Assigning nonelastic nuclear interaction cross sections to Hounsfield units for Monte Carlo treatment planning of proton beams
Bibliographic record
Abstract
In high energy clinical proton beams nonelastic nuclear interactions contribute substantially to the total dose. It is therefore of importance to know these contributions quantitatively and to be able to scale them correctly as a function of Hounsfield units obtained from CT data. In this work, the second of these issues has been addressed. The importance of taking material-dependent nonelastic nuclear interactions into account has been investigated for Monte Carlo calculations. A scaling curve for nonelastic nuclear interactions as a function of Hounsfield unit has been established and compared with similar data for the stopping powers. Monte Carlo simulations using McPTRAN.MEDIA and MCNPX have been performed in homogeneous media and in inhomogeneous slab geometries. The results show that for skeletal tissues and for adipose tissue, the tissue to water nonelastic cross section ratios differ up to 10% compared to the tissue to water stopping power ratios. This results in errors of the order of 2-3% when both contributions to the total dose are scaled in the same way (with stopping power ratios). Monte Carlo simulations in slab geometries with tissue materials for 200 MeV protons show similar effects, but when both contributions are scaled correctly the errors are not larger than 0.5% in the situations investigated here.
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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 | 0.000 | 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".