Hyperpolarized <sup>129</sup>Xe multi‐slice imaging of the human brain using a 3D gradient echo pulse sequence
Bibliographic record
Abstract
Purpose To demonstrate the possibility of performing multi‐slice in‐vivo human brain MRI using hyperpolarized (HP) xenon‐129 ( 129 Xe) in two different orientations and to calculate the signal‐to‐noise ratio (SNR). Methods Two healthy female participants were imaged during a single breath‐hold of HP 129 Xe using a Philips Achieva 3.0T MRI scanner (Philips, Andover, MA). Each HP 129 Xe multi‐slice brain image was acquired during separate HP 129 Xe breath‐holds using 3D gradient echo (GRE) imaging. The acquisition started 10 s after the inhalation of 1 L of HP 129 Xe. Overall, four sagittal and three axial images were acquired (seven imaging sessions per participant). The SNR was calculated for each slice in both orientations. Results The first ever HP 129 Xe multi‐slice images of the brain were acquired in axial and sagittal orientations. The HP 129 Xe signal distribution correlated well with the gray matter distribution. The highest SNR values were close in the axial and sagittal orientations (19.46 ± 3.25 and 18.76 ± 4.94, respectively). Additionally, anatomical features, such as the ventricles, were observed in both orientations. Conclusion The possibility of using multi‐slice HP 129 Xe human brain magnetic resonance imaging was demonstrated for the first time. HP 129 Xe multi‐slice MRI can be implemented for brain imaging to improve current diagnostic methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".