Measurement of build-up region dose with optical cone-beam computed tomography scanner
Bibliographic record
Abstract
Abstract Measurement of the dose gradients from the entrance surface to depth is a standard task for characterizing an ionizing radiation beam. Most gel dosimeters provide spurious results near an air interface, limiting their value for this geometry. In this study, a 3D dosimeter system consisting of a low-diffusion, radiochromic hydrogel cast in a custom polyethylene terephthalate (PETE) vessel and imaged with a modified commercial optical cone-beam computed tomography (CBCT) scanner was employed. The cylindrical vessel wall and flat ends were constructed from a 0.025 cm thick PETE sheet. The optical CBCT scanner was modified to place the entire vessel in the centre of the field of view or to have the vessel base at the optical axis. Pre-irradiation and post-irradiation scans were acquired with the sample mounted in the standard and elevated positions. The sample was irradiated with a 2x2 cm square, 6 MV x-ray beam. Normalized attenuation coefficients from the central quarter of the reconstructed beam images were compared to a Monte Carlo depth dose calculation. Placing the vessel base at the optical axis allowed accurate dose measurements to within 0.2 cm of the entrance face and for the standard position to within 0.4 cm. These measurements validated the Monte Carlo calculation and provide an alternative to parallel plate ion chambers for dose measurement in the build-up region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".