Noninvasive PET Flow Reserve Imaging to Direct Optimal Therapies for Myocardial Ischemia
Bibliographic record
Abstract
Nuclear cardiology imaging with SPECT or PET is used widely in North America for the diagnosis and management of patients with coronary artery disease. Conventional myocardial perfusion imaging (MPI) can identify areas of reversible ischemia as suitable targets for coronary artery revascularization by angioplasty or bypass surgery. However, the accuracy of this technique is limited in patients with advanced disease in multiple coronary arteries, where there is no normal reference territory against which to assess the “relative” perfusion defects. We have developed methods for the routine quantification of absolute myocardial blood flow (MBF mL/min/g) and coronary flow reserve (stress/rest MBF) using rubidium-82 dynamic PET imaging. The incremental diagnostic and prognostic value of absolute flow quantification over conventional MPI has been demonstrated in several recent studies. Clinical use of this added information for patient management to direct optimal therapy and the potential to improve cardiac outcomes remains unclear, but may be informed by recent progress and widespread clinical adoption of invasive fractional flow reserve(FFR)-directed revascularization. This paper presents recent progress in this field, toward noninvasive CFR image-guided therapy with cardiac PET and SPECT.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.026 |
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".