Expanding Applications of Pulmonary MRI in the Clinical Evaluation of Lung Disorders: Fleischner Society Position Paper
Bibliographic record
Abstract
Pulmonary MRI provides structural and quantitative functional images of the lungs without ionizing radiation, but it has had limited clinical use due to low signal intensity from the lung parenchyma. The lack of radiation makes pulmonary MRI an ideal modality for pediatric examinations, pregnant women, and patients requiring serial and longitudinal follow-up. Fortunately, recent MRI techniques, including ultrashort echo time and zero echo time, are expanding clinical opportunities for pulmonary MRI. With the use of multicoil parallel acquisitions and acceleration methods, these techniques make pulmonary MRI practical for evaluating lung parenchymal and pulmonary vascular diseases. The purpose of this Fleischner Society position paper is to familiarize radiologists and other interested clinicians with these advances in pulmonary MRI and to stratify the Society recommendations for the clinical use of pulmonary MRI into three categories: (a) suggested for current clinical use, (b) promising but requiring further validation or regulatory approval, and (c) appropriate for research investigations. This position paper also provides recommendations for vendors and infrastructure, identifies methods for hypothesis-driven research, and suggests opportunities for prospective, randomized multicenter trials to investigate and validate lung MRI methods. © RSNA, 2020 Online supplemental material is available for this article.
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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 | 0.055 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.024 | 0.014 |
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".