Medical Applications of Hyperpolarized and Inert Gases in MR Imaging and NMR Spectroscopy
Bibliographic record
Abstract
MRI is a potentially ideal imaging modality for non-invasive, non-ionizing, and longitudinal assessment of disease. One notable disadvantage of MRI is its low sensitivity compared to other imaging modalities, and this drawback can be rectified with hyperpolarized (HP) agents that have been developed over the past 20 years. HP agents have the potential to vastly improve MRI sensitivity for the diagnosis and management of various diseases. The polarization of NMR-sensitive nuclei other than 1H (e.g. 3He, 129Xe) can be enhanced by a factor of up to 100 000 times above thermal equilibrium levels, thus enabling direct detection of the HP agent at low concentration and with no background signal. In this chapter, a number of HP media applications in MR imaging is discussed, including HP 3He and 129Xe lung imaging, HP 129Xe brain imaging, and HP 129Xe biosensors. Inert fluorinated gas MRI, which is a new lung imaging technique that does not require hyperpolarization, is also briefly discussed. These techniques will likely be important future directions for the HP gas lung imaging community.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".