AAPM TG report 330: EPID‐based quality assurance of linear accelerators
Bibliographic record
Abstract
Abstract Electronic portal imaging devices (EPIDs) have been in widespread clinical use for almost two decades due to favorable measurement characteristics such as fast image acquisition, high sensitivity and resolution, digital data format, long‐term stability, linear dose response, and large detection area. On‐board EPIDs are widely used as a quality assurance (QA) tool to monitor nearly every aspect of linear accelerator (linac) performance. In both the commercial QA equipment market and in clinical practice, there is a trend toward replacing conventional QA approaches with fully automated, EPID‐based QA tools. Despite widespread use, EPIDs are complex devices, and there has yet to be consensus or formal recommendations published by the AAPM for their use in routine linac QA. Task Group 330 (TG‐330) was, therefore, formed to provide guidelines and recommendations for physicists on the safe and appropriate use of EPIDs for linac QA. In particular, the report: (a) provides a comprehensive review of the characteristics and limitations of EPIDs as time‐resolved measurement devices and dosimeters; (b) summarizes the application of EPIDs for linac QA; (c) provides recommendations on efficient and effective implementations of EPID‐based QA techniques; (d) describes risks associated with the use of EPIDs for linac QA and provides examples of how risk analysis can be used to ensure the safe use of EPIDs for linac QA. Many of the guidelines in this report are drawn from the literature that is included in the references and from the collective experience of the task group members. This report does not provide recommendations on the implementation of EPID‐based patient‐specific IMRT or VMAT QA, which is the topic of AAPM's report from AAPM TG‐307.
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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 | 0.037 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.022 |
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".