Polymer gel dosimetry using x-ray computed tomography: a feasibility study<sup>4</sup>
Bibliographic record
Abstract
A new three-dimensional dosimetry technique using x-ray computed tomography (CT) to analyse polymer gels is proposed. The CT imaging is sensitive to radiation-induced density changes that occur within irradiated polyacrylamide gel (PAG). In this preliminary study, a CT imaging protocol is developed to optimize CT images of PAG; the response of PAG CT number to dose (N(CT)-dose response) and the reproducibility of the response are investigated, and the use of CT to analyse PAG is compared with MRI. Experiments were conducted using two 1.5 l cylindrical PAG phantoms (3% acrylamide, 3% bis and 5% gelatin by weight), one irradiated with four intersecting 10 MV photon beams and the other with 10 sets of 6 MV parallel opposed circular radiosurgery fields. The final imaging protocol involves using optimum CT parameters (120 kVp and 200 mAs for our GE HiSpeed CT/i scanner), image averaging and background subtraction. The N(CT)-dose response is reproducible, linear up to 800-1000 cGy and is relatively insensitive to the gel temperature during imaging. The dose resolution is approximately 50 cGy for an image thickness of 10 mm. Despite the low dose resolution, preliminary results indicate that this CT technique provides accurate localization of high dose gradients such as those observed in stereotactic radiosurgery. Thus, given the availability and speed of CT scanners, the technique has the potential to be a valuable and practical 3D dose verification tool in radiation therapy.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".