Sci‐Fri PM: Planning‐07: A low diffusion radiochromic gel dosimeter for three‐dimensional radiation dosimetry
Bibliographic record
Abstract
Purpose: To develop a low diffusion radiochromic leuco crystal violet (LCV) hydrogel utilizing micelles, for three‐dimensional (3‐D) radiation dosimetry. Method and Materials: Concentrations of LCV dye, Triton X‐100 and trichloroacetic acid were varied to determine the optimal gel sensitivity for optical computed tomography (CT). Using a laser optical CT scanner at λ = 594 nm, diffusion rate measurements were performed on half‐irradiated (6 MV x‐rays) cuvette gel samples made with and without surfactant, respectively. A cylindrical 1 L gel volume was irradiated with a 12 MeV electron beam (Varian Clinac 2100C) to a dose of 30 Gy and scanned with cone‐ beam optical CT at λ ∼ 590 nm (Vista™, Modus Medical Devices Inc.). Results: The most radiation sensitive gel formulation was found to be: 1 mM LCV, 4 mM Triton X‐100, 30 mM trichloroacetic acid and 4% gelatin. The diffusion rates of a LCV gel without and with surfactant present were about 2 and 20 times lower than the Fricke xylenol‐orange gel system, respectively. Comparison of the central axis gel attenuation coefficients normalized at depth of maximum dose (dmax) with TG21‐corrected ion chamber data, were in agreement, thus, indicating energy and dose‐rate independence. Conclusion: Radiochromic LCV micelle gels show minimal diffusion effects and a dose response that is linear, energy and dose‐rate independent. Optical CT scanned LCV micelle gels are a promising system for 3‐D dose verification. Conflict of Interest: Two of the authors (JB, KJ) have a licensing agreement with Modus Medical Devices Inc. concerning the commercialization of Vista™.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".