Online virtual isocenter based radiation field targeting for high performance small animal microirradiation
Bibliographic record
Abstract
Advances in precision microirradiators for small animal radiation oncology studies have provided the framework for novel translational radiobiological studies. Such systems target radiation fields at the scale required for small animal investigations, typically through a combination of on-board computed tomography image guidance and fixed, interchangeable collimators. Robust targeting accuracy of these radiation fields remains challenging, particularly at the millimetre scale field sizes achievable by the majority of microirradiators. Consistent and reproducible targeting accuracy is further hindered as collimators are removed and inserted during a typical experimental workflow. This investigation quantified this targeting uncertainty and developed an online method based on a virtual treatment isocenter to actively ensure high performance targeting accuracy for all radiation field sizes. The results indicated that the two-dimensional field placement uncertainty was as high as 1.16 mm at isocenter, with simulations suggesting this error could be reduced to 0.20 mm using the online correction method. End-to-end targeting analysis of a ball bearing target on radiochromic film sections showed an improved targeting accuracy with the three-dimensional vector targeting error across six different collimators reduced from [Formula: see text] mm (mean ± SD) to [Formula: see text] mm for an isotropic imaging voxel size of 0.1 mm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".