Sci-Thurs PM: Delivery-09: Improving megavoltage portal image contrast with low atomic number target materials
Bibliographic record
Abstract
PURPOSE: to investigate the impact of low atomic number (Z) targets and detector design on megavoltage (MV) portal image contrast. METHODS AND MATERIALS: two experimental beams were generated by replacing flattening filtration of a 2100EX linac with beryllium (Be) and aluminum (Al) targets and using the linac in 6 MeV electron mode. A standard MV contrast phantom was used to quantify planar image contrast for the standard 6MV and the 6MeVAl beam, incident on an amorphous silicon (a-Si) detector. Contrast versus separation for 6MV and 6MeV/Al was quantified using a 1 cm bone/solid water slab within increasing thicknesses of solid water. The Monte Carlo BEAMnrc/DOXYZnrc package was used to model beam generation and detection. The beam/detector model was validated with comparison to measured open field profiles. RESULTS: of the photon population is below 60keV for the 4MeV/Be beam. Planar contrast is increased significantly over that of 6MV using low-Z targets, showing additional improvement with removal of the detector's copper build-up layer. Contrast decreases with increasing separation more rapidly for 6MeV/Al than for 6MV; however contrast for the former is superior over the full range of separation examined. CONCLUSIONS: Use of low-Z linac target materials improves MV image contrast. An additional advantage is realized by removing the copper layer from the a-Si detector. CONFLICT OF INTEREST: This research has been sponsored by Varian Medical Systems, Incorporated.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".