Polymer gel dosimeters with enhanced sensitivity for use in x-ray CT polymer gel dosimetry
Bibliographic record
Abstract
A primary limitation of current x-ray CT polymer gel dosimetry is the low contrast, and hence poor dose resolution, of dose images produced by the system. The low contrast is largely due to the low-dose sensitivity of current formulations of polymer gel for x-ray CT imaging. This study reports on the investigation of new dosimeter formulations with improved dose sensitivity for x-ray CT polymer gel dosimetry. We incorporate an isopropanol co-solvent into an N-isopropylacrylamide-based gel formulation in order to increase the total monomer/crosslinker concentration (%T) within the formulation. It is shown that gels of high %T exhibit enhanced dose sensitivity and dose resolutions over traditional formulations. The gels are shown to be temporally stable and reproducible. A single formulation (16%T) is used to demonstrate the capabilities of the x-ray CT polymer gel dosimetry system in measuring known dose distributions. A 1 L gel volume is exposed to three separate irradiations: a single-field percent depth dose, a two-field 'cross' and a three-field 'test case'. The first two irradiations are used to generate a dose calibration curve by which images are calibrated. The calibrated images are compared with treatment planning predictions and it is shown that the x-ray CT polymer gel dosimetry system is capable of capturing spatial and dose information accurately. The proposed new gel formulation is shown to be sensitive, stable and to improve the dose resolution over current formulations so as to provide a feasible gel for clinical applications of x-ray CT polymer gel dosimetry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".