SU-G-JeP2-09: Minimal Skin Dose Increase in Longitudinal Rotating Biplanar Linac-MR Systems: Examination of Radiation Energy and Flattening Filter Design
Bibliographic record
Abstract
Purpose: To quantify increase in entrance skin-dose due to magnetic fields of the Alberta longitudinal linac-MR by examining the effect of radiation energy and flattening filter, using Monte Carlo calculations and accurate 3-D models of the magnetic field. Methods: The 3-D magnetic fields generated by the bi-planar Linac-MR are calculated with FEM using Opera-3D. BEAMnrc simulates the particle phase-space in the presence of the rapidly decaying fringe field of 0.5T MRI assembled with a Varian 600C linac with an isocentre distance of 130 cm for 6 MV and 10 MV beams. Skin doses are calculated at an average depth of 70 µm using DOSXYZnrc with varying SSDs and field sizes. Furthermore, flattening filters are reshaped to compensate for the significant drop in dose rate due to increased SAD of 130 cm and skin-doses are evaluated. Results: The confinement effect of the MRI fringe field on the contaminant electrons is minimal. For SSDs of 100 – 120 cm the increase in skin dose is ∼6% – 19% and ∼1% – 9% for the 6 and 10 MV beams, respectively. For 6MV, skin dose increases from ∼10.5% to 1.5%. for field-size increases of 5×5 cm2 to 20×20 cm2. For 10 MV, skin dose increases by ∼6% for a 5×5 cm2 field, and decreases by ∼1.5% for a 20×20 cm2 field. The reshaped flattening filter increases the dose rate from 355 MU/min to 529 MU/min (6 MV) or 604 MU/min (10 MV), while the skin-dose increases by only an additional ∼2.6% (all percent increases in skin dose are relative to Dmax). Conclusion: There is minimal increase in the entrance skin dose and minimal/no decrease in the dose rate of the Alberta longitudinal linac-MR system. There is even lower skin-dose increase at 10 MV. Funding: Alberta Innovates - Health Solutions (AIHS) Conflict of Interest: Fallone is a co-founder and CEO of MagnetTx Oncology Solutions (under discussions to license Alberta bi-planar linac MR for commercialization)
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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 | 0.001 |
| 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) | 0.002 | 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".