Small field dose delivery evaluations using cone beam optical computed tomography-based polymer gel dosimetry
Bibliographic record
Abstract
This paper explores the combination of cone beam optical computed tomography with an N-isopropylacrylamide (NIPAM)-based polymer gel dosimeter for three-dimensional dose imaging of small field deliveries. Initial investigations indicate that cone beam optical imaging of polymer gels is complicated by scattered stray light perturbation. This can lead to significant dosimetry failures in comparison to dose readout by magnetic resonance imaging (MRI). For example, only 60% of the voxels from an optical CT dose readout of a 1 l dosimeter passed a two-dimensional Low's gamma test (at a 3%, 3 mm criteria, relative to a treatment plan for a well-characterized pencil beam delivery). When the same dosimeter was probed by MRI, a 93% pass rate was observed. The optical dose measurement was improved after modifications to the dosimeter preparation, matching its performance with the imaging capabilities of the scanner. With the new dosimeter preparation, 99.7% of the optical CT voxels passed a Low's gamma test at the 3%, 3 mm criteria and 92.7% at a 2%, 2 mm criteria. The fitted interjar dose responses of a small sample set of modified dosimeters prepared (a) from the same gel batch and (b) from different gel batches prepared on the same day were found to be in agreement to within 3.6% and 3.8%, respectively, over the full dose range. Without drawing any statistical conclusions, this experiment gives a preliminary indication that intrabatch or interbatch NIPAM dosimeters prepared on the same day should be suitable for dose sensitivity calibration.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".