Modeling photon output caused by backscattered radiation into the monitor chamber from collimator jaws using a Monte Carlo technique
Bibliographic record
Abstract
Dose per monitor unit in photon fields generated by clinical linear accelerators can be affected by the backscattered radiation into the monitor chamber from collimator jaws. Thus, it is necessary to account for the backscattered radiation in computing monitor unit setting for a treatment field. In this work, we investigated effects of the backscatter from collimator jaws based on Monte Carlo simulations of a clinical linear accelerator. The backscattered radiation scored within the monitor chamber was identified as originating either from the upper jaws (Y jaws), or from the lower jaws (X jaws). From the results of Monte Carlo simulations, ratios of the monitor-chamber-scored dose caused by the backscatter to the dose caused by the forward radiation, R(x,y), were modeled as functions of the individual X and Y jaw positions. The amount of the backscattered radiation for any field setting was then computed as a compound contribution from both the X and Y jaws. The dose ratios of R(x,y) were then used to calculate the change in photon output caused by the backscatter, Scb(x,y). Results of these calculations were compared with available measured data based on counting the electron pulses or charge from the electron target of an accelerator. Data from this study showed that the backscattered radiation contributes approximately 3% to the monitor-chamber-scored dose. A majority of the backscattered radiation comes from the upper jaws, which are located closer to the monitor chamber. The amount of the backscatter decreases approximately in a linear fashion with the jaw opening. This results in about a 2% increase of photon output from a 10 cm x 10 cm field to a 40 cm x 40 cm field. The off-axis location of the jaw opening does not have a significant effect on the magnitude of the backscatter. The backscatter effect is significant for monitor chambers using kapton windows, particularly for treatment fields using moving jaws. Applying the backscatter correction improves the accuracy of monitor-unit calculation using a model-based dose calculation algorithm such as the convolution method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".