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 with <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sup> FDG. Dynamic 3D-mode PET imaging for quantification of myocardial blood flow (MBF) with <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">82</sup> Rb 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 using <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">82</sup> Rb and <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">13</sup> NH <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> in 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 with <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">82</sup> Rb 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 with <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">82</sup> Rb 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".