Assessment of resting myocardial blood flow in regions of known transmural scar to confirm accuracy and precision of 3D cardiac positron emission tomography
Bibliographic record
Abstract
Abstract Background Composite invasive and non-invasive data consistently demonstrate that resting myocardial blood flow (rMBF) in regions of known transmural myocardial scar (TMS) converge on a value of ~ 0.30 mL/min/g or lower. This value has been confirmed using the 3 most common myocardial perfusion agents (13N, 15O-H2O and 82Rb) incorporating various kinetic models on older 2D positron emission tomography (PET) systems. Thus, rMBF in regions of TMS can serve as a reference “truth” to evaluate low-end accuracy of various PET systems and software packages (SWPs). Using 82Rb on a contemporary 3D-PET-CT system, we sought to determine whether currently available SWP can accurately and precisely measure rMBF in regions of known TMS. Results Median rMBF (in mL/min/g) and COV in regions of TMS were 0.71 [IQR 0.52–1.02] and 0.16 with 4DM; 0.41 [0.34–0.54] and 0.10 with 4DM-FVD; 0.66 [0.51–0.85] and 0.11 with Cedars; 0.51 [0.43–0.61] and 0.08 with Emory-Votaw; 0.37 [0.30–0.42], 0.07 with Emory-Ottawa, and 0.26 [0.23–0.32], COV 0.07 with HeartSee. Conclusions SWPs varied widely in low end accuracy based on measurement of rMBF in regions of known TMS. 3D PET using 82Rb and HeartSee software accurately (0.26 mL/min/g, consistent with established values) and precisely (COV = 0.07) quantified rMBF in regions of TMS. The Emory-Ottawa software yielded the next-best accuracy (0.37 mL/min/g), though rMBF was higher than established gold-standard values in ~ 5% of the resting scans. 4DM, 4DM-FDV, Cedars and Emory-Votaw SWP consistently resulted values higher than the established gold standard (0.71, 0.41, 0.66, 0.51 mL/min/g, respectively), with higher interscan variability (0.16, 0.11, 0.11, and 0.09, respectively). Trial registration: clinicaltrial.gov, NCT05286593, Registered December 28, 2021, https://clinicaltrials.gov/ct2/show/NCT05286593 .
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".