SU‐E‐T‐291: Sensitivity of a Simple 2D EPID in Vivo Dosimetry
Bibliographic record
Abstract
Purpose: As radiotherapy (RT) increases in complexity, so does motivation for in vivo dosimetry (IVD), which may detect errors such as: setup, beam shaping and dose delivered. We have recently developed an easy‐toimplement method for two‐dimensional IVD based on images taken with the electronic portal imaging device (EPID) in cine mode during treatment. The purpose of this work is to characterize its sensitivity to possible RT delivery errors. Methods: We introduced a series of modifications to a simple RT field (10×10, 100MU, 300RR, 20cm homogeneous phantom) to simulate errors. These modifications included multi‐leaf collimator (MLC) position, number of MUs, and collimator angle. We quantified the sensitivity to inhomogeneities by inserting variable amounts of solid lung and bone. Finally we delivered realistic fields to an anthropomorphic phantom to estimate sensitivity to gantry angle and setup errors. Results: Our EPIDIVD is sensitive to MLC positioning errors of 1mm and 3mm in the closed and open directions respectively, and to 3% MU variations. Sensitivity to collimator angle depends on field shape irregularity; in the case of a 10×10 field, we are sensitive to errors of 0.8°. The sensitivity to inhomogeneities is limited by the nature of MV imaging: approximately 1% signal change is noted when switching 5cm of water to equal amounts of bone or lung. This suggests that the EPID‐IVD is likely not sensitive to small setup or gantry angle errors, as confirmed by anthropomorphic tests. Conclusion: We have characterized a simple method of 2D dose reconstruction at isocenter depth inside the patient, which is sensitive to possible RT delivery errors. This method may be useful as a secondary safety check, to prevent large errors from being carried on to following fractions, and to record delivered dose. By using readily available hardware, it is easily implemented and may prove especially useful in centers with limited resources.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".