Surface and near-surface dose measurements at beam entry and exit in a 1.5 T MR-Linac using optically stimulated luminescence dosimeters
Bibliographic record
Abstract
Abstract The objective of this study is to measure surface and near-surface dose at entry and exit surfaces in a 1.5 T MR-Linac (Elekta AB, Stockholm, Sweden) using optically stimulated luminescence dosimeters (OSLDs). OSLDs were expected to be useful for measuring surface dose in a strong magnetic field because they can be taped to undersides to measure exit dose, and their dose response have been shown to be reasonably insensitive to variations in beam angle, beam energy, and magnetic fields. The surface and near-surface dose at the entry and exit of a 20 cm thick solid water phantom was measured with OSLDs for 5 × 5, 10 × 10, and 22 × 22 cm 2 field sizes. The solid water phantom was elevated off the couch top to produce an air gap of 3.7 cm so as to observe the electron return effect (ERE) near the beam exit surface. Measurement depths ranged from surface to 15 mm deep from entry and exit surfaces. The phantom dose distribution was also computed in the Monaco (Elekta AB, Stockholm, Sweden) Monte Carlo treatment planning system (TPS). For the 5 × 5, 10 × 10, and 22 × 22 cm 2 field sizes the surface dose at depth 0 mm was extrapolated from OSLD measurements to be 10.9%, 12.0%, and 13.5%. The surface entry dose was found to be far less field size-dependent compared to a conventional linac, likely due to a lack of electronic contamination due to the strong magnetic field perpendicular to the beam. The ERE effect was observed in the measurements near the exit surface of the phantom, and was in close agreement with the TPS calculation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".