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Validation of an automatic reference region extraction for the quantification of [ 18 F]DPA-714 in dynamic brain PET studies

Abstract : There is a great need for a non-invasive methodology enabling the quantification of translocator protein overexpression in PET clinical imaging. [18F]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 [18F]DPA-714 analysis. Fourteen healthy subjects genotyped for translocator protein underwent an [18F]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 [18F]DPA-714 provides a simple, robust and reproducible technique that can be used for all neurological diseases.
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Daniel Garcia-Lorenzo, Sonia Lavisse, Claire Leroy, Catriona Wimberley, Benedetta Bodini, et al.. Validation of an automatic reference region extraction for the quantification of [ 18 F]DPA-714 in dynamic brain PET studies. Journal of Cerebral Blood Flow and Metabolism, Nature Publishing Group, 2017, 38 (2), pp.333-346. ⟨10.1177/0271678X17692599⟩. ⟨hal-02340034⟩

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