Development of Quality Assurance Software for PRESAGE<sup>REU</sup>Gel Dosimetry
Bibliographic record
Abstract
The aim of this study is to develop a new software tool for 3D dose verification using PRESAGE REU Gel dosimeter.The tool included following functions: importing 3D doses from treatment planning systems (TPS), importing 3D optical density (OD), converting ODs to doses, 3D registration between two volumetric data by translational and rotational transformations, and evaluation with 3D gamma index.To acquire correlation between ODs and doses, CT images of a PRESAGE REU Gel with cylindrical shape was acquired, and a volumetric modulated arc therapy (VMAT) plan was designed to give radiation doses from 1 Gy to 6 Gy to six disk-shaped virtual targets along z-axis.After the VMAT plan was delivered to the targets, 3D OD data were reconstructed from 512 projection data from Vista TM optical CT scanner (Modus Medical Devices Inc, Canada) per every 2 hours after irradiation.A curve for converting ODs to doses was derived by comparing TPS dose profile to OD profile along z-axis, and the 3D OD data were converted to the absorbed doses using the curve.Supra-linearity was observed between doses and ODs, and the ODs were decayed about 60% per 24 hours depending on their magnitudes.Measured doses from the PRESAGE REU Gel were well agreed with the TPS doses at central region, but large under-doses were observed at peripheral region at the cylindrical geometry.Gamma passing rate for 3D doses was 70.36% under the gamma criteria of 3% of dose difference and 3 mm of distance to agreement.The low passing rate was resulted from the mismatching of the refractive index between the PRESAGE gel and oil bath in the optical CT scanner.In conclusion, the developed software was useful for 3D dose verification from PRESAGE gel dosimetry, but further improvement of the Gel dosimetry system were required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".