Population-based input function for TSPO quantification and kinetic modeling with [¹¹C]-DPA-713

Area of Science:

• Nuclear Medicine
• Neuroimaging
• Pharmacokinetics

Background:

• Quantitative positron emission tomography (PET) studies for neurodegenerative diseases necessitate arterial input functions (AIF), which are invasive and pose risks.
• This study investigates the use of population-based input function (PBIF) to assess the reproducibility of [11C]DPA-713 PET kinetic analysis.
• The ultimate aim is to determine if PBIF can replace the need for AIF in clinical practice.

Purpose of the Study:

• To evaluate the reproducibility of [11C]DPA-713 PET kinetic analysis using PBIF compared to patient-specific AIF (PSAIF).
• To assess the impact of different normalization techniques on PBIF accuracy.
• To determine the clinical feasibility of PBIF for Parkinson disease (PD) and healthy volunteer (HV) studies.

Main Methods:

• Eighteen subjects (6 HV, 12 PD) underwent dynamic [11C]DPA-713 PET imaging.
• Kinetic modeling utilized the Logan V_T model, comparing PSAIF (ground truth) with PBIF generated via leave-one-out method and three normalization techniques (Weight×Dose, AUC, Weight×AUC).
• Test-retest scans in 5 HVs assessed parameter repeatability; Bland-Altman analysis evaluated variability.

Main Results:

• Normalizing PBIF by Weight_subject×AUC yielded the lowest bias (±2%) and variability (±38%) compared to PSAIF.
• PBIF-based V_T estimates showed minimal and insignificant differences from PSAIF across all subjects and brain regions.
• PBIF accurately reflected disease group differences (PD vs. HV) in certain genotypes (MAB/HAB), though with some over/underestimation trends.

Conclusions:

• Quantitative [11C]DPA-713 PET kinetic analysis using PBIF is reproducible and comparable to PSAIF.
• The variability observed with PBIF falls within the range of test-retest repeatability.
• PBIF-based kinetic modeling is clinically feasible and presents a viable alternative to invasive PSAIF.