An imaging performance standard for MBF quantification with 3D PET
Bibliographic record
Abstract
1372 Objectives Quantification of myocardial blood flow (MBF) is used increasingly in clinical research and management of CAD patients. New 3D list-mode PET scanners can reconstruct dynamic images for MBF together with standard ECG-gated perfusion images. High-count-rate correction accuracy is critical during the injected bolus first-pass, but this not adequately assessed by current imaging standards. We propose a method to characterize 3D PET systems for quantitative MBF imaging. Methods A Data Spectrum anthropomorphic torso phantom is used with liver and myocardium compartments filled with equal concentration of N-13 or C-11 solution (50 mCi total), and scanned over 8 half-lives in 40 equal time frames. Dynamic images are reconstructed with all corrections and minimal smoothing. Time-activity curves are generated for myocardial wall, cavity and liver compartments to measure reconstructed correction accuracy over a wide dynamic range. Bias is assessed as a function of activity and dead-time factors for 2 state-of-the-art 3D PET systems. Results On the Discovery 690(&RX) PET/CT system, the bias in reconstructed activity was less than 15% with 25(&20) mCi in the field of view, and a corresponding dead-time correction factor of 1.7(&1.7). Myocardium wall-to-liver ratios varied by less than 2%(&4%) indicating minimal loss of resolution due to high-count-rate pile-up effects. Myocardium cavity-to-liver ratios varied by less than 6%(&3%) indicating low and stable residual scatter. Conclusions Dynamic range was assessed for two 3D PET systems to determine suitability for quantitative MBF imaging. On both systems, activity should be administered to limit the dead-time to
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".