Constant-Activity-Rate Infusions for Myocardial Blood Flow Quantification with 82Rb and 3D PET
Bibliographic record
Abstract
Quantification of myocardial blood flow (MBF) can be performed using dynamic82Rb PET imaging. However, the small dynamic range of some 3D PET systems can prevent accurate measurement of the first-pass activity with standard82Rb bolus infusions. The purpose of this study was to investigate the use of slow constant-activity-rate infusions to optimize MBF quantification with 3D PET. Methods: Dynamic 3D PET imaging was performed in a normal dog with 150 MBq of82Rb infused over 15, 30, 60, 120 and 240 s. Left ventricular mean MBF was quantified using a one-compartment model of82Rb kinetics, and compared to standard values obtained using13N-ammonia with a two-compartment model. Results: Peak deadtime and coincidence count-rates decreased by almost 50% and 70% respectively with 240 vs. 30 s tracer infusion, but integral true coincidences (prompt - delayed) were maintained. Mean MBF values remained accurate (1.4 vs. 1.3 mL/min/g) using 240 vs. 30 s infusions, and vs. standard13N-ammonia MBF values (1.3 mL/min/g). Precision of the MBF estimates was also improved by a factor of 3.5. Conclusion: Constant-activity-rate infusions reduce the dynamic range needed for MBF quantification with82Rb and 3D PET imaging. Using a one-compartment model, the precision of MBF estimates are also improved with slower infusions. This may permit improved MBF quantification using 3D PET, with high integral counts maintained for conventional perfusion imaging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".