Dosimetric evaluation of Plastic Water Diagnostic Therapy
Bibliographic record
Abstract
High-precision radiotherapy planning and quality assurance require accurate dosimetric and geometric phantom measurements. Phantom design requires materials with mechanical strength and resilience, and dosimetric properties close to those of water over diagnostic and therapeutic ranges. Plastic Water Diagnostic Therapy (PWDT: CIRS, Norfolk, VA) is a phantom material designed for water equivalence in photon beams from 0.04 MeV to 100 MeV; the material has also good mechanical properties. The present article reports the results of computed tomography (CT) imaging and dosimetric studies of PWDT to evaluate the suitability of the material in CT and therapy energy ranges. We characterized the water equivalence of PWDT in a series of experiments in which the basic dosimetric properties of the material were determined for photon energies of 80 kVp, 100 kVp, 250 kVp, 4 MV, 6 MV, 10 MV, and 18 MV. Measured properties included the buildup and percentage depth dose curves for several field sizes, and relative dose factors as a function of field size. In addition, the PWDT phantom underwent CT imaging at beam qualities ranging from 80 kVp to 140 kVp to determine the water equivalence of the phantom in the diagnostic energy range. The dosimetric quantities measured with PWDT agreed within 1.5% of those determined in water and Solid Water (Gammex rmi, Middleton, WI). Computed tomography imaging of the phantom was found to generate Hounsfield numbers within 0.8% of those generated using water. The results suggest that PWDT material is suitable both for regular radiotherapy quality assurance measurements and for intensity-modulated radiation therapy (IMRT) verification work. Sample IMRT verification results are presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".