SU‐C‐213‐02: Characterizing 3D Printing in the Fabrication of Variable Density Phantoms
Bibliographic record
Abstract
Purpose: In this work, we present characterization, process flow, quality control and application of 3D fabricated low density phantoms for radiotherapy quality assurance. Methods: A Rostock delta 3D printer using polystyrene filament of diameter 1.75 mm was used to print geometric volumes of 2×2×1 cm3 of varying densities. The variable densities of 0.1 to 0.75 g/cm 3 were created by modulating the infill. A computed tomography (CT) scan was performed to establish an infill‐density calibration curve as well as characterize the quality of the print such as uniformity and the infill pattern. The time required to print these volumes was also recorded. Using the calibration, two low density cones (0.19, 0.52 g/cm3) were printed and benchmarked against commercially available phantoms. The dosimetric validation of the low density scaling of Anisotropic Analytical Algorithm (AAA) was performed by using a 0.5 g/cm3 slab of 10×10×2.4 cm3 with EBT3 GafChromic film. The gamma analysis at 3%/3mm criteria were compared for the measured and computed dose planes. Results: Analysis of the volume of air pockets in the infill resulted in a reasonable uniformity for densities 0.4 to 0.75 g/cm3. Printed phantoms with densities below 0.4 g/cm3 exhibited a higher ratio of air to polystyrene resulting in large non‐uniformity. Compared to the commercial inserts, good agreement was observed only for the printed 0.52 g/cm3 cone. Dosimetric comparison for a printed low density volume placed in‐between layers of solid water resulted in >95% gamma agreement between AAA calculated dose planes and measured EBT3 films for a 6MV 5×5 cm2 clinical beam. The comparison showed disagreement in the penumbra region. Conclusion: In conclusion, 3D printing technology opens the door to desktop fabrication of variable density phantoms at economical prices in an efficient manner for the quality assurance needs of a small clinic.
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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 | 0.000 | 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) | 0.001 | 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".