A combined dose calculation and verification method for a small animal precision irradiator based on onboard imaging
Bibliographic record
Abstract
PURPOSE: Novel small animal precision microirradiators (micro-IR) are becoming available for preclinical use and are often equipped with onboard imaging (OBI) devices. We investigated the use of OBI as a means to infer the accuracy of the delivered treatment plan. METHODS: Monte Carlo modeling of the micro-IR including an elliptical Gaussian electron beam incident on the x-ray tube was used to score dose and to continue photon transport to the plane of the OBI device. A model of the OBI detector response was used to generate simulated onboard images. Experimental OBI was performed at 225 kVp, gain∕offset and scatter-glare were corrected. Simulated and experimentally obtained onboard images of phantoms and a mouse specimen were compared for a range of photon beam sizes from 2.5 cm down to 0.1 cm. RESULTS: Simulated OBI can be used in small animal radiotherapy to determine if a treatment plan was delivered according to the prescription within an uncertainty of 5% for beams as small as 4 mm in diameter. For collimated beams smaller than 4 mm, beam profile differences remain primarily in the penumbra region of the smallest beams, which may be tolerable for specific preclinical micro-IR investigations. CONCLUSIONS: Comparing simulated to acquired OBI during small animal treatment radiotherapy represents a useful treatment delivery tool.
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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) | 0.001 | 0.000 |
| 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 | 0.001 |
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".