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

• Published in: Journal of cerebral blood flow and metabolism 2018-02, Vol.38 (2), p.333-346
• Main Authors: García-Lorenzo, Daniel, Lavisse, Sonia, Leroy, Claire, Wimberley, Catriona, Bodini, Benedetta, Remy, Philippe, Veronese, Mattia, Turkheimer, Federico, Stankoff, Bruno, Bottlaender, Michel
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Automation; Bioengineering; Brain - diagnostic imaging; Cerebellum - diagnostic imaging; Cluster Analysis; Female; Fluorine Radioisotopes; Gray Matter - diagnostic imaging; Healthy Volunteers; Humans; Image Processing, Computer-Assisted; Imaging; Life Sciences; Male; Middle Aged; Neurons and Cognition; Nuclear medicine; Original; Positron-Emission Tomography - methods; Positron-Emission Tomography - standards; Pyrazoles; Pyrimidines; Radiopharmaceuticals; Receptors, GABA - genetics; Reproducibility of Results
• ISSN: 0271-678X; 1559-7016
• DOI: 10.1177/0271678X17692599

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 BP ND AIF estimates that were compared to either BP ND LoganSCA or BP ND LoganCRB 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. BP ND AIF , BP ND LoganSCA and BP ND LoganCRB were highly correlated (ICC of 0.91 ± 0.05) but BP ND LoganSCA were ∼26% higher and less variable than BP ND LoganCRB . 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.