Comparing quantitative measures of global and regional tau binding for 18F-AV-1451 PET scans (2019)

1628 Objectives: Quantitative measurement of tau binding from PET tracers (such as AV-1451) is crucial in the PET-based study of tauopathies (such as Alzheimer’s). The traditional standardized uptake value ratio (SUVR) method has been challenged by the off-target binding in reference region (Figure 1). Recently, several alternative methods have been proposed for unbiased tau binding quantification, including the Parametric Estimate of Reference Signal Intensity (PERSI) method and standardized uptake value peak-alignment (SUVP) method. In this study, we compared the normal/disease group difference and classification accuracy of SUVR, PERSI and SUVP measures, for both global and regional tau binding, on AV-1451 PET scans.
Methods: PET and MRI images of 44 subjects were retrieved from the Alzheimer9s Disease Neuroimaging Initiative database (ADNI), including 22 normal control (NL) and 22 Alzheimer’s disease (AD), matched with age and gender. The MRI images were acquired with standard T1 MPRAGE sequence (240x256x176 matrix; 1.0x1.0 mm pixel; 1.2mm slice thickness). Using Freesurfer (v6.0), regions of interest (ROIs) were defined on MRI images for global and regional binding: cerebral cortex for global binding; and regions affected in Braak Stages (BS) for regional binding (Supplementary Data). The PET acquisition involves an intravenous injection of 370MBq AV-1451 followed by a saline flush. A continuous 30 min brain scan (6 frames, 5 min each) was performed ~75 min after the injection, followed by a CT or transmission scan for attenuation correction. PET scans were reconstructed using a 3-D OSEM algorithm (four iterations, 12 subsets, 5 mm FWHM, 128×128 matrix, 3mm slice thickness). The 80-100 min summed images of PET scan were used to generate the standardized uptake value (SUV) maps, and then co-registered to the MRI images. The SUVR measure was computed by dividing the average SUV of ROI by the average SUV of reference region (cerebellar cortex). The PERSI measure replaced the reference region in SUVR measure by fitting a bimodal gaussian distributionto to the white matter SUV histogram, and using the center and width of the lower-intensity peak to identify the voxels to be included. The SUVP measure involves a transformation on the raw SUV maps: SUVP=(SUV-M)/S, where M and S are the mode and standard deviation of the whole brain SUV distribution. The average positive SUVP (SUVP-AVG) can then be computed by averaging all positive (i.e. >0) SUVP values of an ROI; and positive binding percentage for SUVP (SUVP-PBP) were computed by the ratio of voxel number with SUVP>1.5 over the total voxel number of the ROI. All binding measures were log-transformed, and tested for group differences with two-sample T-tests. The disease classification accuracy was estimated with the area under curve (AUC) of the Receiver Operating Characteristic (ROC) curve.
 Results: As expected, AD subjects showed higher tau binding than NL, both globally and regionally, under all measures (Figure 2). For regional bidding, BS 3-4 regions (including temporal lobe) showed most prominent group difference and highest classification accuracy (AUC=0.85 for SUVP-AVG, SUVP-PBP and PERSI, AUC=0.79 for SUVR). This is consistent with previous reports for AV-1451 scans. For global binding and binding of BS 5-6 regions, SUVP achieved highest classification accuracy (AUC>0.88 for global, AUC>0.81 for BS 5-6 regions), seconded by PERSI (AUC=0.81 for global, AUC=0.76 for BS 5-6 regions), and then SUVR (AUC=0.76 for global, AUC=0.72 for BS 5-6 regions).
Conclusions: All three quantification methods detected the group difference of tau binding between AD and NL. Both PERSI and SUVP showed classification improvement over SUVR. SUVP achieved equal or higher classification accuracy than PERSI in global, BS 3-4, and BS 5-6 regional binding. These results have potential value for the quantitative measures of other tau PET scans, as well as Amyloid PET scans.