Optimization of 3D contrast‐enhanced pulmonary magnetic resonance angiography in pediatric patients with congenital heart disease
Bibliographic record
Abstract
Contrast kinetics were studied in the main pulmonary artery (MPA) and ascending aorta (AAo) of 12 children with congenital heart disease. This information was used to optimize the timing of data acquisition for contrast-enhanced MR angiography in these vessels. To reduce contrast-agent dosage in these fragile patients, contrast enhancement was measured during routine diagnostic 3D magnetic resonance (MR) angiography instead of using test-bolus methods. This was possible by acquiring 2D cross-sectional images of the MPA and AAo during the 3D scan. Time-to-peak in the MPA and AAo was 4.9 +/- 2.2 and 6.1 +/- 2.2 s, respectively, while the transit time between the two vessels was 4.5 +/- 0.6 s. A point-spread-function analysis showed that intravascular signal strength was maximized if data acquisition began 4.7 +/- 2.3 s after the first arrival of contrast in the MPA and 5.6 +/- 2.3 s in the AAo. Little signal loss and artifact resulted when longer acquisition delays were used because contrast-agent clearance was slow. Based on these results, MR angiography of both the MPA and the AAo in children with congenital heart disease can be performed using elliptic-centric k-space sampling and a trigger delay of 7.9 s after contrast arrival in the AAo (i.e., time-to-peak signal strength in the AAo plus one SD to account for intersubject variability).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".