TAME-Q: an open-source preprocessing pipeline for reproducible semi-quantification of florzolotau (18 F) PET

Abstract Background Florzolotau (18 F) PET can visualise tau aggregates in Alzheimer’s disease (AD) and non-AD tauopathies. A previously developed histogram-based grey-matter reference strategy enables semi-quantification without prespecifying a single anatomically spared reference region, but its reliance on in-house and proprietary tools has limited reproducibility. We developed TAME-Q, an automated, openly accessible pipeline implementing this strategy, and evaluated its technical performance and concordance with the previous workflow. Methods TAME-Q was applied to dynamic florzolotau PET and T1-weighted MRI from 37 participants on the AD continuum, 46 with progressive supranuclear palsy–Richardson syndrome (PSP-RS), and 50 healthy controls. The pipeline performs image alignment, tissue segmentation, Gaussian fitting of grey-matter intensity histograms, reference estimation, SUVR generation, FreeSurfer-based parcellation, extraction of 132 ROI-level SUVRs, and generation of quality-assurance outputs. Outputs were evaluated using quantitative screening and structured visual review. Disease-specific regional SUVRs and Elastic Net-derived AD-tau and PSP-tau scores were compared with a benchmark implementation. Held-out discrimination across ten 75%/25% shuffle-split iterations and post hoc reduced-feature and atlas-sensitivity analyses were also evaluated. Results TAME-Q completed automated preprocessing for all 133 participants, and all outputs were accepted after quality control. Bimodal and monomodal histogram fits were used in 121 and 12 participants, respectively. Age- and sex-adjusted SUVRs were higher in the inferior temporal gyrus in AD and in the globus pallidus in PSP-RS (Holm-adjusted p < 0.001). Histogram- and cerebellar-derived reference values showed broadly similar group-level scaling but differed in some individuals. TAME-Q-derived and benchmark tau scores were strongly correlated (AD-tau, r = 0.979; PSP-tau, r = 0.960). Full-cohort AUCs were 0.999 (95% CI: 0.998–1.000) for AD and 0.967 (95% CI: 0.941–0.992) for PSP; corresponding mean held-out AUCs were 0.996 ± 0.009 and 0.935 ± 0.010. Full-cohort AUCs were similar after coefficient truncation to the top 20 ROIs and after retraining with the unmerged FreeSurfer atlas. Conclusions TAME-Q provides an automated and inspectable implementation of histogram-based florzolotau PET semi-quantification and produced outputs concordant with the benchmark workflow in this single-center dataset. External multicenter validation is required to establish generalisability across acquisition settings and populations.

Authors

Institutions

Publication Details

Journal
BMC Medical Imaging
Published
2026-09-09
DOI
https://doi.org/10.1186/s12880-026-02775-5
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

TAME-Q: an open-source preprocessing pipeline for reproducible semi-quantification of florzolotau (18 F) PET

Kiyotaka Nemoto, Masashi Tamura, Kenjiro Nakayama, Miho Ota et al.
BMC Medical Imaging
Dementia and Cognitive Impairment Research
article

TAME-Q: an open-source preprocessing pipeline for reproducible semi-quantification of florzolotau (18 F) PET

Kiyotaka Nemoto, Masashi Tamura, Kenjiro Nakayama, Miho Ota, Asaka Oyama, Miyuki Nemoto, Makoto Higuchi, Takahiko Tokuda, Kenji Tagai, Hironobu Endo, Tetsuaki Arai
article en

Abstract

Abstract Background Florzolotau (18 F) PET can visualise tau aggregates in Alzheimer’s disease (AD) and non-AD tauopathies. A previously developed histogram-based grey-matter reference strategy enables semi-quantification without prespecifying a single anatomically spared reference region, but its reliance on in-house and proprietary tools has limited reproducibility. We developed TAME-Q, an automated, openly accessible pipeline implementing this strategy, and evaluated its technical performance and concordance with the previous workflow. Methods TAME-Q was applied to dynamic florzolotau PET and T1-weighted MRI from 37 participants on the AD continuum, 46 with progressive supranuclear palsy–Richardson syndrome (PSP-RS), and 50 healthy controls. The pipeline performs image alignment, tissue segmentation, Gaussian fitting of grey-matter intensity histograms, reference estimation, SUVR generation, FreeSurfer-based parcellation, extraction of 132 ROI-level SUVRs, and generation of quality-assurance outputs. Outputs were evaluated using quantitative screening and structured visual review. Disease-specific regional SUVRs and Elastic Net-derived AD-tau and PSP-tau scores were compared with a benchmark implementation. Held-out discrimination across ten 75%/25% shuffle-split iterations and post hoc reduced-feature and atlas-sensitivity analyses were also evaluated. Results TAME-Q completed automated preprocessing for all 133 participants, and all outputs were accepted after quality control. Bimodal and monomodal histogram fits were used in 121 and 12 participants, respectively. Age- and sex-adjusted SUVRs were higher in the inferior temporal gyrus in AD and in the globus pallidus in PSP-RS (Holm-adjusted p < 0.001). Histogram- and cerebellar-derived reference values showed broadly similar group-level scaling but differed in some individuals. TAME-Q-derived and benchmark tau scores were strongly correlated (AD-tau, r = 0.979; PSP-tau, r = 0.960). Full-cohort AUCs were 0.999 (95% CI: 0.998–1.000) for AD and 0.967 (95% CI: 0.941–0.992) for PSP; corresponding mean held-out AUCs were 0.996 ± 0.009 and 0.935 ± 0.010. Full-cohort AUCs were similar after coefficient truncation to the top 20 ROIs and after retraining with the unmerged FreeSurfer atlas. Conclusions TAME-Q provides an automated and inspectable implementation of histogram-based florzolotau PET semi-quantification and produced outputs concordant with the benchmark workflow in this single-center dataset. External multicenter validation is required to establish generalisability across acquisition settings and populations.

BMC Medical Imaging
University of Tsukuba (JP), National Institutes for Quantum Science and Technology (JP), Ibaraki Prefectural Central Hospital (JP), The University of Osaka (JP)
Gender equality
Openalex Percentile: Top 9%
Dementia and Cognitive Impairment Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.