Quantification of lung microstructure with hyperpolarized <sup>3</sup>He diffusion MRI
Bibliographic record
Abstract
The structure and integrity of pulmonary acinar airways and their changes in different diseases are of great importance and interest to a broad range of physiologists and clinicians. The introduction of hyperpolarized gases has opened a door to in vivo studies of lungs with MRI. In this study we demonstrate that MRI-based measurements of hyperpolarized (3)He diffusivity in human lungs yield quantitative information on the value and spatial distribution of lung parenchyma surface-to-volume ratio, number of alveoli per unit lung volume, mean linear intercept, and acinar airway radii-parameters that have been used by lung physiologists for decades and are accepted as gold standards for quantifying emphysema. We validated our MRI-based method in six human lung specimens with different levels of emphysema against direct unbiased stereological measurements. We demonstrate for the first time MRI images of these lung microgeometric parameters in healthy lungs and lungs with different levels of emphysema (mild, moderate, and severe). Our data suggest that decreases in lung surface area per volume at the initial stages of emphysema are due to dramatic decreases in the depth of the alveolar sleeves covering the alveolar ducts and sacs, implying dramatic decreases in the lung's gas exchange capacity. Our novel methods are sufficiently sensitive to allow early detection and diagnosis of emphysema, providing an opportunity to improve patient treatment outcomes, and have the potential to provide safe and noninvasive in vivo biomarkers for monitoring drug efficacy in clinical trials.
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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 | 0.001 |
| 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 | 0.001 |
| 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".