Comparison of video and CCD cameras in online portal imagers calibrated for dosimetry
Bibliographic record
Abstract
Video-based electronic portal imaging devices (EPIDs) are the most common type of portal imager found in clinical practice. In addition to providing a method to detect and correct patient set-up errors and record treatments, possibilities exist for the dosimetric use of video-based EPIDs. To determine if a particular system is suitable for dosimetry, the entire system and especially the camera should be thoroughly tested and calibrated. Comparisons were made of the performance of a Newvicon camera and a CCD camera in a video-based EPID. Tests were performed on the cameras to investigate linearity, noise, frame grabber effects, clamping error, and lens vignetting. Tests of the entire imaging system were performed to determine the behaviour of the veiling glare, the uniformity of the spatial sensitivity, and the EPID response with changes in field size and phantom thickness. The veiling glare was caused by several factors: the mirror, the cameras themselves, and the phantom scatter and beam hardening. The magnitude of the glare was spatially dependent. The EPID response was not spatially uniform, but increased by approximately 25% i the region farthest from the screen-mirror junction. The cause of this increase was determined to be the mirror. The dose-corrected EPID response with changes in phantom thickness was nearly flat for medium-sized fields, although it did increase slightly for large fields and decrease slightly for small fields. The overall performance of the CCD camera was found to be significantly better than that of the Newvicon camera. Central axis dosimetry would be straightforward to calibrate; however, the spatially dependent glare observed in the system would make it extremely difficult to deconvolve an EPID image to provide a two-dimensional dose map.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".