Saline-push improves rubidium-82 PET image quality
Bibliographic record
Abstract
Rubidium-82 (82Rb) PET is used widely for myocardial perfusion imaging. The purpose of this study was to investigate if an additional saline-push following the 82Rb elution improves PET image quality. 82Rb PET scans were acquired with and without 26 mL saline-push in six patients as part of a clinical quality improvement program. Dynamic images were analyzed to measure the total activity delivered to the superior vena cava (SVC) and retained in the left ventricle (LV) myocardium. Tracer uptake images were used to measure blood background coefficient-of-variation (COV), myocardium-to-blood signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) to assess image quality. Similar eluted activity was measured with/without the saline-push (830 vs 795 MBq; P = 0.24). The activity delivered to the heart and retained in the myocardium was consistently increased more than twofold (SVC: + 114% and LV: + 104%; P < 0.001) with the saline-push. Image quality was improved in all patients, with lower background noise (COV: − 19%), higher SNR (+ 24%) and CNR (+ 27%) (all P ≤ 0.01). The saline-push used to flush 82Rb activity out of the infuser tubing, patient injection and intravenous access lines consistently increased the activity delivered to the heart by twofold. This technique is recommended to maximize image quality with 82Rb PET.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".