X-ray-Sensitive Doped CaF<sub>2</sub>-Based MRI Contrast Agents for Local Radiation Dose Measurement
Bibliographic record
Abstract
Ionizing radiation has become widely used in medicine, with application in diagnostic techniques, such as computed tomography (CT) and radiation therapy (RT), where X-rays are used to diagnose and treat tumors. The X-rays used in CT and, in particular, in RT can have harmful side effects; hence, an accurate determination of the delivered radiation dose is of utmost importance to minimize any damage to healthy tissues. For this, medical specialists mostly rely on theoretical predictions of the delivered dose or external measurements of the dose. To extend the practical use of ionizing radiation-based medical techniques, such as magnetic resonance imaging (MRI)-guided RT, a more precise measurement of the internal radiation dose internally is required. In this work, a novel approach is presented to measure dose in liquids for potential future in vivo applications. The strategy relies on MRI contrast agents (CAs) that provide a dose-sensitive signal. The demonstrated materials are (citrate-capped) CaF 2 nanoparticles (NPs) doped with Eu 3+ or Fe 2+ /Fe 3+ ions. Free electrons generated by ionizing radiation allow the reduction of Eu 3+, which produces a very small contrast in MRI, to Eu 2+, which induces a strong contrast. Oxidative species generated by high-energy X-rays can be measured indirectly using Fe 2+ because it oxidizes to Fe 3+, increasing the contrast in MRI. Notably, in the results, a strong increase in the proton relaxation rates is observed for the Eu 3+ -doped NPs at 40 kV. At 6 MV, a significant increase in proton relaxation rates is observed using CaF 2 NPs doped with Fe 2+ /Fe 3+ after irradiation. The presented concept shows great promise for use in the clinic to measure in vivo local ionizing radiation dose, as these CAs can be intravenously injected in a saline solution.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".