SU‐E‐T‐161: Assessment of Phantom Positioning Accuracy in IMRT Quality Assurance: Insert Design and Implementation
Bibliographic record
Abstract
Purpose: A multi‐centre program for intensity modulated radiation therapy (IMRT) quality assurance (QA) has previously been established. In this work, a phantom insert has been designed, in conjunction with an image analysis algorithm, to determine phantom positioning accuracy as part of program development. Methods: An insert was designed for the ArcCheck phantom that includes four BBs (located outside the sensitive area of the phantom) that are visualized using portal images (PI). A procedure has been developed to acquire PI at 8 gantry angles (2 field sizes per angle) for offline analysis. The image analysis method includes three steps: 1) cross correlation analysis is used to automatically detect the BB position with respect to the radiation isocentre in each PI; 2) 2D BB coordinates are reconstructed to 3D using the projected images; 3) the phantom positioning accuracy is obtained by a 3D rigid body transformation analysis of the BB coordinates. To evaluate the reliability of the image analysis method, the PI procedure was completed with the phantom aligned to the lasers (reference position) and with known shifts in phantom position (translational and rotational). Results: The measured change in phantom position, was compared to the known phantom shifts. Good agreement was found between the measured shift and known shift (within 0.2 mm or 0.2°). For the most complicated shift applied (5° yaw, 3 mm lateral translation and 4 mm vertical translation) the agreement was within 0.1° and 0.1 mm for rotations and translations respectively. Conclusion: A phantom insert has been designed to determine the accuracy of phantom positioning with respect to the radiation isocenter. Initial work illustrates that the analysis method can accurately characterize known positioning errors, which will help to isolate the impact of phantom misalignment in the context of IMRT QA. Supported in part by Cancer Care Ontario
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".