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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".