Oxygen‐enhanced MR imaging of mice lungs
Bibliographic record
Abstract
Inhaled molecular oxygen has been widely used in humans to evaluate pulmonary ventilation using MRI. MR imaging has recently played a greater role in examining the morphologic and physiologic characteristics of mouse models of lung disease where structural changes are highly correlated to abnormalities in respiratory function. The motivation of this work is to develop oxygen-enhanced MR imaging for mice. Conventional human MR techniques cannot be directly applied to mouse imaging due to smaller dimensions and faster cardiac and respiratory physiology. This study examines the development of oxygen-enhanced MR as a noninvasive tool to assess regional ventilation in spontaneously breathing mice. An optimized cardiac-triggered, respiratory-gated fast spin-echo imaging sequence was developed to address demands of attaining adequate signal from the parenchyma, maintaining practical acquisition times, and compensating for rapid physiological motion. On average, a 20% T1-shortening effect was observed in mice breathing 100% oxygen as compared to air. The effect of ventilation was shown as a significant signal intensity increase of 11% to 16% in the mouse parenchyma with 100% oxygen inhalation. This work demonstrates that adequate contrast and resolution can be achieved using oxygen-enhanced MR to visualize ventilation, providing an effective technique to study ventilation defects in mice.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".