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The 18 kDa translocator protein (TSPO) is the main molecular target to image neuroinflammation by positron emission tomography (PET). However, TSPO-PET quantification is complex and none of the kinetic modelling approaches has been validated using a voxel-by-voxel comparison of TSPO-PET data with the actual TSPO levels of expression. Here, we present a single case study of binary classification of in vivo PET data to evaluate the statistical performance of different TSPO-PET quantification methods. To that end, we induced a localized and adjustable increase of TSPO levels in a non-human primate brain through a viral-vector strategy. We then performed a voxel-wise comparison of the different TSPO-PET quantification approaches providing parametric [ 18 F]-DPA-714 PET images, with co-registered in vitro three-dimensional TSPO immunohistochemistry (3D-IHC) data. A data matrix was extracted from each brain hemisphere, containing the TSPO-IHC and TSPO-PET data for each voxel position. Each voxel was then classified as false or true, positive or negative after comparison of the TSPO-PET measure to the reference 3D-IHC method. Finally, receiver operating characteristic curves (ROC) were calculated for each TSPO-PET quantification method. Our results show that standard uptake value ratios using cerebellum as a reference region (SUV CBL ) has the most optimal ROC score amongst all non-invasive approaches.

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 [ 11 C]PBR28, [ 18 F]DPA714 and [ 11 C]-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 [ 11 C]-R-PK11195 data from six healthy subjects. Then, we compared the [ 11 C]-R-PK11195 vascular binding estimates with previously published values for [ 18 F]DPA714 and [ 11 C]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. [ 11 C]PBR28), hence poorly suited for the extraction of a reference region using supervised clustering.

There is a great need for a non-invasive methodology enabling the quantification of translocator protein overexpression in PET clinical imaging. [ 18 F]DPA-714 has emerged as a promising translocator protein radiotracer as it is fluorinated, highly specific and returned reliable quantification using arterial input function. Cerebellum gray matter was proposed as reference region for simplified quantification; however, this method cannot be used when inflammation involves cerebellum. Here we adapted and validated a supervised clustering (supervised clustering algorithm (SCA)) for [ 18 F]DPA-714 analysis. Fourteen healthy subjects genotyped for translocator protein underwent an [ 18 F]DPA-714 PET, including 10 with metabolite-corrected arterial input function and three for a test-retest assessment. Two-tissue compartmental modelling provided [Formula: see text] estimates that were compared to either [Formula: see text] or [Formula: see text] generated by Logan analysis (using supervised clustering algorithm extracted reference region or cerebellum gray matter). The supervised clustering algorithm successfully extracted a pseudo-reference region with similar reliability using classes that were defined using either all subjects, or separated into HAB and MAB subjects. [Formula: see text], [Formula: see text] and [Formula: see text] were highly correlated (ICC of 0.91 ± 0.05) but [Formula: see text] were ∼26% higher and less variable than [Formula: see text]. Reproducibility was good with 5% variability in the test-retest study. The clustering technique for [ 18 F]DPA-714 provides a simple, robust and reproducible technique that can be used for all neurological diseases.