Bibliographic record
Abstract
BACKGROUND: One disadvantage of magnetic resonance imaging (MRI) is the inability to adequately image the lungs. Recent advances in hyperpolarized gas technology [e.g., helium-3 ((3)He) and xenon-129 ((129)Xe)] have changed this. However, the required technology is expensive and often needing extra physics or engineering staff. Hence there is considerable interest in developing (1)H (proton)-based MRI approaches that can be readily implemented on standard clinical systems. Thus, the purpose of this work was to compare a newly developed free breathing proton-based MR lung imaging method to that of a standard gadolinium (Gd) based perfusion approach. METHODS: Healthy volunteers [10] were scanned using a 3-T MRI with 8 parallel receivers, and a cardiac gated fast spin echo (FSE) sequence. Acquisition was cardiac triggered, with different time delays incremented to cover the entire cardiac cycle. Image k-space was filled rectilinearly. But to reduce motion artefacts k-space was retrospectively sorted using the minimal variance algorithm (MVA), based on physiologic data recorded from both the respiratory bellows and electrocardiogram (ECG). Resorted and reconstructed FSE images were compared to contrast enhanced lung images, obtained following intravenous injection of Gd-DTPA-BMA. RESULTS: Biphasic variation in FSE lung signal intensity was observed across the cardiac cycle with a maximal signal change following rapid cardiac ejection (between S and T waves), and following rapid isovolumetric relaxation. A difference image between systolic and diastolic states in the cardiac cycle resulted in images with improved lung contrast to noise ratio (CNR). FSE image intensity was uniform over lung parenchyma while Gd-based enhancement of spoiled gradient recalled echo (SPGR) images showed gravitational dependence. CONCLUSIONS: Here we show how 1H-MR images of lung can be obtained during free breathing. The image contrast obtained during this approach is likely the result of flow and oxygen modulation during the cardiac cycle. This free breathing method provides lung images comparable to those obtained using Gd-enhancement. Besides having the advantage of free breathing, this approach doesn't require any Gd-contrast or suffer from methodological problems associated with perfusion (e.g., poor bolus timing). However, as gravitational differences typically observed in lung perfusion are not visible with this method it is not providing exclusive microvascular perfusion information.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".