Validation of an electron Monte Carlo dose calculation algorithm in the presence of heterogeneities using EGSnrc and radiochromic film measurements
Bibliographic record
Abstract
The purpose of this study is to validate Eclipse's electron Monte Carlo algorithm (eMC) in heterogeneous phantoms using radiochromic films and EGSnrc as a reference Monte Carlo algorithm. Four heterogeneous phantoms are used in this study. Radiochromic films are inserted in these phantoms, including in heterogeneous media, and the measured relative dose distributions are compared to eMC calculations. Phantoms A, B, and C contain 1D heterogeneities, built with layers of lung- (phantom A) and bone- (phantoms B and C) equivalent materials sandwiched in Plastic Water. Phantom D is a thorax anthropomorphic phantom with 2D lung heterogeneities. Electron beams of 6, 9, 12 and 18 MeV from a Varian Clinac 2100 are delivered to these phantoms with a 10 × 10 cm2 applicator. Monte Carlo simulations with an independent algorithm (EGSnrc) are also used as a reference tool for two purposes: (1) as a second validation of the eMC dose calculations, and (2) to calculate the stopping power ratio between radiochromic films and bone medium, when dose is measured inside the heterogeneity. Percent depth dose (PDD) film measurements and eMC calculations agree within 2% or 3 mm for phantom A, and within 3% or 3 mm for phantoms B and C for almost all beam energies. One exception is observed with phantom B and the 6 MeV, where measured PDDs and those calculated with eMC differ by up to 4 mm. Gamma analysis of the measured and calculated 2D dose distributions in phantom D agree with criteria of 3%, 3mm for 9, 12, and 18 MeV beams, and criteria of 5%, 3 mm for the 6 MeV beam. Dose calculations in heterogeneous media with eMC agree within 3% or 3 mm with radiochromic film measurements. Six (6) MeV beams are not modeled as accurately as other beam energies. The eMC algorithm is suitable for clinical dose calculations involving lung and bone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".