SU-F-303-15: Ion Chamber Dose Response in Magnetic Fields as a Function of Incident Photon Energy
Bibliographic record
Abstract
Purpose: In considering the continued development of synergetic MRI-radiation therapy machines, we seek to quantify the variability of ion chamber response per unit dose in the presence of magnetic fields of varying strength as a function of incident photon beam quality and geometric configuration. Methods: To account for the effect of magnetic fields on the trajectory of charged particles a new algorithm was introduced into the EGSnrc Monte Carlo code. In the egs_chamber user code the dose to the cavity of an NE2571 ion chamber is calculated in two configurations, in 0 to 2 T magnetic fields, with an incoming parallel 10×10 cm2 photon beam with energies ranging between 0.5 MeV and 8 MeV. In the first, the photon beam is incident on the long-axis of the ion chamber (config-1), and in the second the beam is parallel to the long-axis and incident from the conical end of the chamber (config-2). For both, the magnetic field is perpendicular to the direction of the beam and the long axis of the chamber. Results: The ion chamber response per unit dose to water at the same point is determined as a function of magnetic field and is normalized to the 0T case for each of incoming photon energies. For both configurations, accurate modeling of the ion chamber yielded closer agreement with the experimental results obtained by Meijsing et. al (2009). Config-1 yields a gradual increase in response with increasing field strength to a maximum of 13.4% and 1.4% for 1 MeV and 8 MeV photon beams, respectively. Config-2 produced a decrease in response of up to 6% and 13% for 0.5 MeV and 8 MeV beams, respectively. Conclusion: These results provide further support for ion chamber calibration in MRI-radiotherapy coupled systems and demonstrates noticeable energy dependence for clinically relevant fields.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".