Positron Range Correction Helps Enhance the Image Quality of Cardiac<sup>82</sup>Rb PET/CT
Bibliographic record
Abstract
The image quality and quantitative accuracy of 82Rb myocardial perfusion imaging (MPI) using PET is challenged by the extensive positron range (PR) effects, with the PR of 82Rb being about 7 mm in soft tissues. This study explored the feasibility of applying postacquisition PR correction (PRC) to routine 82Rb PET/CT MPI acquisitions and assessed its impact on diagnostic accuracy and image quality. Methods: We implemented a PRC method adjusted to 82Rb into a vendor-provided reconstruction toolbox, using tissue-specific corrections for soft tissue, bone, and air/lungs. The PRC was evaluated in 2 cohorts: the first comprised 25 healthy volunteers who underwent repeated 82Rb MPI within 2 wk, and the second included 66 patients with known or suspected coronary artery disease. We measured the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for the volunteer cohort. In the patient cohort, the impact of PRC was evaluated as changes in the area under the receiver operating characteristic curve (AUC), using fractional flow reserve as the gold standard (values < 80% were considered significantly reduced). We calculated AUCs for stress and ischemic total perfusion deficits. Results: In the volunteer cohort, PRC-based reconstructions (standard reconstruction [STD] + PRC) demonstrated significantly improved SNR and CNR compared with STD, with median increases of 22% and 47% for SNR and CNR, respectively (P < 0.05). For the patient cohort, comparable AUCs were reported for STD- versus PRC-based reconstructions (stress total perfusion deficits, 0.84 vs. 0.83 [P = 0.49]; ischemic total perfusion deficits, 0.87 vs. 0.87 [P = 0.80]). Conclusion: PRC significantly enhances SNR and CNR compared with STD without affecting the diagnostic accuracy of the scans. Given the significantly improved image quality, PRC may be recommended for MPI using 82Rb PET/CT clinical-routine-assessment interpretation of TPD.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".