Inert fluorinated gas MRI: a new pulmonary imaging modality
Bibliographic record
Abstract
Fluorine-19 ((19)F) MRI of the lungs using inhaled inert fluorinated gases can potentially provide high quality images of the lungs that are similar in quality to those from hyperpolarized (HP) noble gas MRI. Inert fluorinated gases have the advantages of being nontoxic, abundant, and inexpensive compared with HP gases. Due to the high gyromagnetic ratio of (19)F, there is sufficient thermally polarized signal for imaging, and averaging within a single breath-hold is possible due to short longitudinal relaxation times. Therefore, the gases do not need to be hyperpolarized prior to their use in MRI. This eliminates the need for an expensive polarizer and expensive isotopes. Inert fluorinated gas MRI of the lungs has been previously demonstrated in animals, and more recently in healthy volunteers and patients with lung diseases. The ongoing improvements in image quality demonstrate the potential of (19)F MRI for visualizing the distribution of ventilation in human lungs and detecting functional biomarkers. In this brief review, the development of inert fluorinated gas MRI, current progress, and future prospects are discussed. The current state of HP noble gas MRI is also briefly discussed in order to provide context to the development of this new imaging modality. Overall, this may be a viable clinical imaging modality that can provide useful information for the diagnosis and management of chronic respiratory diseases.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".