WE‐D‐I‐6B‐03: The Role of Secondary Photons in the Quantum Absorption Efficiency of Megavoltage X‐Ray Detectors: Is Dmax the Ideal X‐Ray Converter Thickness?
Bibliographic record
Abstract
Purpose: To quantify the impact of x‐ray converter thickness and determine the role of secondary photons on the quantum absorption efficiency of megavoltage x‐ray detectors (metal plate/phosphor screen) used in portal imaging and megavoltage CT. Method and Materials: The Electron Gamma Shower (EGSnrc) Monte Carlo code was used to simulate the coupled photon‐electron transport within a copper (Cu) metal plate / gadolinium oxysulphide phosphor screen detector. The DOSRZnrc user code was used to score the spectrum of x‐ray energy deposition within the phosphor layer of the detector. In the simulations, a wide range of metal plate thicknesses (0–60 mm), phosphor screen thicknesses (0.1–5 mm), and incident photon energies (1–10 MeV) were investigated. The quantum absorption efficiency (QAE) was calculated from each absorbed energy distribution (AED) simulation. Results: Plots of QAE versus copper metal plate thickness indicate: the maximum QAE does not occur at the depth of maximum dose (dmax), but rather for a thicker metal plate; the metal plate thickness corresponding to maximum QAE increases with phosphor thickness; the magnitude of the QAE increases with phosphor thickness; and the maximum QAE is independent of the incident photon energy. For example, for a 1 MeV incident photon energy and 1 mm phosphor thickness, a factor of two improvement in the QAE can be achieved using a 12 mm thick metal plate. Conclusion: Our results suggest that using thicker metal plate converters can increase the QAE of megavoltage x‐ray detectors. This improvement in QAE can potentially lead to reductions in patient dose for megavoltage imaging. Furthermore, in terms of maximizing the QAE, higher order Compton scattered and pair annihilation photons that originate in the metal plate play a more important role than primary electrons.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".