Sci-Thur PM: YIS - 08: The effect of copper conversion plates on low-Z target image quality
Bibliographic record
Abstract
Current generation electronic portal imaging devices (EPID) contain a 1.0 mm copper conversion plate to increase detection efficiency of a therapeutic megavoltage spectrum. When using these EPIDs for low-Z target imaging, the conversion plate largely attenuates the large populations of diagnostic energy photons, thereby decreasing the benefits of low-Z target imaging. In this work we measure directly the effect the variation in thickness of a copper conversion plate has on image quality in planar and cone beam computed tomography imaging. Monte Carlo modeling was used to quantify changes to the diagnostic spectrum and detector response for low-Z target beams generated with 2.35 and 7.00 MeV electrons incident on a carbon target. Planar contrast-to-noise ratio (CNR) measurements were made as a function of copper thickness. Cone beam computed tomography (CBCT) image CNR measurements were made as a function of dose both with and without the copper plate present in the EPID. The presence of copper in the EPID decreased the diagnostic photon population by up to 20% and suppressed the peak detector response at 60 kV by a factor of 6.4. Planar CNR was increased by a factor ranging from 1.4 to 4.0 with no copper present compared to 1.0 mm thickness. Increases in CBCT image CNR ranged from a factor of 1.3 to 2.1 with the copper plate removed. As a result of this we suggest that the copper conversion plate be removed from the EPID when used for low-Z target planar or CBCT imaging.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".