Simulation of Low-Dose Protocols for Myocardial Perfusion <sup>82</sup>Rb Imaging
Bibliographic record
Abstract
Quantification of myocardial perfusion and myocardial blood flow using 82Rb PET is increasingly used for assessment of coronary artery disease. Current guidelines suggest injections of 1,100–1,500 MBq for both stress and rest. Reducing the injected dose avoids PET system saturation in first-pass flow images and reduces radiation exposure, but the impact on myocardial perfusion quantification of static perfusion images is not fully understood. In this study, we aimed to evaluate the feasibility of performing myocardial perfusion scans using either a half-dose (HfD) or quarter-dose (QD) protocol using reconstructions from acquired full-dose (FD) scans. Methods: This study comprised 171 patients who underwent rest/stress 82Rb PET with a 3-dimensional 4-ring PET/CT scanner using a FD protocol and invasive coronary angiography within 6 mo of the PET emission scan. HfD and QD reconstructions were obtained using the prescribed percentage of events from the FD list-mode files. The total perfusion deficit was quantified for rest (rTPD), stress (sTPD), and ischemia (ITPD = sTPD − rTPD). Diagnostic accuracy for obstructive coronary artery disease, defined as at least 70% stenosis in any of the 3 major coronary arteries, was compared with area under the receiver-operating-characteristic curve (AUC). Results: Patients with a median body mass index of 28.0 (interquartile range, 23.9–31.7) were injected with doses of 1,165 ± 189 MBq of 82Rb. For sTPD, FD and HfD protocols had similar AUCs (FD, 0.807; HfD, 0.802; P = 0.108), whereas QD had a reduced AUC (0.786, P = 0.037). There was no difference in the AUC obtained for ITPD among the 3 protocols (FD, 0.831; HfD, 0.835; QD, 0.831; all P ≥ 0.805). Conclusion: HfD imaging does not affect the quantitative diagnostic accuracy of 82Rb PET on 3-dimensional PET/CT systems and could be used clinically.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".