Electronic portal imaging with an avalanche‐multiplication‐based video camera
Bibliographic record
Abstract
The aim of this study is to investigate the degree to which the imaging quality of an existing (video-based) electronic portal imaging device (EPID) system may be improved by using an avalanche-multiplication-based video camera (called the avalanche-gain method). Due to avalanche multiplication in the target of the video camera tube, the new camera can be made up to several hundred times more sensitive than a camera using a conventional video (e.g., Saticon) tube. As a result, the camera noise which limits the performance of current video-based EPIDs should be overwhelmed and made negligible. The detective quantum efficiency (DQE) of an EPID using the avalanche-gain method has been measured with 6 MV and 18 MV beams obtained using a linear accelerator. It is shown that the camera noise is indeed much smaller than quantum noise and that the DQE of the system is significantly increased compared to conventional video-based EPIDs. Variation of DQE of the avalanche-gain video portal system with a change of demagnification was also investigated. It has been shown that the improvement of optical coupling has less effect in this system than that in a conventional video-based EPID system.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".