Perception-Enhanced Generative Adversarial Network for Synthesizing Tau Positron Emission Tomography images from Structural Magnetic Resonance Images: a cross-center and cross-tracer study
Bibliographic record
Abstract
Abstract Different tau positron emission tomography (PET) radiotracer holds promises for monitoring the accumulation of tau pathology in vivo. However, the low availability relative to the massive demand for tau-PET has ultimately hinders the potential benefits for the majority of patients. Here, we developed a unified deep learning model for synthesizing tau positron emission tomography (PET) images from the more available structural magnetic resonance imaging (sMRI). A total of 1387 subjects from two different cohorts were included in this study, involving tau-PET with 18F-flortaucipir and 18F-florzolotau. The tracer-specific models were trained independently and cross-validated internally and externally. The overall evaluations and regional-specific quantitative evaluations of the synthetic tau-PET have verified that the synthetic tau-PET followed a uniform distribution of reality and could accurately quantifying regional tau deposition, and the proposed method achieved the state-of-the-art performances on commonly used metrics and satisfies the reconstruction needs for clinical standards.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".