Automatic motion correction for myocardial blood flow estimation improves diagnostic performance for coronary artery disease in 18F-flurpiridaz positron emission tomography-myocardial perfusion imaging
Bibliographic record
Abstract
Background Motion correction (MC) is critical for accurate quantification of myocardial blood flow (MBF) and flow reserve (MFR) from 18 F-flurpiridaz positron emission tomography (PET) myocardial perfusion imaging (MPI). However, manual correction is time consuming and introduces inter-observer variability. We aimed to validate an automatic MC algorithm for 18 F-flurpiridaz PET-MPI in terms of diagnostic performance for predicting coronary artery disease (CAD). Methods In total, 231 patients who underwent invasive coronary angiography and rest/pharmacologic stress 18 F-flurpiridaz PET-MPI from the phase III Flurpiridaz trial (NCT01347710) were enrolled. For manual MC, two operators (Reader 1 and Reader 2) shifted each frame's images in three directions. The automatic MC algorithm, initially developed for 82 Rb-chloride PET-MPI, was optimized for 18 F-flurpiridaz. Diagnostic performance was compared using minimal segmental MBF/MFR with and without MC to predict obstructive CAD by invasive coronary angiography. Results Manual MC took 10 minutes per case (both stress and rest) on average, while automatic MC required <10 seconds. The area under the receiver operating characteristic curves (AUCs) for significant CAD using minimal segmental MBF were comparable between automatic and manual MC (AUC = 0.877 automatic, AUC = 0.888 Reader 1 and AUC = 0.892 Reader 2; all P > 0.05). AUCs of minimal segmental MBF with manual and automatic MC were significantly higher than without MC ( P < 0.05 for both). Similar findings were observed with minimal segmental MFR . Conclusions Automatic MC can be performed rapidly, with diagnostic performance for predicting obstructive CAD comparable to manual MC. This method could be utilized for analysis of MBF/MFR in patients undergoing 18 F-flurpiridaz PET-MPI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".