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

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 <named-content>BPND AIF</named-content> estimates that were compared to either <named-content>BPND LoganSCA</named-content> or <named-content>BPND LoganCRB</named-content> 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. <named-content>BPND AIF</named-content>, <named-content>BPND LoganSCA</named-content> and <named-content>BPND LoganCRB</named-content> were highly correlated (ICC of 0.91 ± 0.05) but <named-content>BPND LoganSCA</named-content> were ∼26% higher and less variable than <named-content>BPND LoganCRB</named-content>. 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. [ABSTRACT FROM AUTHOR]