Sci‐Sat AM: Radiation Dosimetry and Practical Therapy Solutions ‐ 08: A Low‐Cost Optical Scanner and Gantry for use with 3D Printing of Radiation Therapy Accessories
Bibliographic record
Abstract
Purpose: Three‐dimensional printing has been implemented at our institution to create customized treatment accessories including shielding and bolus. In order to effectively use 3D printing, the topography of the patient must first be acquired. To this end, we have evaluated a low‐cost structured‐light 3D scanner in order to assess the clinical viability of this technology. Methods: For ease of use, the scanner (3D Systems, Sense 3D Scanner) was mounted in a simple gantry that guided its motion and maintained an optimum distance between the scanner and the object. To characterise the spatial accuracy of the scanner, we used a geometric phantom and an anthropomorphic head phantom. The geometric phantom was machined from plastic and had overall dimensions of 24 cm by 15 cm and included a hemispherical and a tetrahedron protrusion roughly the dimensions of an average forehead and nose respectively. Meshes acquired by the optical scanner were compared to meshes generated from high‐resolution CT images. Results: Scans were acquired in under one minute. Most of the optical scans contained noticeable artefacts although in most instances these were considered minor. Using an algorithm that calculated distances between the two meshes, we found most of the optical scanner measurements agreed with those from CT to within about 1 mm for the geometric phantom and to within about 2 mm for the head phantom. Conclusion: In summary, we deemed this scanner to be clinically acceptable and it has been used to design treatment accessories for several skin cancer patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.081 | 0.016 |
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".