Consistent tracer administration profile improves test–retest repeatability of myocardial blood flow quantification with 82Rb dynamic PET imaging
Bibliographic record
Abstract
Quantification of myocardial blood flow (MBF) and stress/rest flow reserve is used increasingly to diagnose multi-vessel coronary artery disease and micro-vascular disease with PET imaging. However, variability in the measurements may limit physician confidence to direct revascularization therapies based on specific threshold values. This study evaluated the effects of rubidium-82 (82Rb) tracer injection profile using a constant-activity-rate (CA) vs a constant-flow-rate (CF) infusion to improve test–retest repeatability of MBF measurements. 22 participants underwent single-session 82Rb dynamic PET imaging during rest and dipyridamole stress using one of 2 test–retest infusion protocols: CA–CA (n = 12) or CA–CF (n = 10). MBF was quantified using a single-tissue-compartment model (1TCM) and a simplified retention model (SRM). Non-parametric test–retest repeatability coefficients (RPCnp) were compared between groups. Myocardium-to-blood contrast and signal-to-noise ratios of the late uptake images (2 to 6 minutes) were also compared to evaluate standard myocardial perfusion image (MPI) quality. MBF values in the CA–CA group were more repeatable (smaller RPCnp) than the CA–CF group using the 1TCM at rest alone, rest and stress combined, and stress/rest reserve (21% vs 36%, 16% vs 19%, and 20% vs 27%, P < 0.05, respectively), and using the SRM at Rest and Stress alone, Rest and Stress combined, and stress/rest reserve (21% vs 38%, 15% vs 25%, 22% vs 38%, and 23% vs 49%, P < 0.05, respectively). In terms of image quality, myocardium-to-blood contrast and signal-to-noise ratios were not significantly different between groups. Constant-activity-rate ‘square-wave’ infusion of 82Rb produces more repeatable tracer injection profiles and decreases the test–retest variability of MBF measurements, when compared to a constant-flow-rate ‘bolus’ administration of 82Rb, especially with SRM, and without compromising standard MPI quality.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".