High spatial resolution hyperpolarized <sup>3</sup>He MRI of the rodent lung using a single breath X‐centric gradient‐recalled echo approach
Bibliographic record
Abstract
Purpose Hyperpolarized (HP) gas MRI of the rodent lung is of great interest because of the increasing need for novel biomarkers with which to develop new therapies for respiratory diseases. The use of fast gradient‐recalled echo (FGRE) for high‐resolution HP gas rodent lung MRI is challenging as a result of signal loss caused by significant diffusion weighting, particularly in the larger airways. In this work, a modified FGRE approach is described for HP 3He rodent lung MRI using a centric‐out readout scheme (ie, x‐centric), allowing high‐resolution, density‐weighted imaging. Methods HP 3He x‐centric imaging was performed in a phantom and compared with a conventional partial‐echo FGRE acquisition for in‐plane spatial resolutions varying between 39 and 312 µm. Partial‐echo and x‐centric acquisitions were also compared for high spatial‐resolution breath‐hold (1 s) imaging of rodent lungs. Results X‐centric provided improved signal‐to‐noise ratio efficiency by a factor of up to 13/1.7 and 6.7/1.8, compared with the partial‐echo FGRE for the airways/parenchyma of mouse and rat, respectively, at high spatial resolutions in vivo (<78 µm). In particular, rodent major airways with less restricted diffusion of 3He could only be visualized with the x‐centric method. Conclusions The x‐centric method significantly reduces diffusion weighting, allowing high spatial and temporal resolution HP 3He gas density‐weighted rodent lung MRI. Magn Reson Med 78:2334–2341, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 0.000 |
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".