Measured vs simulated portal images for low MU fields on three accelerator types: Possible consequences for 2D portal dosimetry
Bibliographic record
Abstract
PURPOSE: As external beam treatment plans become more dynamic and the dose to normal tissue is further constrained, treatments may consist of a larger number of beams, each delivering smaller doses (or monitor units, MU), in, e.g., volumetric modulated arc therapy (VMAT). Electronic portal imaging devices (EPID) may be used to verify external beam treatments on integrated fractions as well as in a more time dependent manner such as field by field. For treatment verification performed during a fraction (e.g., individual fields or VMAT control points), the lower limit of EPID measurement capability becomes important. The authors quantified the signal and timing accuracy of EPID images for low MU intensity modulated radiotherapy (IMRT) and conformal fields. METHODS: EPID images were collected from three different vendor's accelerators for low MU fields and compared to expected images. Simulations were performed to replicate the EPID acquisition pattern and to enhance the understanding of EPID readout schemes. RESULTS: Large discrepancies between observed and predicted images were noted due to an under-response to single low MU fields. It is shown that a variability of up to 37% can be observed for low MU fields in clinically used EPID acquisition modes and that the majority of this variability can be accounted for by the readout scheme, integration, and timing of EPID acquisitions. Simulations have confirmed the causes of the discrepancies. The occurrence and extent of the variation has been estimated for clinical settings. CONCLUSIONS: Incorrect absolute EPID signals collected for low MU fields in external beam treatments will negatively affect quantitative applications such as individual field based EPID dosimetry, typically appearing as an underdose, unless corrections to currently employed EPID readout schemes are made.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".