Sci‐Sat AM: Radiation Dosimetry and Practical Therapy Solutions ‐ 05: Not all geometries are equivalent for magnetic field Fano cavity tests
Bibliographic record
Abstract
The coupling of MRI and radiation treatment systems for the application of magnetic resonance guided radiation therapy necessitates a reliable magnetic field capable Monte Carlo (MC) code. In addition to the influence of the magnetic field on dose distributions, the question of proper calibration has arisen due to the several percent variation of ion chamber and solid state detector responses in magnetic fields when compared to the 0 T case (Reynolds et al., Med Phys, 2013). In the absence of a magnetic field, EGSnrc has been shown to pass the Fano cavity test (a rigorous benchmarking tool of MC codes) at the 0.1 % level (Kawrakow, Med.Phys, 2000), and similar results should be required of magnetic field capable MC algorithms. To properly test such developing MC codes, the Fano cavity theorem has been adapted to function in a magnetic field (Bouchard et al., PMB, 2015). In this work, the Fano cavity test is applied in a slab and ion‐chamber‐like geometries to test the transport options of an implemented magnetic field algorithm in EGSnrc. Results show that the deviation of the MC dose from the expected Fano cavity theory value is highly sensitive to the choice of geometry, and the ion chamber geometry appears to pass the test more easily than larger slab geometries. As magnetic field MC codes begin to be used for dose simulations and correction factor calculations, care must be taken to apply the most rigorous Fano test geometries to ensure reliability of such algorithms.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.015 |
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".