Spying on parahydrogen-induced polarization transfer using a half-tesla benchtop MRI and hyperpolarized imaging enabled by automation
Bibliographic record
Abstract
Abstract Nuclear spin hyperpolarization is a quantum effect that enhances the nuclear magnetic resonance signal by several orders of magnitude and has enabled real-time metabolic imaging in humans. However, the translation of hyperpolarization technology into routine use in laboratories and medical centers is hampered by the lack of portable, cost-effective polarizers that are not commercially available. Here, we present a portable, automated polarizer based on parahydrogen-induced hyperpolarization (PHIP) at an intermediate magnetic field of 0.5 T (achieved by permanent magnets). With a footprint of 1 m 2 , we demonstrate semi-continuous, fully automated 1 H hyperpolarization of ethyl acetate- d6 and ethyl pyruvate- d6 to P = 14.4% and 16.2%, respectively, and a 13 C polarization of 1- 13 C-ethyl pyruvate- d6 of P = 7%. The duty cycle for preparing a dose is no more than 1 min. To reveal the full potential of 1 H hyperpolarization in an inhomogeneous magnetic field, we convert the anti-phase PHIP signals into in-phase peaks, thereby increasing the SNR by a factor of 5. Using a spin-echo approach allowed us to observe the evolution of spin order distribution in real time while conserving the expensive reagents for reaction monitoring, imaging and potential in vivo usage. This compact polarizer will allow us to pursue the translation of hyperpolarized MRI towards in vivo applications further.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".