Effects of doubling and tripling the spatial resolution in standard 3D contrast‐enhanced magnetic resonance angiography of carotid artery disease
Bibliographic record
Abstract
PURPOSE: To determine whether three-dimensional (3D) contrast-enhanced MR angiography (CE-MRA) of carotid artery disease may be more effective when performed at double or triple the spatial resolution of the present common clinical standard at 1.5T. MATERIALS AND METHODS: A total of 110 consecutive patients with suspected carotid artery disease were imaged with elliptical centric 3D CE-MRA. The total acquisition time was increased from the standard clinical protocol of 21 seconds up to 60 seconds in 10-second steps, with corresponding voxel volume reductions from 0.95 mm(3) down to 0.35 mm(3). Quantitative and blinded qualitative measurements were then performed to determine the preferred imaging time. RESULTS: In patients with significant stenosis, the 40-second acquisition with 0.53-mm(3) voxels produced significantly sharper images of the carotid bifurcation than the 21-second standard using 0.95-mm(3) voxels, but did not have a significant effect in patients without disease. CONCLUSION: In patients with carotid artery stenosis, decreasing the voxel volume to 0.5 mm(3) by increasing the scan time from 21 to 40 seconds resulted in sharper depiction of the carotid stenosis. Further decreases in voxel volume, by extending the acquisition time further, did not improve the vessel depiction due to both signal-to-noise ratio (SNR) and sharpness losses.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 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".