The effects of an initial depolarization pulse on dissolved phase hyperpolarized <sup>129</sup>Xe brain MRI
Bibliographic record
Abstract
Purpose To evaluate the effect of an initial 90° depolarization RF pulse on the dissolved‐phase hyperpolarized (HP) xenon‐129 (129Xe) brain imaging and to compare the SNR variability of HP 129Xe images acquired without an initial depolarization RF pulse to those following the initial depolarization pulse. Methods Five cognitive normal healthy volunteers were imaged using a Philips Achieva 3.0T MRI scanner during a single breath‐hold following inhalation of 1 L of HP 129Xe. Each participant underwent six HP 129Xe scans. Three scans were performed using conventional single‐slice projection HP 129Xe brain imaging, and the other three scans were performed using the HP 129Xe time‐of‐flight imaging with an initial rectangular depolarization pulse. Results Although the utilization of an initial depolarization results in the reduction of the mean image SNR, the presence of an initial depolarization RF pulse reduces the SNR variability of the HP 129Xe brain image by a factor of 2.26. The highest SNR variability was observed from the posterior brain region, where the anterior region possessed the lower level of signal variability. Conclusion An initial 90° depolarization RF pulse, applied prior to the HP 129Xe image acquisition, reduced the HP 129Xe signal variability more than two times between the different breath‐hold images.
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 | 0.003 |
| 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 | 0.000 | 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 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".