Optimization of time frame binning for FDOPA uptake quantification in glioma

Abstract

Introduction 3,4-dihydroxy-6-[18F]fluoro-L-phenylalanine (FDOPA) uptake quantification in glioma assessment can be distorted using a non-optimal time frame binning of time-activity curves (TAC). Under-sampling or over-sampling dynamic PET images induces significant variations on kinetic parameters quantification. We aimed to optimize temporal time frame binning for dynamic FDOPA PET imaging. Methods Fourteen patients with 33 tumoral TAC with biopsy-proven gliomas were analysed. The mean SUVmax tumor-to-brain ratio (TBRmax) were compared at 20 min and 35 min postinjection (p.i). Five different time frame samplings within 20 min were compared: 11x10sec-6x15sec-5x20sec-3x300sec; 8x15sec-2x30sec-2x60sec-3x300sec; 6x20sec-8x60sec-2x300sec; 10x30sec-3x300sec and 4x45sec-3x90sec-5x150sec. The reversible singletissue compartment model with blood volume parameter (VB) was selected using the Akaike information criterion. K1 values extracted from 1024 noisy simulated TAC using Monte Carlo method from the 5 different time samplings were compared to a target K1 value as the objective, which is the average of the K1 values extracted from the 33 lesions using an imaging-derived input function for each patient. Results The mean TBRmax was significantly higher at 20 min p.i. than at 35 min p.i (respectively 1.4 +/-0.8 and 1.2 +/-0.6; p <0.001). The target K1 value was 0.161 mL/ccm/min. The 8x15sec-2x30sec-2x60sec-3x300sec time sampling was the optimal time frame binning. K1 values extracted using this optimal time frame binning were significantly different with K1 values extracted from the other time frame samplings, except with K1 values obtained using the 11x10sec-6x15sec-5x20sec-3x300sec time frame binning.

Usefulness of 18F-FDOPA PET for the management of primary brain tumors: a systematic review of the literature

“…Still, three main applicability concerns remain due to the use of carbidopa for some patients, the combination with MRI in cases of no radiotracer uptake (17% in this study) and a selection bias (patients were included only if both molecular data were known and a dynamic PET acquisition had been made). Moreover, it is worth noting that a recent study reported by Girard et al sought to improve the time frame binning for dynamic 18 F-FDOPA PET imaging [ 33 ]. The authors used a three-compartment model, which is the most commonly used for full kinetic analysis of PET, and compared five different time samplings in 14 patients.…”

Impact of point-spread function reconstruction on dynamic and static 18F-DOPA PET/CT quantitative parameters in glioma

“…Voxel size (XYZ) was 1×1×2 mm 3 . From the bolus arrival time, a 0-20-min static and dynamic (8×15 sec, 2×30 sec, 2×60 sec, 3×300 sec) series were reconstructed (18).…”

“…SUVmax and SUVmean were respectively the maximum and the mean of the SUVs of the VOI. The reversible single-tissue compartment model with blood volume parameter (with K1 = rate constant from blood to tissue, k2 = rate constant from the tissue compartment to the arterial blood) was used to extract kinetic parameters (PMOD software version 3.8; PMOD Technologies; Zürich, Switzerland) (18).…”

Additive Value of Dynamic FDOPA PET/CT for Glioma Grading

“…Voxel size (XYZ) was 1 × 1 × 2 mm 3 . A 20-min static image 10 min after injection in accordance with current recommendations (23) and an optimal dynamic time sampling of 8 × 15 s-−2 × 30 s-−2 × 60 s-−3 × 300 s from the bolus arrival time (24) were reconstructed from a 40-min list-mode acquisition immediately after FDOPA injection.…”