Region- and voxel-based quantification in human brain of18F-LSN3316612, a radioligand for O-GlcNAcase

1562 Objectives: Previous studies found that the positron emission tomography (PET) radioligand 18F-LSN3316612 accurately quantified O-GlcNAcase in human brain using a two-tissue compartment model (2TCM). This study sought to identify optimal kinetic model(s) as an alternative to 2TCM for quantifying 18F-LSN3316612 binding, particularly in order to generate good-quality parametric images. Methods: Ten healthy human volunteers underwent both test and retest PET scans using 18F-LSN3316612. Kinetic analysis was performed at the region level with 2TCM using 120-minute PET data, and arterial input function was used as the gold standard. Quantification was then obtained at both the region and voxel levels using Logan plot, Ichise9s multilinear analysis-1 (MA1), standard spectral analysis (SA), and impulse response function at 120 minutes (IRF120). To avoid arterial sampling, a noninvasive relative quantification (standardized uptake value ratio (SUVR)) was also tested using the corpus callosum as a pseudo-reference region. Venous samples were also assessed to see whether they could substitute for arterial ones. Results: Logan and MA1 generated parametric images of good visual quality and their total distribution volume (VT) values at both the region and voxel levels were strongly correlated with 2TCM-derived VT (r=0.96-0.99) and showed little bias (up to -8%). SA was more weakly correlated to 2TCM-derived VT (r=0.93-0.98) and was more biased (~16%). IRF120 showed a strong correlation with 2TCM-derived VT (r=0.96) but generated noisier parametric images. All techniques were comparable to 2TCM in terms of test-retest variability and reliability except IRF120, which gave significantly worse Results: Noninvasive SUVR values were not correlated with 2TCM-derived VT, and arteriovenous equilibrium was never reached. Conclusions: Logan and MA1 are optimal kinetic models and alternatives to 2TCM for quantifying 18F-LSN3316612 and generating good-quality parametric images.