Cone-beam optical computed tomography for gel dosimetry II: imaging protocols
Bibliographic record
Abstract
This work develops imaging protocols for improved dose readout of a Fricke-xylenol orange-gelatin (FXG) gel-filled 1 L polyethylene terephthalate (PETE) jar dosimeter using a commercial Vista(TM) cone-beam optical computed tomography (CT) scanner from Modus Medical Devices Inc. (London, ON, Canada). To ensure good management of light source-detector stability, it was determined that (a) a minimum of 2 h warm-up time is necessary prior to dosimeter scanning, (b) the light source should be kept on until the completion of the last data scan except for the minimum amount of time required to acquire dark field images, and (c) the optional Vista software projection image normalization routine should be used in image reconstruction. The institution of dosimeter scan time and temperature control was strongly indicated from the experiments. A standard post-irradiation wait time of 30 min measured to within ±30 s was established to minimize the measurement uncertainties due to dosimeter development and diffusion. To alleviate thermochromic behavior leading to inaccurate dose readout, holding bath warm up and pre-scan temperature adjustment procedures were developed to control dosimeter temperature to within ±0.2 °C. The possibility of stray light minimizing protocols was also investigated and deemed to be unnecessary. The largest significant sources of stray light in the system were identified as being due to angled scatter from the dosimeter gelatin matrix and refraction from the jar wall interfaces. It was concluded that these phenomena would be better addressed through dosimeter modification and an inter-jar dose-to-attenuation calibration methodology, rather than by setting additional imaging protocols.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".