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Generalization of endothelial modelling of TSPO PET imaging: Considerations on tracer affinities
The 18 kDa translocator protein (TSPO) is a marker of microglia activation and the main target of positron emission tomography (PET) ligands for neuroinflammation. Previous works showed that accounting for TSPO endothelial binding improves PET quantification for [11C]PBR28, [18F]DPA714 and [11C]-R-P...
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Published in: | Journal of cerebral blood flow and metabolism 2019-05, Vol.39 (5), p.874-885 |
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Main Authors: | , , , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | The 18 kDa translocator protein (TSPO) is a marker of microglia activation and the main target of positron emission tomography (PET) ligands for neuroinflammation. Previous works showed that accounting for TSPO endothelial binding improves PET quantification for [11C]PBR28, [18F]DPA714 and [11C]-R-PK11195. It is still unclear, however, whether the vascular signal is tracer-dependent. This work aims to explore the relationship between the TSPO vascular and tissue components for PET tracers with varying affinity, also assessing the impact of affinity towards the differentiability amongst kinetics and the ensuing ligand amenability to cluster analysis for the extraction of a reference region. First, we applied the compartmental model accounting for vascular binding to [11C]-R-PK11195 data from six healthy subjects. Then, we compared the [11C]-R-PK11195 vascular binding estimates with previously published values for [18F]DPA714 and [11C]PBR28. Finally, we determined the suitability for reference region extraction by calculating the angle between grey and white matter kinetics. Our results showed that endothelial binding is common to all TSPO tracers and proportional to their affinity. By consequence, grey and white matter kinetics were most similar for the radioligand with the highest affinity (i.e. [11C]PBR28), hence poorly suited for the extraction of a reference region using supervised clustering. |
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ISSN: | 0271-678X 1559-7016 |
DOI: | 10.1177/0271678X17742004 |