Sci‐Sat AM: Radiation Dosimetry and Practical Therapy Solutions ‐ 07: A mould room in a box – 3D scanning and printing technology in the radiotherapy clinic
Bibliographic record
Abstract
Purpose: We describe the process by which our centre is currently implementing 3D printing and scanning technology for treatment accessory fabrication. This technology can increase efficiency and accuracy of accessory design, production and placement during daily use. Methods: A low‐cost 3D printer and 3D optical scanner have been purchased and are being commissioned for clinical use. Commissioning includes assessing: the accuracy of the 3D scanner through comparison with high resolution CT images; the dosimetric characteristics of polylactic acid (PLA) for electron beams; the clinical utility of the technology, and; methods for quality assurance. Results: The agreement between meshes generated using the 3D scanner and CT data was within 2 millimeters for an anthropomorphic head phantom. In terms of electron beam attenuation, 1 centimetre of printed PLA was found equivalent to 1.17 cm of water. In proof‐of‐concept tests, several types of treatment accessories have been prototyped to date that will benefit from this technology. These include electron and photon bolus for areas with complex surface contours including the ear for electron treatments, the extremities for photon treatments and lead shielding for orthovoltage treatments. Imaging with CT and x‐ray showed minimal defects, which will have no significant clinical impact. Geometric fidelity and fit to volunteers and patients was found to be excellent. Conclusions: 3D Printing and scanning can increase efficiency in the clinic for treatments requiring custom accessories. Customized boluses and shielding had excellent fit and reduced uncertainty in positioning.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.120 | 0.045 |
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".