Coronary Artery Imaging with Single-Source Rapid Kilovolt Peak–Switching Dual-Energy CT
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 it