The three-dimensional scintillation dosimetry method: test for a<sup>106</sup>Ru eye plaque applicator
Bibliographic record
Abstract
The need for fast, accurate and high resolution dosimetric quality assurance in radiation therapy has been outpacing the development of new and improved 2D and 3D dosimetry techniques. This paper summarizes the efforts to create a novel and potentially very fast, 3D dosimetry method based on the observation of scintillation light from an irradiated liquid scintillator volume serving simultaneously as a phantom material and as a dose detector medium. The method, named three-dimensional scintillation dosimetry (3DSD), uses visible light images of the liquid scintillator volume at multiple angles and applies a tomographic algorithm to a series of these images to reconstruct the scintillation light emission density in each voxel of the volume. It is based on the hypothesis that with careful design and data processing, one can achieve acceptable proportionality between the local light emission density and the locally absorbed dose. The method is applied to a Ru-106 eye plaque immersed in a 16.4 cm3 liquid scintillator volume and the reconstructed 3D dose map is compared along selected profiles and planes with radiochromic film and diode measurements. The comparison indicates that the 3DSD method agrees, within 25% for most points or within approximately 2 mm distance to agreement, with the relative radiochromic film and diode dose distributions in a small (approximately 4.5 mm high and approximately 12 mm diameter) volume in the unobstructed, high gradient dose region outside the edge of the plaque. For a comparison, the reproducibility of the radiochromic film results for our measurements ranges from 10 to 15% within this volume. At present, the 3DSD method is not accurate close to the edge of the plaque, and further than approximately 10 mm (<10% central axis depth dose) from the plaque surface. Improvement strategies, considered important to provide a more accurate quick check of the dose profiles in 3D for brachytherapy applicators, are discussed.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".