Pulmonary functional <scp>MRI</scp>: Detecting the <scp>structure–function</scp> pathologies that drive asthma symptoms and <scp>quality of life</scp>
Bibliographic record
Abstract
Abstract Pulmonary functional MRI (PfMRI) using inhaled hyperpolarized, radiation‐free gases (such as 3He and 129Xe) provides a way to directly visualize inhaled gas distribution and ventilation defects (or ventilation heterogeneity) in real time with high spatial (~mm3) resolution. Both gases enable quantitative measurement of terminal airway morphology, while 129Xe uniquely enables imaging the transfer of inhaled gas across the alveolar–capillary tissue barrier to the red blood cells. In patients with asthma, PfMRI abnormalities have been shown to reflect airway smooth muscle dysfunction, airway inflammation and remodelling, luminal occlusions and airway pruning. The method is rapid (8–15 s), cost‐effective (~$300/scan) and very well tolerated in patients, even in those who are very young or very ill, because unlike computed tomography (CT), positron emission tomography and single‐photon emission CT, there is no ionizing radiation and the examination takes only a few seconds. However, PfMRI is not without limitations, which include the requirement of complex image analysis, specialized equipment and additional training and quality control. We provide an overview of the three main applications of hyperpolarized noble gas MRI in asthma research including: (1) inhaled gas distribution or ventilation imaging, (2) alveolar microstructure and finally (3) gas transfer into the alveolar–capillary tissue space and from the tissue barrier into red blood cells in the pulmonary microvasculature. We highlight the evidence that supports a deeper understanding of the mechanisms of asthma worsening over time and the pathologies responsible for symptoms and disease control. We conclude with a summary of approaches that have the potential for integration into clinical workflows and that may be used to guide personalized treatment planning.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".