Flortaucipir F 18 Quantitation Using Parametric Estimation of Reference Signal Intensity
Bibliographic record
Abstract
PET imaging of tau pathology in Alzheimer disease may benefit from the use of white matter reference regions. These regions have shown reduced variability compared with conventional cerebellar regions in amyloid imaging. However, they are susceptible to contamination from partial-volume blurring of tracer uptake in the cortex. We present a new technique, PERSI (Parametric Estimation of Reference Signal Intensity), for flortaucipir F 18 count normalization that leverages the advantages of white matter reference regions while mitigating potential partial-volume effects. Methods: Subjects with a clinical diagnosis of Alzheimer disease, mild cognitive impairment, or normal cognition underwent T1-weighted MRI and florbetapir imaging (to determine amyloid [Aβ] status) at screening and flortaucipir F 18 imaging at single or multiple time points. Flortaucipir F 18 images, acquired as 4 × 5 min frames 80 min after a 370-MBq injection, were motion-corrected, averaged, and transformed to Montreal Neurological Institute (MNI) space. The PERSI reference region was calculated for each scan by fitting a bimodal gaussian distribution to the voxel-intensity histogram within an atlas-based white matter region and using the center and width of the lower-intensity peak to identify the voxel intensities to be included. Four conventional reference regions were also evaluated: whole cerebellum, cerebellar gray matter, atlas-based white matter, and subject-specific white matter. SUVr (standardized uptake value ratio) was calculated for a statistically defined neocortical volume of interest. Performance was evaluated with respect to test–retest variability in a phase 2 study of 21 subjects (5–34 d between scans). Baseline variability in controls (SD of SUVr and ΔSUVr) and effect sizes for group differences (Cohen d; Aβ-positive impaired vs. Aβ-negative normal) were evaluated in another phase 2 study with cross-sectional data (n = 215) and longitudinal data (n = 142/215; 18 ± 2 mo between scans). Results: PERSI showed superior test–retest reproducibility (1.84%) and group separation ability (cross-sectional Cohen d = 9.45; longitudinal Cohen d = 2.34) compared with other reference regions. Baseline SUVr variability and ΔSUVr were minimal in Aβ control subjects with no specific flortaucipir F 18 uptake (SUVr, 1.0 ± 0.04; ΔSUVr, 0.0 ± 0.02). Conclusion: PERSI reduced variability while enhancing discrimination between diagnostic cohorts. Such improvements could lead to more accurate disease staging and robust measurements of changes in tau burden over time for the evaluation of putative therapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".