3D list-mode cardiac PET for simultaneous quantification of myocardial blood flow and ventricular function
Bibliographic record
Abstract
Current PET instrumentation and performance metrics have been developed primarily to optimize whole-body imaging with18FDG. Dynamic 3D-mode PET imaging for quantification of myocardial blood flow (MBF) with82Rb requires high count-rate capability and correction accuracy maintained over a wide range of activity. Therefore, we propose a new method to evaluate dynamic range for 3D cardiac PET imaging and evaluate the performance of a high count-rate PET system (GEHC Discovery Rx) for single-scan simultaneous quantification of MBF and left ventricular ejection fraction (EF) using list-mode PET imaging. Dynamic imaging was performed over a wide range of activities using82Rb and13NH3in a heart phantom, within and without an anthropomorphic torso, by FORE-FBP and a 12 mm 3D Hann filter. Time-activity curves (TAC) in the liver, myocardium and ventricle were analyzed to determine the operating range where quantitative accuracy is maintained in the reconstructed images. Dynamic rest-stress list-mode imaging was also performed in 21 patients with82Rb PET to compare MBF and EF quantification between 2D and 3D-modes. Based on the phantom results, injected activity was targeted at 10 MBq/kg to permit accurate measurement of the bolus first-pass activity in the LV cavity. The phantom studies indicated that activity concentrations were measured accurately (≪15% deviation) with prompt count rates ≪10 Mcps and dead-time losses ≪35%. There was no count-rate-dependent loss of resolution observed in the myocardium:liver TACs, even above these limits. Residual scatter in the ventricle cavity:liver was 3.4% and consistent across the whole dynamic range. For the patient studies, pseudo-NEC rates were 30% higher in 3D (p≪0.001), resulting in improved image quality. There were no significant differences in segmental myocardium uptake distribution, LVEF or MBF between 2D and 3D-modes (P=NS). Conclusion: Quantitative 3D cardiac imaging appears to be accurate with82Rb activity administered in the range of 9±1.5 MBq/kg used in this study. If the dynamic range of the PET system was increased further, higher injected activity and improved ECG-gated image quality may be obtained, while still retaining the quantitative accuracy of the first-pass data for accurate MBF quantification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".