Development of optimised tissue-equivalent materials for proton therapy
Bibliographic record
Abstract
Abstract Objective . In proton therapy there is a need for proton optimised tissue-equivalent materials as existing phantom materials can produce large uncertainties in the determination of absorbed dose and range measurements. The aim of this work is to develop and characterise optimised tissue-equivalent materials for proton therapy. Approach . A mathematical model was developed to enable the formulation of epoxy-resin based tissue-equivalent materials that are optimised for all relevant interactions of protons with matter, as well as photon interactions, which play a role in the acquisition of CT numbers. This model developed formulations for vertebra bone- and skeletal muscle-equivalent plastic materials. The tissue equivalence of these new materials and commercial bone- and muscle-equivalent plastic materials were theoretical compared against biological tissue compositions. The new materials were manufactured and characterised by their mass density, relative stopping power (RSP) measurements, and CT scans to evaluate their tissue-equivalence. Main results . Results showed that existing tissue-equivalent materials can produce large uncertainties in proton therapy dosimetry. In particular commercial bone materials showed to have a relative difference up to 8% for range. On the contrary, the best optimised formulations were shown to mimic their target human tissues within 1%–2% for the mass density and RSP. Furthermore, their CT-predicted RSP agreed within 1%–2% of the experimental RSP, confirming their suitability as clinical phantom materials. Significance . We have developed a tool for the formulation of tissue-equivalent materials optimised for proton dosimetry. Our model has enabled the development of proton optimised tissue-equivalent materials which perform better than existing tissue-equivalent materials. These new materials will enable the advancement of clinical proton phantoms for accurate proton 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".