Sci‐Fri AM: YIS‐08: Prototype fan beam optical computed tomography scanner for three‐dimensional dose verification in radiation therapy
Bibliographic record
Abstract
A prototype, rapid, high precision fan beam optical computed tomography (OptCT) scanner for three‐dimensional polymer gel dosimetry of complex radiotherapy protocols has been developed. The scanner employs a sixty degree fan beam of 543 nm laser light to interrogate irradiated gel samples up to 19 cm in cross‐sectional diameter. Rapid data acquisition with minimal scattered light and minimal refraction and reflection artefacts is achieved with the unique radial design of the front and back apertures of the index matching medium tank, concentric arrangement of a CNC machined collimator and five photodiode detector arrays for light detection. Characteristics of the scanner include: an SNR of optical absorbance in light field projections of up to 100:1; SNR of optical absorbance in transmission projections of up to 92:1 for phantoms with absorbances ranging from 0.1 to 1.0 and a spatial resolution of 0.25 mm. OptCT dose maps extracted from normoxic NIPAM polymer gels irradiated with (a) a rectangular 3×3 cm2 field, (b) a 4‐field wedged treatment and (c) a C‐shaped IMRT plan are examined and compared to the Eclipse TPS. For these treatments, dose resolution is examined in steep and moderate dose gradient regions using dose difference and gamma function metrics. The effect of different OptCT reconstruction parameters on ring and streaking artefacts is also demonstrated.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".