Feasibility of Population-Based Input Function for Kinetic Modeling of [11C]-DPA-713

Abstract

Introduction: Quantitative positron emission tomography (PET) studies of neurodegenerative diseases typically require the measurement of arterial input functions (AIF), an invasive and risky procedure. This study aims to assess the reproducibility of [ 11 C]DPA-713 PET kinetic analysis using population-based input function (PBIF). The final goal is to possibly eliminate the need for AIF. Materials and Methods: Eighteen subjects including six healthy controls (HC) and twelve Parkinson disease (PD) subjects from two [ 11 C]-DPA-713 PET studies were included. Each subject underwent 90 minutes of dynamic PET imaging. Five healthy subjects underwent a test-retest scan within the same day to assess the repeatability of the kinetic parameters. Kinetic modeling was carried out using the Logan total volume of distribution (VT) model. For each data set, kinetic analysis was performed using a patient specific AIF (PSAIF, ground-truth standard), and then repeated using the PBIF. PBIF was generated using the leave-one-out method for each subject from the remaining 17 subjects, and after normalizing the PSAIFs by 3 techniques: (a) Weight subject ×Dose Injected (b) Area Under AIF Curve (AUC), and (c) Weight subject ×AUC. The variability in the total distribution volume (V T ) measured with PSAIF, in the test/retest study were determined for selected brain regions (white matter, cerebellum, thalamus, caudate, putamen, pallidum, brainstem, hippocampus and amygdala) using the Bland-Altman analysis, and for each of the 3 normalization techniques. Similarly, for all subjects, the variabilities due to the use of PBIF were assessed. Results: Bland-Altman analysis showed systematic bias between test and retest studies, which was reduced after normalizing the V T estimate by the corresponding gray matter value. The corresponding mean bias and 95% limits of agreement (LOA) for the studied brain regions were 30% and 5%; ±70% and ±20% without and with gray matter normalization respectively. Comparing PBIF- and PSAIF-based VT estimate for all subjects and all brain regions, normalization by Weight subject ×AUC yielded the smallest 95% LOA among the three normalization techniques (±12%, ±13% and ±10% for Weight subject ×Dose Injected , AUC and Weight subject ×AUC respectively). Conclusions: The variability in VT is within that obtained for the test-retest studies. Therefore, VT assessed using PBIF-based kinetic modeling is clinically feasible, and can be an alternative to PSAIF.