MO‐G‐BRB‐01: A Combined Dose Delivery and Transmission Dose Verification Model for a Small Animal Precision Irradiator for Pre‐Clinical Studies
Bibliographic record
Abstract
Purpose: Novel small animal micro‐irradiators are becoming available for pre‐clinical research but they lack dedicated treatment planning systems. The purpose of this study is to develop both a Monte Carlo (MC) model and a portal dose prediction model of a small animal micro‐IR to enable forward dose calculations and compare the planned against the delivered treatment. Methods: A MC model of a small animal micro‐IR from the x‐ray tube assembly to the detector was developed. The model was compared to radiochromic film and portal images for validation. A portal dose calibration model was also developed and portal dose images were compared to film measurements. A rodent was irradiated with 1 Gy at 225 kVp (0.32 mm Cu) while portal images were acquired. A simulated portal image from the MC model was compared to the portal image acquired during irradiation. Results: Simulations of the MC modelˈs x‐ray spectra, beam profile, and half value layer thicknesses agreement within 2 % against measurements or external calculations. The portal dose prediction model resulted with 63% of pixels with a gamma value less than 1 for a gamma criterion of 5% 0.8 mm. A visual comparison between the simulated portal image and acquired portal image during irradiation of a rodent show good agreement but intensity values around regions of bone deviate from the acquired portal image. Conclusion: We have demonstrated that we can simulate the entire irradiation process of a small animal micro‐irradiator and generate comparable predicted portal images compared to acquired portal images. We believe that further refinement to the tissue and density assignment in the MC model will improve our results.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".