Optimal reconstruction interval in dual source CT coronary angiography: a single-center experience in 285 patients
Bibliographic record
Abstract
PURPOSE: We aimed to evaluate the visibility of coronary arteries and bypass-grafts in patients who underwent dual source computed tomography (DSCT) angiography without heart rate (HR) control and to determine optimal intervals for image reconstruction. MATERIALS AND METHODS: A total of 285 consecutive cases who underwent coronary (n=255) and bypass-graft (n=30) DSCT angiography at our institution were identified retrospectively. Patients with atrial fibrillation were excluded. Ten datasets in 10% increments were reconstructed in all patients. On each dataset, the visibility of coronary arteries was evaluated using the 15-segment American Heart Association classification by two radiologists in consensus. RESULTS: Mean HR was 76±16.3 bpm, (range, 46-127 bpm). All coronary segments could be visualized in 277 patients (97.19%). On a segment-basis, 4265 of 4275 (99.77%) coronary artery segments were visible. All segments of 56 bypass-grafts in 30 patients were visible (100%). Total mean segment visibility scores of all coronary arteries were highest at 70%, 40%, and 30% intervals for all HRs. The optimal reconstruction intervals to visualize the segments of all three coronary arteries in descending order were 70%, 60%, 80%, and 30% intervals in patients with a mean HR <70 bpm; 40%, 70%, and 30% intervals in patients with a mean HR 70-100 bpm; and 40%, 50%, and 30% in patients with a mean HR >100 bpm. CONCLUSION: Without beta-blocker administration, DSCT coronary angiography offers excellent visibility of vascular segments using both end-systolic and mid-late diastolic reconstructions at HRs up to 100 bpm, and only end-systolic reconstructions at HRs over 100 bpm.
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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".