Effects of cardiac motion on 3D contrast‐enhanced MR angiography of the carotid arteries
Bibliographic record
Abstract
PURPOSE: To determine the effect of cardiac-related carotid artery motion on the image quality of 3D contrast-enhanced MR angiography (CEMRA) in patients presenting with suspected carotid artery disease. MATERIALS AND METHODS: Twenty patients with suspected carotid artery disease underwent cardiac-gated cinematic steady-state free precession of the carotid arteries followed by standard 3D CEMRA at 1.5 T. Using postprocessing, computer programs determined the degree of vessel wall dilation and translation across the cardiac cycle from the cinematic exam and related this to vessel wall sharpness in 3D CEMRA, which was determined objectively by computer analysis and subjectively by a panel of expert neuroradiologists. RESULTS: In patients, across 40 arteries the average carotid vessel movement due to cardiac pulsation was 0.36 ± 0.17 mm and translation 1.53 ± 0.94 mm. When using computer analysis of sharpness, the mean carotid wall motion had a weak negative correlation with 3D CEMRA vessel sharpness (Pearson's correlation -0.23, P < 0.01). However, the same trend was not present from the radiological review. CONCLUSION: In standard 3D CEMRA in patients with suspected carotid artery disease, cardiac-related carotid movement was a statistically significant source of degradation in vessel sharpness, but did not appear to be clinically significant.
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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 | 0.006 |
| 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) | 0.001 | 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".