SU‐E‐T‐244: Designing Low‐Z Targets To Enhance Surface Dose: A Monte Carlo Simulation
Bibliographic record
Abstract
Purpose: Recent developments in The Varian Truebeam linac platform allows for the introduction of low‐Z targets into the beam line for the imaging purposes. We have proposed using a low‐Z target for radiation therapy purposes to enhance the surface dose during radiation treatment. The target arm of the Varian Truebeam accelerator consists of multiple targets with are linearly translated into the beam line. We have designed two Low‐Z targets made of carbon: 1) a step target consisting of three steps of 15%, 30% and 60% CSDA range for 2.5 MeV electrons Figure 1a; 2) and a ramp target, an incline plane 2cm long with thicknesses ranging from 0% to 60% CSDA range, Figure 1b. The purpose of this work will determine the spectral characteristics of these target designs and determine if they have practical clinical applications for enhancing surface dose. Methods: To calculate the spectral characteristics of these targets, a standard Monte Carlo model of a Varian Clinac accelerator was used. Simulations were performed with a carbon step target, and a carbon ramp target, located at the same position as the electron foil in the rotating carousel. Simulations were carried out using a 2.5 MeV electron beam. Results: The step target design produced spectral characteristics which were similar to spectral model using a single disk target of the same thickness. The ramp target provides a means to have positional variation of the spectral components of the beam, however, the electron component as 60% CSDA us much broader than the step target. Conclusion: The carbon step‐target provides a spectral distribution which is similar to a carbon disk of comparable thickness. The spectral distribution from the ramp‐target can be modified as a function of position to provide a wide range of low energy electrons for surface dose enhancement.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".