Abstract
BACKGROUND: Previous studies found that the positron emission tomography (PET) radioligand [(18)F]LSN3316612 accurately quantified O-GlcNAcase in human brain using a two-tissue compartment model (2TCM). This study sought to assess kinetic model(s) as an alternative to 2TCM for quantifying [(18)F]LSN3316612 binding, particularly in order to generate good-quality parametric images. METHODS: The current study reanalyzed data from a previous study of 10 healthy volunteers who underwent both test and retest PET scans with [(18)F]LSN3316612. Kinetic analysis was performed at the region level with 2TCM using 120-min PET data and arterial input function, which was considered as the gold standard. Quantification was then obtained at both the region and voxel levels using Logan plot, Ichise's multilinear analysis-1 (MA1), standard spectral analysis (SA), and impulse response function at 120 min (IRF(120)). 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 (V(T)) values at both the region and voxel levels were strongly correlated with 2TCM-derived V(T) (r = 0.96-0.99) and showed little bias (up to - 8%). SA was more weakly correlated to 2TCM-derived V(T) (r = 0.93-0.98) and was more biased (~ 16%). IRF(120) showed a strong correlation with 2TCM-derived V(T) (r = 0.96) but generated noisier parametric images. All techniques were comparable to 2TCM in terms of test-retest variability and reliability except IRF(120), which gave significantly worse results. Noninvasive SUVR values were not correlated with 2TCM-derived V(T), and arteriovenous equilibrium was never reached. CONCLUSIONS: Compared to SA and IRF, Logan and MA1 are more suitable alternatives to 2TCM for quantifying [(18)F]LSN3316612 and generating good-quality parametric images.