Hyperpolarized noble gas magnetic resonance imaging of the animal lung: Approaches and applications
Bibliographic record
Abstract
Hyperpolarized noble gas (HNG) magnetic resonance (MR) imaging is a very promising noninvasive tool for the investigation of animal models of lung disease, particularly to follow longitudinal changes in lung function and anatomy without the accumulated radiation dose associated with x rays. The two most common noble gases for this purpose are H3e (helium 3) and X129e (xenon 129), the latter providing a cost-effective approach for clinical applications. Hyperpolarization is typically achieved using spin-exchange optical pumping techniques resulting in ∼10 000-fold improvement in available magnetization compared to conventional Boltzmann polarizations. This substantial increase in polarization allows high spatial resolution (<1 mm) single-slice images of the lung to be obtained with excellent temporal resolution (<1 s). Complete three-dimensional images of the lungs with 1 mm slice thickness can be obtained within reasonable breath-hold intervals (<20 s). This article provides an overview of the current methods used in HNG MR imaging with an emphasis on ventilation studies in animals. Special MR hardware and software considerations are described in order to use the strong but nonrecoverable magnetization as efficiently as possible and avoid depolarization primarily by molecular oxygen. Several applications of HNG MR imaging are presented, including measurement of gross lung anatomy (e.g., airway diameters), microscopic anatomy (e.g., apparent diffusion coefficient), and a variety of functional parameters including dynamic ventilation, alveolar oxygen partial pressure, and xenon diffusing capacity.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".