Measuring the Spin-Lattice Relaxation Magnetic Field Dependence of Hyperpolarized [1-<sup>13</sup>C]pyruvate
Bibliographic record
Abstract
The fundamental limit to in vivo imaging applications of hyperpolarized 13C-enriched compounds is their finite spin-lattice relaxation times. Various factors affect the relaxation rates, such as buffer composition, solution pH, temperature, and magnetic field. In this last regard, the spin-lattice relaxation time can be measured at clinical field strengths, but at lower fields, where these compounds are dispensed from the polarizer and transported to the MRI, the relaxation is even faster and difficult to measure. To have a better understanding of the amount of magnetization lost during transport, we used fast field-cycling relaxometry, with magnetic resonance detection of 13C nuclei at ~0.75 T, to measure the nuclear magnetic resonance dispersion of the spin-lattice relaxation time of hyperpolarized [1-13C]pyruvate. Dissolution dynamic nuclear polarization was used to produce hyperpolarized samples of pyruvate at a concentration of 80 mmol/L and physiological pH (~7.8). These solutions were rapidly transferred to a fast field-cycling relaxometer so that relaxation of the sample magnetization could be measured as a function of time using a calibrated small flip angle (3°-5°). To map the T1 dispersion of the C-1 of pyruvate, we recorded data for different relaxation fields ranging between 0.237 mT and 0.705 T. With this information, we determined an empirical equation to estimate the spin-lattice relaxation of the hyperpolarized substrate within the mentioned range of magnetic fields. These results can be used to predict the amount of magnetization lost during transport and to improve experimental designs to minimize signal loss.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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 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".