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 (129Xe) 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 129Xe using a Philips Achieva 3.0T MRI scanner (Philips, Andover, MA). Each HP 129Xe multi‐slice brain image was acquired during separate HP 129Xe breath‐holds using 3D gradient echo (GRE) imaging. The acquisition started 10 s after the inhalation of 1 L of HP 129Xe. 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 129Xe multi‐slice images of the brain were acquired in axial and sagittal orientations. The HP 129Xe 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 129Xe human brain magnetic resonance imaging was demonstrated for the first time. HP 129Xe multi‐slice MRI can be implemented for brain imaging to improve current diagnostic methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".