WE-C-AUD C-04: Automated Pretreatment Verification of Portal Imager Positioning
Bibliographic record
Abstract
Purpose: The use of an EPID in the clinic typically requires manual translations of the device based on light fields. These manipulations potentially are time-consuming and prone to errors; accidental irradiations of the electronics components of EPIDs can shorten their lifetime and degrade the image quality. To eliminate these laborious manipulations in the treatment room, we have developed a custom application dedicated to the verification and correction of the portal imager position. Method and Materials: The application has been developed in Matlab and was compiled as a standalone application. Two versions were developed to accommodate the particularities of the two EPIDs available at our institution (aS500 (Varian) and iViewGT (Elekta)). Based on the treatment plans of the requested patient, the software loads the parameters required to simulate treatment fields. The graphical user interface shows the selected fields with the detector limits, so that the user can modify the fields requiring double exposition or cropping for imaging. Afterward, the user verifies the portal imager positioning and manually or automatically finds, if necessary, the proper imager translation. Results: At our institution, this application allows technologists to prepare EPID positioning while they are doing the final plan verification of a patient. Since the introduction of this application, the treatment time of plans having large or asymmetrical fields was reduced of at least one minute (of a 15 to 30 min time slots every day) and the risk of irradiating EPID's electronics was decreased or eliminated. Conclusion: This application streamlines the clinic workflow and saves time in the treatment room. In addition, because it has the potential to reduce accidental EPID's electronics irradiations, it can help to preserve their image quality in the long term.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.024 | 0.010 |
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".