Sci—Wed PM: Delivery—10: Optical CT‐based Gel Dosimetry in Image Guided Adaptive Radiation Therapy
Bibliographic record
Abstract
Three dimensional gel dosimetry has become more clinically practical with the development of normoxic and less toxic polymer gels and of new accessible imaging techniques for dose readout. In this paper we describe the application of NIPAM polymer and Fricke xylenol (FXG) gels to image‐guided adaptive radiation therapy (IGART). The gel dosimetry was performed with a commercial optical CT imager. The first investigation was the validation of cone‐beam CT (CBCT) based patient localization and repositioning being implemented into our clinic. Gel dosimeters inserted in a phantom mimicking prostate cancer treatment, were processed and irradiated with and without required repositioning, and the dose delivery compared to treatment plans. In the second study, FXG dosimetry was used to determine the dose reduction from a CBCT upgrade on Varian linacs. In both experiments 3D dose data sets were obtained. The IGART repositioning gel experiments clearly showed when the process was followed as intended or when it failed. Without repositioning there were large volumes of disagreement between planned and measured dose distributions, with repositioning a 3D gamma comparison gave good agreement with > 95% of the voxels in agreement. The gel dosimetry of the changes with the upgrade to OBI Advanced confirmed a dose reduction of ∼90%. These results indicate that gel dosimetry provides features for IGART validation not available with conventional dosimeters. In particular, since a gel dosimetry phantom can be put through an IGART process as a patient, the whole process can be tested and validated in a regular quality control program.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.079 | 0.021 |
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".