Poster ‐ 38: On the physical and dosimetric properties of 3D printed electron bolus fabricated using polylactic acid
Bibliographic record
Abstract
Purpose: 3D printing technology could simplify and improve electron bolus fabrication. The purpose of this study was to characterize the density, dimensional accuracy, uniformity, and attenuation of PLA boluses fabricated with a low‐cost 3D printer. Methods: Several solid square slabs were printed with specific requested dimensions and 100% infill using different fill patterns. These pieces were imaged using an x‐ray flat panel imager in order to check for uniformity of the prints. Percentage depth doses (PDDs) were measured downstream of the slabs in solid water using a parallel plate chamber and compared to measurements in water in order to characterize attenuation. The dimensions of the PLA slabs were measured using digital calipers. The slabs were also weighed to find their density. Results: The fill pattern used to create boluses can affect the attenuation of the bolus. Fill patterns should be chosen carefully and quality assurance should be done for each printed piece. PLA causes the electron PDD to shift towards shallower depths, compared to water, by 1.7 mm for each centimeter of PLA. Agreement with design dimensions was within 1 mm in the plane of the printer bed, and within 1/3 of a millimeter (roughly the thickness of a single layer), perpendicular to the printer bed. Average density was in the range 1.20 – 1.22. Conclusions: 3D printing shows great promise for use in fabricating electron bolus. This work indicates that printed PLA can be a suitable material provided the increased attenuation is properly accounted for.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".