Hyperpolarized MR Imaging: Neurologic Applications of Hyperpolarized Metabolism
Bibliographic record
Abstract
Hyperpolarization is the general term for a method of enhancing the spin-polarization difference of populations of nuclei in a magnetic field. No less than 5 distinct techniques (dynamic nuclear polarization [DNP]; parahydrogen-induced polarization-parahydrogen and synthesis allow dramatically enhanced nuclear alignment [PHIP-PASADENA]; xenon/helium polarization transfer; Brute Force; (1)H hyperpolarized water) are currently under exhaustive investigation as means of amplifying the intrinsically (a few parts per million) weak signal intensity used in conventional MR neuroimaging and spectroscopy. HD-MR imaging in vivo is a metabolic imaging tool causing much of the interest in HD-MR imaging. The most successful to date has been DNP, in which carbon-13 ((13)C) pyruvic acid has shown many. PHIP-PASADENA with (13)C succinate has shown HD-MR metabolism in vivo in tumor-bearing mice of several types, entering the Krebs-tricarboxylic acid cycle for ultrafast detection with (13)C MR imaging, MR spectroscopy, and chemical shift imaging. We will discuss 5 promising preclinical studies: (13)C succinate PHIP in brain tumor; (13)C ethylpyruvate DNP and (13)C acetate; DNP in rodent brain; (13)C succinate PHIP versus gadolinium imaging of stroke; and (1)H hyperpolarized imaging. Recent developments in clinical (13)C neurospectroscopy encourage us to overcome the remaining barriers to clinical HD-MR imaging.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".