Abolition of Respiratory-Motion Artifact in Computed Tomography Coronary Angiography with Ultrafast Examinations: A Comparison between 64-row and 320-row Multidetector Scanners
Bibliographic record
Abstract
PURPOSE: To compare the incidence of respiratory artifact in computed tomography (CT) coronary angiography performed with 64-row and 320-row multidetector scanners and to assess its effect on coronary evaluability. METHODS: A retrospective review of consecutive coronary angiograms performed on a 64-row multidetector CT from March to April 2007 (group 1: 115 patients, 63 men; mean age [standard deviation] 59.6 +/- 12.8 years) and on a 320-row multidetector CT from March to April 2008 (group 2: 169 patients, 89 men; mean [SD] age 57.9 +/- 11.6 years). Two cardiac radiologists assessed the occurrence of respiratory artifact and coronary evaluability in studies with respiratory artifacts. Unevaluable coronary segments because of motion at the same anatomical level of the respiratory artifact were considered unevaluable because of this artifact. The association between the occurrence of respiratory artifact and patient biometrics, medication, and scan parameters was examined. RESULTS: Respiratory artifacts were detected in 9 of the 115 patients from group 1 (7.8%) and in none of the 169 patients from group 2 (P < .001). Group 1 had longer scan times (median, 9.3 seconds; range, 7.5-14.4 seconds) compared with group 2 (median, 1.5 seconds; range, 1.1-3.5 seconds; P < .001). In group 1, 4 patients (3.5%) showed unevaluable coronary segments because of respiratory artifacts, and the CT coronary angiography was repeated in 1 patient (0.9%). CONCLUSIONS: Respiratory artifacts are important in CT coronary angiography performed with 64-row multidetector scanners and impair the diagnostic utility of the examination in up to 3.5% of the studies. These artifacts can be virtually eliminated with a faster scan time provided by 320-row multidetector CT.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".