An anthropomorphic maxillofacial phantom using 3-dimensional printing, polyurethane rubber and epoxy resin for dental imaging and dosimetry
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to construct an anthropomorphic maxillofacial phantom for dental imaging and dosimetry purposes using three-dimensional (3D) printing technology and materials that simulate the radiographic properties of tissues. METHODS: Stereolithography photoreactive resins, polyurethane rubber and epoxy resin were modified by adding calcium carbonate and strontium carbonate powders or glass bubbles. These additives were used to change the materials' CT numbers to mimic various body tissues. A maxillofacial phantom was designed using CT images of a head. RESULTS: Commercial 3D printing resins were found to have CT numbers near 120 HU and were used to print intervertebral discs and an external skin for the maxillofacial phantom. By adding various amounts of calcium carbonate and strontium carbonate powders the CT number of the resin was raised to 1000 & 1500 HU and used to print bone mimics. Epoxy resin modified by adding glass bubbles was used in assembly and as a cartilaginous mimic. Glass bubbles were added to polyurethane rubber to reduce the CT number to simulate soft tissue and filled spaces between the printed anatomy and external skin of the phantom. CONCLUSION: The maxillofacial phantom designed for dental imaging and dosimetry constructed using 3D printing, polyurethane rubbers and epoxy resins represented a patient anatomically and radiographically. The results of the designed phantom, materials and assembly process can be applied to generate different phantoms that better represent diverse patient types and accommodate different ion chambers.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".