Handheld electromagnet carrier for transfer of hyperpolarized carbon‐13 samples
Bibliographic record
Abstract
PURPOSE: Hyperpolarization of carbon-13 ((13) C) nuclei by dissolution dynamic nuclear polarization increases signal-to-noise ratio (SNR) by >10,000-fold for metabolic imaging, but care must be taken when transferring hyperpolarized (HP) samples from polarizer to MR scanner. Some (13) C substrates relax rapidly in low ambient magnetic fields. A handheld electromagnet carrier was designed and constructed to preserve polarization by maintaining a sufficient field during sample transfer. METHODS: The device was constructed with a solenoidal electromagnet, powered by a nonmagnetic battery, holding the HP sample during transfer. A specially designed switch automated deactivation of the field once transfer was complete. Phantom and rat experiments were performed to compare MR signal enhancement with or without the device for HP [(13) C]urea and [1-(13) C]pyruvate. RESULTS: The magnetic field generated by this device was tested to be >50 G over a 6-cm central section. In phantom and rat experiments, [(13) C]urea transported via the device showed SNR improvement by a factor of 1.8-1.9 over samples transferred through the background field. CONCLUSION: A device was designed and built to provide a suitably high yet safe magnetic field to preserve hyperpolarization during sample transfer. Comparative testing demonstrated SNR improvements of approximately two-fold for [(13) C]urea while maintaining SNR for [1-(13) C]pyruvate.
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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".