Imaging dose of cone-beam computed tomography in nanoparticle-enhanced image-guided radiotherapy: A Monte Carlo phantom study
Bibliographic record
Abstract
Using kilovoltage cone-beam computed tomography (kV-CBCT) and heavy-atom radiosensitizers in image-guided radiotherapy (IGRT) can provide numerous benefits, such as image contrast enhancement in radiation dose delivery. However, the increased use of kV-CBCT for daily imaging procedures may inevitably deposit certain amount of radiation dose to the patient, especially when nanoparticles used as radiosensitizers are involved. In this study, we use Monte Carlo simulation to evaluate the imaging dose escalation due to nanoparticle addition with varying nanoparticle material, nanoparticle concentration and photon beam energy. A phantom was used to determine the relationships between the imaging dose enhancement ratios (IDERs) and different concentrations (3–40 mg/ml) of gold (Au), platinum (Pt), iodine (I), silver (Ag) and iron oxide (Fe2O3) nanoparticles, under the delivery of 120–140 kVp photon beams from the CBCT. It is found that gold and platinum nanoparticles of 40 mg/ml concentration had the highest IDER (∼1.6) under the 120 kVp photon beam. This nanoparticle addition resulted in a 0.63% increase of imaging dose based on a typical dose prescription of 200 cGy per fraction in radiotherapy, and is within the standard uncertainty of ±5% in radiation dose delivery. This study proves that the incorporation of higher concentration nanoparticles under lower photon beam energy could increase the imaging dose. The results from this study can enable us to understand more about the incorporation of heavy-atom nanoparticles in IGRT systems.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".