Minimal skin dose increase in longitudinal rotating biplanar linac-MR systems: examination of radiation energy and flattening filter design
Post-publication record
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Bibliographic record
Abstract
The magnetic fields of linac-MR systems modify the path of contaminant electrons in photon beams, which alters patient entrance skin dose. Also, the increased SSD of linac-MR systems reduces the maximum achievable dose rate. To accurately quantify the changes in entrance skin dose, the authors use EGSnrc Monte Carlo calculations that incorporate 3D magnetic field of the Alberta 0.5 T longitudinal linac-MR system. The Varian 600C linac head geometry assembled on the MRI components is used in the BEAMnrc simulations for 6 MV and 10 MV beam models and skin doses are calculated at an average depth of 70 μm using DOSXYZnrc. 3D modeling shows that magnetic fringe fields decay rapidly and are small at the linac head. SSDs between 100 and 120 cm result in skin-dose increases of between ~6%-19% and ~1%-9% for the 6 and 10 MV beams, respectively. For 6 MV, skin dose increases from ~10.5% to ~1.5% for field-size increases of 5 × 5 cm(2) to 20 × 20 cm(2). For 10 MV, skin dose increases by ~6% for a 5 × 5 cm(2) field, and decreases by ~1.5% for a 20 × 20 cm(2) field. Furthermore, the proposed reshaped flattening filter increases the dose rate from the current 355 MU min(-1) to 529 MU min(-1) (6 MV) or 604 MU min(-1) (10 MV), while the skin-dose increases by only an additional ~2.6% (all percent increases in skin dose are relative to D max). This study suggests that there is minimal increase in the entrance skin dose and minimal/no decrease in the dose rate of the Alberta longitudinal linac-MR system. The even lower skin dose increase at 10 MV offers further advantages in future designs of linac-MR prototypes.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".