It appears you don't have support to open PDFs in this web browser. To view this file, Open with your PDF reader
Abstract
Background
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 assess kinetic model(s) as an alternative to 2TCM for quantifying [18F]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 [18F]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 (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
Compared to SA and IRF, Logan and MA1 are more suitable alternatives to 2TCM for quantifying [18F]LSN3316612 and generating good-quality parametric images.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details

1 National Institutes of Health, Molecular Imaging Branch, National Institute of Mental Health, Bethesda, USA (GRID:grid.94365.3d) (ISNI:0000 0001 2297 5165); Yonsei University College of Medicine, Department of Nuclear Medicine, Gangnam Severance Hospital, Seoul, Republic of Korea (GRID:grid.15444.30) (ISNI:0000 0004 0470 5454)
2 King’s College London, Department of Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, London, UK (GRID:grid.13097.3c) (ISNI:0000 0001 2322 6764)
3 National Institutes of Health, Molecular Imaging Branch, National Institute of Mental Health, Bethesda, USA (GRID:grid.94365.3d) (ISNI:0000 0001 2297 5165)