Contrast Enhancement for Portal Imaging in Nanoparticle-Enhanced Radiotherapy: A Monte Carlo Phantom Evaluation Using Flattening-Filter-Free Photon Beams
Bibliographic record
Abstract
Our team evaluated contrast enhancement for portal imaging using Monte Carlo simulation in nanoparticle-enhanced radiotherapy. Dependencies of percentage contrast enhancement on flattening-filter (FF) and flattening-filter-free (FFF) photon beams were determined by varying the nanoparticle material (gold, platinum, iodine, silver, iron oxide), nanoparticle concentration (3-40 mg/mL) and photon beam energy (6 and 10 MV). Phase-space files and energy spectra of the 6 MV FF, 6 MV FFF, 10 MV FF and 10 MV FFF photon beams were generated based on a Varian TrueBeam linear accelerator. We found that gold and platinum nanoparticles (NP) produced the highest contrast enhancement for portal imaging, compared to other NP with lower atomic numbers. The maximum percentage contrast enhancements for the gold and platinum NP were 18.9% and 18.5% with a concentration equal to 40 mg/mL. The contrast enhancement was also found to increase with the nanoparticle concentration. The maximum rate of increase of contrast enhancement for the gold NP was equal to 0.29%/mg/mL. Using the 6 MV photon beams, the maximum contrast enhancements for the gold NP were 79% (FF) and 78% (FFF) higher than those using the 10 MV beams. For the FFF beams, the maximum contrast enhancements for the gold NP were 53.6% (6 MV) and 53.8% (10 MV) higher than those using the FF beams. It is concluded that contrast enhancement for portal imaging can be increased when a higher atomic number of NP, higher nanoparticle concentration, lower photon beam energy and no flattening filter of photon beam are used in nanoparticle-enhanced radiotherapy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".