HAI Book 2025 - Flipbook - Page 282
Povala, Guilherme
The universal tau PET scale (Uni�㔏) for Flortaucipir, MK6240, PI2620 and
RO948 harmonization
Guilherme Povala1, Guilherme Bauer-Negrini1, Bruna Bellaver1, Firoza Z. Lussier1, Livia
Amaral1, Pamela Lukasewicz Ferreira1, Dana L. Tudorascu1, Quentin Finn2, Joseph Masdeu2,
David Soleimani-Meigooni3, Juan Fortea4, Val Lowe5, Hwamee Oh6, Belen Pascual2, Brian A.
Gordon7, Pedro Rosa-Neto8, Suzanne Baker9, Tharick A. Pascoal1
1
University of Pittsburgh, Department of Psychiatry, Pittsburgh, PA, US
Houston Methodist Research Institute, Department of Neurology, Houston, TX, US
3
University of California San Francisco, Memory and Aging Center, San Francisco, CA, US
4
Hospital de la Santa Creu i Sant Pau, Sant Pau Memory Unit, Department of Neurology, Barcelona, ES
5
Mayo Clinic, Department of Radiology, Rochester, MN, US
6
Brown University, Department of Psychiatry and Human Behavior, Providence, RI, US
7
Washington University in St. Louis, Department of Radiology, Saint Louis, MO, US
8
Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Research
Institute, Montréal, QC, CA
9
Lawrence Berkeley National Laboratory, Berkeley, CA, US
2
Objective: HEAD is a longitudinal, multi-site, observational, non-randomized head-to-head study of tau-PET
tracers. Here, we tested the hypothesis that the Uni�㔏 scale can accurately harmonize the tau-PET tracers
Flortaucipir, MK6240, PI2620, and RO948 onto a universal tau-PET measurement.
Methods: We assessed 402 individuals across aging and AD spectrum scanned head-to-head with Flortaucipir
and MK6240, and a subset of 67 also with PI2620 and RO948. Tau-PET SUVRs were processed to a common 8mm
FWHM using inferior cerebellar gray matter as reference. We previously estimated Uni�㔏 parameters on a training
set (n=200) fitting a single-step smoothed hyperbolic tangent mathematical model to the CenTauR MetaTemporal ROI SUVRs anchored in the mean SUVR of young participants and the 90th percentile from cognitively
impaired individuals. We compared tau positivity classifications of the tracers on Uni�㔏 with the classification
derived from the original SUVRs (mean + 3 SD from young participants).
Results: Uni�㔏 was able to successfully harmonize Flortaucipir and MK6240 by aligning the values of both tracers
(Fig.1). For Flortaucipir, the tau positivity classifications of the Uni�㔏 scale matched those from the original SUVRs
(ground truth). For MK6240, there was one mismatched case (Fig.1). Additionally, by utilizing tracer-specific
parameters, Uni�㔏 successfully harmonized PI2620 and RO948 with both Flortaucipir and MK6240, achieving
regression lines close to unity, confirming its applicability across multiple tau-PET tracers (Fig.2). Finally, upon
applying the Uni�㔏 transformation to all brain voxels, Uni�㔏 reasonably harmonized 3D images to the Uni�㔏 scale,
substantially reducing visual variabilities (Fig.3).
Conclusion: Our results indicate that tau PET tracers can be harmonized to a common scale using anchor points
from a large head-to-head dataset. It remains to be seen how Uni�㔏 will perform in the ongoing 18-month follow-up
of the HEAD study. Uni�㔏 is freely available on multiple platforms (www.unitau.app) for ROI or voxel-wise tau PET
harmonization.
HAI2025 - 282
