Assessing the accuracy of electronic portal imaging device (EPID)‐based dosimetry: II. Evaluation of a dosimetric uncertainty budget and development of a new film‐in‐EPID absorbed dose calibration methodology
Bibliographic record
Abstract
PURPOSE: The aim of this study is to reduce the uncertainty associated with determining dose-to-water using an amorphous silicon electronic portal imaging detector (EPID) under reference conditions by developing a direct calibration formalism based on radiochromic film measurements made within the EPID panel and detailed Monte Carlo simulations. To our knowledge, this is the first EPID-based dosimetry study reporting an uncertain budget METHODS: Pixel sensitivity and relative off-axis response were mapped by simultaneously irradiating film contained within the imager panel and acquiring an EPID image set. The detector panel was disassembled for the purpose of modeling the EPID in detail using the EGSnrc DOSXYZnrc usercode, which was in turn used to calculate dose-to-film in the EPID and dose-to-water in water conversion factors RESULTS: A direct comparison of the two correction methodologies investigated in this work, the previously established empirical method and the proposed simultaneous measurement approach involving in-EPID film dosimetry, produced an agreement with an RMS deviation of 1.4% overall. A combined standard relative uncertainty of 3.3% (k = 1) was estimated for the determination of absorbed dose to water at the position of the EPID using the proposed calibration methodology CONCLUSIONS: This work describes a direct method of calibrating EPID response in terms of absorbed dose to water requiring fewer measurements than other empirical approaches, and without 2D spatial interpolation of correction factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".