Measurement and modeling of the effect of support arm backscatter on dosimetry with a Varian EPID
Bibliographic record
Abstract
PURPOSE: Amorphous silicon EPIDs have been used for planar dose verification in IMRT treatments for many years. The support arm used to attach some types of EPIDs to linear accelerators can introduce inaccuracies to dosimetry measurements due to the presence of metallic parts in their structures. It is demonstrated that this uncertainty may be as large as approximately 6% of maximum image signal for large fields. In this study, a method has been described to quantify, model and correct for the effect of backscattered radiation from the EPID support arm (E-Arm type, Varian Medical Systems). METHODS: Measurements of a support arm backscatter kernel were made using several 1 x 1 cm2 6 MV pencil beam irradiations at a sample of positions over the sensitive area of the EPID in standard clinical setup and repeated with the EPID removed from the support arm but at the same positions. A curve-fit to the subtraction of EPID response obtained on and off the arm was used to define the backscatter kernel. The measured kernel was compared with a backscatter kernel obtained by Monte Carlo simulations with EGS/BEAM code. A backscatter dose prediction using the measured backscatter kernel was added to an existing EPID dose prediction model. The improvement in the agreement of the modified model predictions with EPID measurements for a number of open fields and IMRT beams were investigated by comparison to the original model results. RESULTS: Considering all functions tested to find the best functional fit to the data points, a broad Gaussian curve proved to be the optimum fit to the backscatter data. The best fit through the Monte Carlo simulated backscatter kernel was also found to be a Gaussian curve. The maximum decrease in normalized root mean squared deviation of the measured and modeled EPID image profiles for open fields was 13.7% for a 15 x 15 cm2 field with no decrease observed for a 3 x 3 cm2 (the smallest) field as it was not affected by the arm backscatter. Gamma evaluation (2%, 2 mm criteria) showed the improvement in agreement between the model and measurement results when the backscatter was incorporated. The average increase in Gamma pass rate was 2% for head and neck and 1.3% for prostate IMRT fields investigated in this study. CONCLUSIONS: The application of the backscatter kernel determined in this study improved the accuracy of dosimetry using a Varian EPID with E-arm for open fields of different sizes: Eight head and neck and seven prostate IMRT fields. Further improvement in the agreement between the model predictions and EPID measurements requires more sophisticated modeling of the backscatter.
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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) | 0.000 | 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".