Accuracy of Dynamic Computed Tomography Adenosine Stress Myocardial Perfusion Imaging in Estimating Myocardial Blood Flow at Various Degrees of Coronary Artery Stenosis Using a Porcine Animal Model
Bibliographic record
Abstract
OBJECTIVE: To determine the accuracy of computed tomography (CT) dynamic stress myocardial perfusion imaging to estimate myocardial blood flow (MBF) in a porcine animal model with variable degrees of induced coronary artery stenosis in comparison with microsphere-derived MBF. METHODS AND MATERIALS: Seven domestic pigs (36 ± 4 kg) received stents (confirmed 3.0 mm diameter) in the left anterior descending coronary artery distal to first diagonal branch. A balloon catheter was placed within the stent and inflated to various degrees to obtain a defined luminal narrowing (50% and 75% diameter stenosis) as confirmed by intra-arterial flow wire measurement. All models underwent adenosine-mediated (140 μg/kg/min) dynamic stress and rest myocardial perfusion CT imaging using a dual-source CT scanner (shuttle-mode with 100 kV/300 mAs, 20 mL iopromide) with prospective acquisitions every second heartbeat for 30 seconds. CT-estimated MBF (MBFCT) was calculated using a model-based parametric deconvolution method and correlated to that of fluorescent microspheres (MBFmic) injected at each perfusion state. RESULTS: All study procedures were performed without complications, and all animals completed the study protocol. Among 448 myocardial segments, 31 (7%) were considered nonevaluable because of motion artifacts. With stress, MBFCT increased significantly (1.10 ± 0.25 vs. 0.80 ± 0.28 mL/g/min, P < 0.001; at stress and rest, respectively) in all myocardial segments and correlated with MBFmic (r = 0.67, P < 0.001). MBFCT overestimated MBFmic, independently of adenosine-stress and degree of coronary stenosis (β = 2.3, 95% confidence interval: 1.81-2.79 mL/g/min, P < 0.001). Although there were no differences in MBFCT between 50% and 75% coronary stenosis at rest (0.01 ± 0.08 mL/g/min, P = 0.86), MBFCT was significantly lower at 75% than at 50% under stress conditions (0.53 ± 0.19 vs. 0.71 ± 0.24 mL/g/min, P = 0.002). CONCLUSIONS: CT-derived MBF measurements at rest and stress with varying degrees of coronary stenosis show a valid difference but an underestimated correlation with microsphere-derived MBF in a porcine animal model.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".