Functional Thoracic MRI: Recent Advances in Pulmonary Assessment
Bibliographic record
Abstract
Functional thoracic MRI provides regional assessment of the three principal components of lung function: ventilation, perfusion, and gas exchange. It offers advantages over pulmonary function tests like spirometry, which yield only global measurements. MRI enables comprehensive evaluation of respiratory mechanics, including chest wall and diaphragm motion, dynamic large airway instability, and lung ventilation using various contrast mechanisms and gas agents. Perfusion imaging, with or without exogenous contrast material, further supports the assessment of mechanical lung properties in both healthy and diseased states. Advanced MRI techniques also allow for quantification of distal airspace dimensions and gas exchange or diffusion capacity using inert noble gases, at both global and regional levels. Dynamic contrast-enhanced perfusion MRI enables assessment of key pathophysiologic mechanisms, such as hypoxic pulmonary vasoconstriction, and provides direct visualization of ventilation-perfusion mismatch across various lung diseases. Emerging noninvasive, non-contrast-enhanced techniques, including combined ventilation-perfusion imaging based on signal oscillations from blood flow and respiration, hold substantial promise for clinical translation. This review provides an overview of recent advances in functional thoracic MRI for evaluating regional lung function and pathophysiology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".