Validation of an automatic reference region extraction for the quantification of [18F]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.

Trial registration: ClinicalTrials.gov NCT02305264.

Keywords: Inflammation; brain imaging and clinical trials; microglia; positron emission tomography.

Figures

Figure 1.

(a) Description of the SuperDPA method and (b) evaluation of this method through affinity, validation and test–retest studies.

Figure 2.

(a) Weighting maps of low/non-specific class (used for reference region selection) for one representative HAB subject using SuperDPAHAB (left) and SuperDPAALL (right). (b) BPNDLOGAN parametric map (axial and sagittal views) from one HAB subject using SuperDPAALL. (c) Averaged TACs of thalamus (green line with solid circle), cerebellar gray matter (solid black line) and SCA-based-reference-regions obtained with SuperDPAALL (dashed blue line) and SuperDPAHAB/MAB (dotted red line) methods. TACs are average SUV from HABs (left, n = 7) and MABs (right, n = 7).

Figure 3.

BPNDLOGAN estimates of each ROI using SuperDPAALL (top), SuperDPAHAB/MAB (middle) and the CRBGM (bottom) methods in HAB (n = 7) and MAB (n = 7) subjects; BPNDAIF estimates of each ROI using the same methods. Error bars indicate the (SD). *Significant difference, p < 0.05.

Figure 4.

Relationship between BPND estimates with the arterial input function analysis (BPNDAIF) and with reference input Logan graphical analysis (BPNDLOGAN) using the SuperDPAALL (left), SuperDPAHAB/MAB (middle) extracted reference region and the CRBGM (right). Regression lines in black lines.

Figure 5.

Evaluation of intrasubject-variability (test-retest) of the [18F]DPA-714 scan measures of each ROI using CRBGM , SuperDPAALL and SuperDPAHAB/MAB methods. Test–retest variability is calculated as the absolute value of the difference as follows Variability(%)=100×|(DVRTest-DVRReTest)|/Mean(DVRTest,DVRReTest)