Magnetic field effects on the energy deposition spectra of MV photon radiation
Bibliographic record
Abstract
Several groups worldwide have proposed various concepts for improving megavoltage (MV) radiotherapy that involve irradiating patients in the presence of a magnetic field-either for image guidance in the case of hybrid radiotherapy-MRI machines or for purposes of introducing tighter control over dose distributions. The presence of a magnetic field alters the trajectory of charged particles between interactions with the medium and thus has the potential to alter energy deposition patterns within a sub-cellular target volume. In this work, we use the MC radiation transport code PENELOPE with appropriate algorithms invoked to incorporate magnetic field deflections to investigate electron energy fluence in the presence of a uniform magnetic field and the energy deposition spectra within a 10 microm water sphere as a function of magnetic field strength. The simulations suggest only very minor changes to the electron fluence even for extremely strong magnetic fields. Further, calculations of the dose-averaged lineal energy indicate that a magnetic field strength of at least 70 T is required before beam quality will change by more than 2%.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".