kinetic modeling; Image-derived input function; Grey matter; F-PI-2620; Total-body PET
• ISSN: 2191-219X; 2191-219X
• DOI: 10.1186/s13550-025-01260-4

Background Accurate quantification of tau binding from 18 F-PI-2620 PET requires kinetic modeling and an input function. We aimed to implement a non-invasive Image-derived input function (IDIF) using the state-of-the-art total-body uEXPLORER PET/CT scanner to quantify tau binding and tracer delivery rate from 18 F-PI-2620 in the brain. Additionally, we investigated the impact of scan duration on the quantification of kinetic parameters. Results 18 F-PI-2620 total-body PET dynamic (90 min) data from 15 elderly (66–92 years) participants were acquired. Time-activity curves were obtained from grey matter regions of interest (ROIs) known to be affected in Alzheimer’s disease, including the medial temporal lobe, posterior cingulate, and lateral parietal cortex. These curves were fitted to the two-tissue compartmental model (2TCM) using a subject-specific IDIF (plasma and metabolite corrected) derived from the descending aorta. ROI-specific kinetic parameters were estimated for different scan durations ranging from 10 to 90 min. The parameters included blood fraction volume (v b ), rate constants (K 1 , k 2 , k 3 , k 4 ), total distribution volume (V T ), distribution volume ratio (DVR), and tracer arrival delay. Logan graphical analysis was also used to estimate V T and compared with 2TCM. Differences in kinetic parameters were observed between ROIs, including significant reduction in tracer delivery rate (K 1 ) in the medial temporal lobe (q < 0.001). All kinetic parameters remained relatively stable (compared to parameters quantified with full 90-minute data) after the 60-minute scan window across all ROIs ( r  ≥ 0.89; p  < 0.001), with K 1 showing high stability after 30 min of scan duration ( r  ≥ 0.92; p  < 0.001). Excellent correlation was observed between V T estimated using 2TCM and Logan plot analysis ( r  ≥ 0.96; p  < 0.001). Conclusions This study demonstrated the utility of IDIF from a lager blood pool, derived using the total-body PET in quantifying 18 F-PI-2620 kinetics in the brain. Our findings suggest that a 60-minute scan window may be required for the reliable quantification of kinetic parameters using IDIF, whereas a 30-minute scan time may be sufficient for the quantification of K 1 .