Technical note: development and validation of a Monte Carlo tool for analysis of patient-generated photon scatter
Bibliographic record
Abstract
Abstract Scattered radiation unavoidably generated in the patient will negatively impact both kilovoltage (KV) and megavoltage (MV) imaging applications. Recently, ‘hybrid’ methods (i.e. combining analytical and Monte Carlo (MC) techniques) are being investigated as a solution to accurately yet quickly calculate the scattered contribution for both KV and MV images. We have developed a customized MC simulation user code for investigating the individual components of patient-scattered photon fluence, which serves as a valuable tool in this area of research. The MC tool is based on the EGSnrc/DOSXYZnrc user code. The IAUSFL flag options associated with subroutine AUSGAB, combined with LATCH tracking, are used to classify the various interactions of particles with the media. Photons are grouped into six different categories: primary, 1st Compton scatter, 1st Rayleigh scatter, multiple scatter, bremsstrahlung, and positron annihilation. We take advantage of the geometric boundary check in DOSXYZnrc, to write exiting photon particle information to a phase-space file. The tool is validated using homogeneous and heterogeneous phantom configurations with monoenergetic and polyenergetic beams under parallel and divergent beam geometry, comparing MC-simulated exit primary fluence and singly-scattered fluence to corresponding analytical calculations. This MC tool has been validated to separately score the primary and scatter fluence components of the KV and MV imaging applications in the field of radiation therapy. The results are acceptable for the various configurations and beam energies tested here. Overall, the mean percentage differences are less than 0.2% and standard deviations less than 1.6%. This will be a critical test instrument for research in photon scatter applications and particularly for the development of hybrid methods, and is freely available from the authors for research purposes. 5
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".