Comparison of 1.5 and 3.0 T for Contrast-Enhanced Pulmonary Magnetic Resonance Angiography
Bibliographic record
Abstract
OBJECTIVE: In a recent multi-center trial of gadolinium contrast-enhanced magnetic resonance angiography (Gd-MRA) for diagnosis of acute pulmonary embolism (PE), two centers utilized a common MRI platform though at different field strengths (1.5T and 3T) and realized a signal-to-noise gain with the 3T platform. This retrospective analysis investigates this gain in signal-to-noise of pulmonary vascular targets. METHODS: Thirty consecutive pulmonary MRA examinations acquired on a 1.5T system at one institution were compared to 30 consecutive pulmonary MRA examinations acquired on a 3T system at a different institution. Both systems were from the same MRI manufacturer and both used the same Gd-MRA pulse sequence, although there were some protocol adjustments made due to field strength differences. Region-of-interests were manually defined on the main pulmonary artery, 4 pulmonary veins, thoracic aorta, and background lung for objective measurement of signal-to-noise, contrast-to-noise, and bolus timing bias between centers. RESULTS: The 3T pulmonary MRA protocol achieved higher spatial resolution yet maintained significantly higher signal-to-noise ratio (≥13%, p = 0.03) in the main pulmonary vessels relative to 1.5T. There was no evidence of operator bias in bolus timing or patient hemodynamic differences between groups. CONCLUSION: Relative to 1.5T, higher spatial resolution Gd-MRA can be achieved at 3T with a sustained or greater signal-to-noise ratio of enhanced vasculature.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".