Coronary Artery Imaging with Single-Source Rapid Kilovolt Peak–Switching Dual-Energy CT
Bibliographic record
Abstract
PURPOSE: To evaluate beam-hardening (BH) artifact reduction in coronary computed tomography (CT) angiography with dual-energy CT, to define the optimal monochromatic-energy levels for coronary and myocardial signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in dual-energy CT, and to compare these levels with single-energy CT. MATERIALS AND METHODS: The study was approved by the institutional review board and/or ethics committee at each site. Patients provided informed consent. Thirty-nine patients were prospectively enrolled to undergo dual-energy CT, and 25 also underwent single-energy CT. Myocardial and coronary SNR, CNR, and iodine concentration were measured across multiple segments at varying monochromatic energy levels (40-140 keV). BH was defined as signal decrease in basal inferior wall versus midinferior wall, and signal increase in midseptum versus midinferior wall. Generalized estimating equation was used to identify optimal monochromatic-energy levels and compare them with single-energy CT. RESULTS: BH was noted at single-energy CT with basal inferior wall mean reduction of 19.7 HU ± 29.2 (standard deviation) and midseptum increase of 46.3 HU ± 36.3. There was reduction in this artifact at 90 keV or greater (1.7 HU ± 18.4 in basal inferior wall and 20.1 HU ± 37.5 in midseptum at 90 keV; P < .05). SNR and CNR were higher in the myocardium and coronary arteries at 60-80 keV than single-energy CT (myocardium: SNR, 3.02 vs 2.39, and CNR, 6.73 vs 5.16; coronary arteries: SNR, 10.83 vs 7.75, and CNR, 13.31 vs 9.54; P < .01). Mean iodine concentration in resting myocardium was 2.19 mg/mL ± 0.57. CONCLUSION: Rapid kilovolt peak-switching dual-energy CT resulted in significant BH reduction and improvements in SNR and CNR in the myocardium and coronary arteries.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".