<sup>19</sup>F MRI of the Lungs Using Inert Fluorinated Gases: Challenges and New Developments
Bibliographic record
Abstract
Fluorine‐19 (19F) MRI using inhaled inert fluorinated gases is an emerging technique that can provide functional images of the lungs. Inert fluorinated gases are nontoxic, abundant, relatively inexpensive, and the technique can be performed on any MRI scanner with broadband multinuclear imaging capabilities. Pulmonary 19F MRI has been performed in animals, healthy human volunteers, and in patients with lung disease. In this review, the technical requirements of 19F MRI are discussed, along with various imaging approaches used to optimize the image quality. Lung imaging is typically performed in humans using a gas mixture containing 79% perfluoropropane (PFP) or sulphur hexafluoride (SF6) and 21% oxygen. In lung diseases, such as asthma, chronic obstructive pulmonary disease (COPD), and cystic fibrosis (CF), ventilation defects are apparent in regions that the inhaled gas cannot access. 19F lung images are typically acquired in a single breath‐hold, or in a time‐resolved, multiple breath fashion. The former provides measurements of the ventilation defect percent (VDP), while the latter provides measurements of gas replacement (ie, fractional ventilation). Finally, preliminary comparisons with other functional lung imaging techniques are discussed, such as Fourier decomposition MRI and hyperpolarized gas MRI. Overall, functional 19F lung MRI is expected to complement existing proton‐based structural imaging techniques, and the combination of structural and functional lung MRI will provide useful outcome measures in the future management of pulmonary diseases in the clinic. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:343–354.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".