MicroCT imaging dose to mouse organs using a validated Monte Carlo model of the small animal radiation research platform (SARRP)
Bibliographic record
Abstract
The goal of this work was to establish imaging dose to mouse organs with a validated Monte Carlo (MC) model of the image-guided Small Animal Radiation Research Platform (SARRP) and to investigate the effect of scatter from the internal walls on animal therapy dose determination. A MC model of the SARRP was built in the BEAMnrc code and validated with a series of homogeneous and heterogeneous phantom measurements. A segmented microCT scan of a mouse was used in DOSXYZnrc to determine mouse organ microCT imaging doses to 15-35 g mice for the SARRP pancake (mouse lying on couch) and standard (mouse standing on couch) imaging geometries for 40-80 kVp tube voltages. Imaging dose for off-center positioning shifts and maintaining image noise across tube voltages were also calculated. Half-value layer (HVL) measurements for the 220 kVp therapy beam in the presence of the SARRP shielding cabinet were modeled in BEAMnrc and compared to the 100 cm source-to-detector distance (SDD) in the scatter free, narrow-beam geometry recommended by the American Association of Physicists in Medicine Task Group 61 (AAPM TG-61). For a 60 kVp, 0.8 mA, and 60 s scan protocol, maximum mean organ imaging doses to boney and non-boney structures were 10.5 cGy and 3.5 cGy, respectively, for an average size 20 g mouse. Current-exposure combinations above 323, 203, 147, 116, and 95 mAs for 40-80 kVp tube voltages, respectively, will increase body doses above 10 cGy. MicroCT mean body dose was 18% lower in pancake compared to standard imaging geometry. An 11% difference in measured HVL at a 50 cm SDD was found compared to MC simulated HVL for the AAPM TG-61 recommended scatter free geometry at a 100 cm SDD. This change in HVL resulted in a 0.5% change in absorbed dose to water calculations for the treatment beam.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".