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Updated: Nov 7, 2025

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

Mercy I Akerele1, Sara A Zein2, Sneha Pandya2

  • 1Department of Radiology, Weill Cornell Medical College, New York, NY, 10021, USA. mia4006@med.cornell.edu.

EJNMMI Physics
|April 29, 2021
PubMed
Summary

Population-based input function (PBIF) provides reproducible quantitative [11C]DPA-713 PET kinetic analysis for neurodegenerative diseases, offering a viable alternative to invasive arterial input functions (AIF). This method is clinically feasible for Parkinson disease (PD) studies.

Keywords:
Kinetic modelingNormalizationPopulation-based input function[11C]DPA-713

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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 VT 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 Weightsubject×AUC yielded the lowest bias (±2%) and variability (±38%) compared to PSAIF.
  • PBIF-based VT 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.