UNLABELLED: (18)F-florbetaben is a novel (18)F-labeled tracer for PET imaging of β-amyloid deposits in the human brain. We evaluated the kinetic model-based approaches to the quantification of β-amyloid binding in the brain from dynamic PET data. The validity of the practically useful tissue ratio was also evaluated against the model-based parameters. METHODS: (18)F-florbetaben PET imaging was performed with concurrent multiple arterial sampling after tracer injection (300 MBq) in 10 Alzheimer disease (AD) patients and 10 age-matched healthy controls. Regional brain-tissue time-activity curves for 90 min were analyzed by a 1-tissue-compartment model and a 2-tissue-compartment model (2TCM) with metabolite-corrected plasma data estimating the specific distribution volume (VS) and distribution volume ratio (DVR [2TCM]) and a multilinear reference tissue model estimating DVR (DVR [MRTM]) using the cerebellar cortex as the reference tissue. Target-to-reference tissue standardized uptake value ratios (SUVRs) at 70-90 min were also calculated. RESULTS: All brain regions required 2TCM to describe the time-activity curves. All β-amyloid binding parameters in the cerebral cortex (VS, DVR [2TCM], DVR [MRTM], and SUVR) were significantly increased in AD patients (P < 0.05), and there were significant linear correlations among these parameters (r(2) > 0.83). Effect sizes in group discrimination between 8 β-amyloid-positive AD scans and 9 β-amyloid-negative healthy control scans for all binding parameters were excellent, being largest for DVR (2TCM) (4.22) and smallest for VS (3.25) and intermediate and the same for DVR (MRTM) and SUVR (4.03). CONCLUSION: These results suggest that compartment kinetic model-based quantification of β-amyloid binding from (18)F-florbetaben PET data is feasible and that all β-amyloid binding parameters including SUVR are excellent in discriminating between β-amyloid-positive and -negative scans.
UNLABELLED: (18)F-florbetaben is a novel (18)F-labeled tracer for PET imaging of β-amyloid deposits in the human brain. We evaluated the kinetic model-based approaches to the quantification of β-amyloid binding in the brain from dynamic PET data. The validity of the practically useful tissue ratio was also evaluated against the model-based parameters. METHODS: (18)F-florbetaben PET imaging was performed with concurrent multiple arterial sampling after tracer injection (300 MBq) in 10 Alzheimer disease (AD) patients and 10 age-matched healthy controls. Regional brain-tissue time-activity curves for 90 min were analyzed by a 1-tissue-compartment model and a 2-tissue-compartment model (2TCM) with metabolite-corrected plasma data estimating the specific distribution volume (VS) and distribution volume ratio (DVR [2TCM]) and a multilinear reference tissue model estimating DVR (DVR [MRTM]) using the cerebellar cortex as the reference tissue. Target-to-reference tissue standardized uptake value ratios (SUVRs) at 70-90 min were also calculated. RESULTS: All brain regions required 2TCM to describe the time-activity curves. All β-amyloid binding parameters in the cerebral cortex (VS, DVR [2TCM], DVR [MRTM], and SUVR) were significantly increased in ADpatients (P < 0.05), and there were significant linear correlations among these parameters (r(2) > 0.83). Effect sizes in group discrimination between 8 β-amyloid-positive AD scans and 9 β-amyloid-negative healthy control scans for all binding parameters were excellent, being largest for DVR (2TCM) (4.22) and smallest for VS (3.25) and intermediate and the same for DVR (MRTM) and SUVR (4.03). CONCLUSION: These results suggest that compartment kinetic model-based quantification of β-amyloid binding from (18)F-florbetaben PET data is feasible and that all β-amyloid binding parameters including SUVR are excellent in discriminating between β-amyloid-positive and -negative scans.
Authors: Wenhua Chu; Dong Zhou; Vrinda Gaba; Jialu Liu; Shihong Li; Xin Peng; Jinbin Xu; Dhruva Dhavale; Devika P Bagchi; André d'Avignon; Naomi B Shakerdge; Brian J Bacskai; Zhude Tu; Paul T Kotzbauer; Robert H Mach Journal: J Med Chem Date: 2015-07-31 Impact factor: 7.446
Authors: Swen Hesse; Georg-Alexander Becker; Michael Rullmann; Anke Bresch; Julia Luthardt; Mohammed K Hankir; Franziska Zientek; Georg Reißig; Marianne Patt; Katrin Arelin; Donald Lobsien; Ulrich Müller; S Baldofski; Philipp M Meyer; Matthias Blüher; Mathias Fasshauer; Wiebke K Fenske; Michael Stumvoll; Anja Hilbert; Yu-Shin Ding; Osama Sabri Journal: Eur J Nucl Med Mol Imaging Date: 2017-01-09 Impact factor: 9.236
Authors: Damien Cressier; Martine Dhilly; Thang T Cao Pham; Fabien Fillesoye; Fabienne Gourand; Auriane Maïza; André F Martins; Jean-François Morfin; Carlos F G C Geraldes; Éva Tóth; Louisa Barré Journal: Mol Imaging Biol Date: 2016-06 Impact factor: 3.488