Comparison of hyperpolarized <sup>3</sup>He MRI with Xe‐enhanced computed tomography imaging for ventilation mapping of rat lung
Bibliographic record
Abstract
Lung ventilation was mapped in five healthy Brown Norway rats (210-377 g) using both hyperpolarized (3)He MRI and Xe-enhanced computed tomography (Xe-CT) under similar ventilator conditions. Whole-lung measurements of ventilation r obtained with (3)He MRI were not significantly different from those obtained from Xe-CT (p = 0.1875 by Wilcoxon matched pairs test). The ventilation parameter r is defined as the fraction of refreshed gas per unit volume per breath. Regional ventilation was also measured in four regions of the lung using both methods. A two-tailed paired t-test was performed for each region, yielding p > 0.05 for all but the upper portion of the right lung. The distribution of regional ventilation was evaluated by calculating ventilation gradients in the superior/inferior (S/I) direction. The average S/I gradient obtained using the (3)He MRI method was found to be 0.17 ± 0.04 cm(-1) , whereas the average S/I gradient obtained using the Xe-CT method was found to be 0.016 ± 0.005 cm(-1) . In general, S/I ventilation gradients obtained from both methods were significantly different from each other (p = 0.0019 by two-tailed paired t-test). These regional differences in ventilation measurements may be caused by the manner in which the gas contrast agents distribute physiologically and/or by the imaging modality.
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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) | 0.000 | 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 | 0.000 | 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".