NaDyF<sub>4</sub>Nanoparticles as T<sub>2</sub>Contrast Agents for Ultrahigh Field Magnetic Resonance Imaging
Bibliographic record
Abstract
A major limitation of the commonly used clinical MRI contrast agents (CAs) suitable at lower magnetic field strengths (<3.0 T) is their inefficiency at higher fields (>7 T), where next-generation MRI scanners are going. We present dysprosium nanoparticles (β-NaDyF4 NPs) as T2 CAs suitable at ultrahigh fields (9.4 T). These NPs effectively enhance T2 contrast at 9.4 T, which is 10-fold higher than the clinically used T2 CA (Resovist). Evaluation of the relaxivities at 3 and 9.4 T show that the T2 contrast enhances with an increase in NP size and field strength. Specifically, the transverse relaxivity (r2) values at 9.4 T were ∼64 times higher per NP (20.3 nm) and ∼6 times higher per Dy(3+) ion compared to that at 3 T, which is attributed to the Curie spin relaxation mechanism. These results and confirming phantom MR images demonstrate their effectiveness as T2 CAs in ultrahigh field MRIs.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".