SU-E-T-93: A CT Polymer Gel Dosimetry System for End-To-End Dosimetry
Bibliographic record
Abstract
Purpose: To design and test an x-ray CT polymer gel dosimetry system for 3D end-to-end dosimetry of brain and head and neck radiotherapy Methods: A head and neck phantom has been designed to undergo the entire RT process from immobilization and planning CT through to treatment. The phantom houses a 1L polymer gel in one of two locations: cranial and inferiorly in the neck region allowing for quality assurance of both brain and head and neck treatment processes. The gel dosimeter measures the delivered radiation dose in 3D, and following CT read-out, provides a dose record of the entire treatment process, i.e. “end-to-end” dosimetry. Enhanced dose-sensitivity N-isopropylacrylamide (NIPAM) based gels along with an optimized CT protocol were used to minimize uncertainty in image CT numbers (H). Tests of phantom set-up were performed to quantify image noise, uniformity and positioning reproducibility using locking bar (ideal conditions) and aquaplast mask (clinical conditions). The end-to-end dosimetry capability was tested by undertaking the full RT process (immobilization, planning CT, treatment planning, treatment set-up and delivery) and comparing delivered and planned doses for a simple “star-pattern” irradiation. Results: Removing the phantom head for gel read-out minimized noise and artifacts (63% noise reduction), produced uniform images and has minimal impact on phantom re-positioning (less than 0.5mm). Full phantom re-positioning reproducibility was also excellent: less than 0.9 mm for both locking bar and mask immobilization. Initial end-to-end dosimetry tests indicate accurate localization of treatment dose to within 1 mm. Conclusions: An x-ray CT polymer gel dosimetry system for performing 3D end-to-end dosimetry has been designed, tested and is demonstrated to provide accurate 3D localization of delivered radiation dose. Future work will assess clinical processes as undertaken by appropriate RT staff and look at other treatment sites.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".