Monte Carlo simulation on the imaging contrast enhancement in nanoparticle-enhanced radiotherapy
Bibliographic record
Abstract
This study focused on the imaging in radiotherapy by finding the relationship between the imaging contrast ratio and appropriate gold, iodine, iron oxide, silver, and platinum nanoparticle concentrations; the relationship between the imaging contrast ratio and different beam energies for the different nanoparticle concentrations; the relationship between the contrast ratio and various beam energies for gold nanoparticles; and the relationship between the contrast ratio and different thicknesses of the incident layer of the phantom including variety of gold nanoparticles (GNPs) concentration. Monte Carlo simulation was used to model the gold, iodine, iron oxide, silver, and platinum nanoparticle concentration which were infused within a heterogeneous phantom (50 cm × 50 cm × 10.5 cm) choosing different concentrations (3, 7, 18, 30, and 40 mg), and beams (100, 120, 130, and 140 kVp) correspondingly that were delivered into the phantom. The results showed obvious connection between the high concentration and having a high imaging contrast ratio, low energy and a high contrast ratio, small thickness, and a high contrast ratio. The superior nanoparticle obtained was GNP, the better concentration was 40 mg, the better beam energy was 100 kVp, and the better thickness was 0.5 cm. It is concluded that our study successfully proved that medical imaging contrast could be improved by increasing the contrast ratio using GNP as the finest choice to accomplish this improvement considering a high concentration, low beam energy, and a small thickness.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".