SU‐FF‐T‐320: Measurement of Surface and Exit Dose in Megavoltage X‐Ray Beams Using Micro‐MOSFET Detectors
Bibliographic record
Abstract
Purpose: Knowledge of entrance and exit dose, specifically in breast cancers, is of significant clinical importance. New micro‐MOSFET (Thomson & Nielsen Electronics Ltd., Ottawa, Canada) detectors offer an efficient means to accomplish this task. In this study we investigate the use of MOSFETs to measure surface and exit dose in external photon beams. Method and Materials: Ratios of measurements at the surface and a depth of dmax in a solid water phantom were correlated with Monte Carlo (BEAM) generated percentage depth dose curves to determine the water‐equivalent thickness of the micro‐MOSFET detectors. This was done for 6 and 18 MV x‐rays in a 10×10 cm2 field, both normally and obliquely incident. Exit dose was measured similarly and equivalent thickness determined. Results: Correlation of the predicted depth dose and measured ratios indicates a water‐equivalent thickness of 0.8–1.0 mm for the micro‐MOSFET at the surface. All results indicate that the equivalent thickness is independent of angle of incidence and energy. The same detectors show an equivalent thickness that is approximately 0.4 mm and energy independent when measuring exit dose. We anticipate final results to include additional measurements at 10 MV and a field size of 40×40 cm2. Conclusions: This work indicates micro‐MOSFET detectors are a reliable (reproducible within 3%) detector of surface dose and exit dose as they exhibit a water‐equivalent thickness that is independent of energy and angle of incidence. We believe they offer a unique opportunity in their application to in vivo surface dose measurement.
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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 | 0.001 |
| 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) | 0.001 | 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".