Technical Note: Manufacturing of a realistic mouse phantom for dosimetry of radiobiology experiments
Bibliographic record
Abstract
Purpose The goal of this work was to design a realistic mouse phantom as a useful tool for accurate dosimetry in radiobiology experiments. Methods A subcutaneous tumor‐bearing mouse was scanned in a microCT scanner, its organs manually segmented and contoured. The resulting geometries were converted into a stereolithographic file format (STL) and sent to a multimaterial 3D printer. The phantom was split into two parts to allow for lung excavation and 3D‐printed with an acrylic‐like material and consisted of the main body (mass density ρ=1.18 g/cm3) and bone (ρ=1.20 g/cm3). The excavated lungs were filled with polystyrene (ρ=0.32 g/cm3). Three cavities were excavated to allow the placement of a 1‐mm diameter plastic scintillator dosimeter (PSD) in the brain, the center of the body and a subcutaneous tumor. Additionally, a laser‐cut Gafchromic film can be placed in between the two phantom parts for 2D dosimetric evaluation. The expected differences in dose deposition between mouse tissues and the mouse phantom for a 220‐kVp beam delivered by the small animal radiation research platform (SARRP) were calculated by Monte Carlo (MC). Results MicroCT scans of the phantom showed excellent material uniformity and confirmed the material densities given by the manufacturer. MC dose calculations revealed that the dose measured by tissue‐equivalent dosimeters inserted into the phantom in the brain, abdomen, and subcutaneous tumor would be underestimated by 3–5%, which is deemed to be an acceptable error assuming the proposed 5% accuracy of radiobiological experiments. Conclusions The low‐cost mouse phantom can be easily manufactured and, after a careful dosimetric characterization, may serve as a useful tool for dose verification in a range of radiobiology experiments.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".