Parametric imaging of myocardial blood flow with 82Rb PET: An accuracy and image quality analysis
Bibliographic record
Abstract
BACKGROUND: We aimed to develop a framework for generating three-dimensional (3D) myocardial blood flow (MBF) images, computing their accuracy against clinically validated two-dimensional (2D) polar MBF maps of the left ventricle, and evaluating their improvements in image quality over relative myocardial perfusion imaging (MPI). METHODS: N = 40 patients with a wide range of defect severities and uptake dynamics were retrospectively studied. The FlowQuant™ software was used to generate reference MPI and polar MBF maps and was adapted for voxel-wise MBF mapping. We evaluated agreement between parametric vs polar values for MBF at rest and stress and for reserve (stress/rest MBF). We also assessed improvements in image quality, assessed by signal-to-noise ratio, contrast-to-noise ratio, tissue-to-blood ratio, and defect severity, from relative MPI to MBF. RESULTS: There was excellent agreement between 3D parametric and 2D polar maps for all flow parameters (interclass correlation coefficient >0.96), albeit with minimal bias (<8%) for rest and stress MBF at the patient level. Image quality substantially improved from MPI to MBF in every patient for all image-quality metrics (P < 0.0001) CONCLUSIONS: We developed a robust methodology for producing highly accurate 3D MBF images exhibiting considerably improved image quality compared to relative MPI commonly used in clinical practice.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".