Charged particle transport in magnetic fields in EGSnrc
Bibliographic record
Abstract
PURPOSE: To accurately and efficiently implement charged particle transport in a magnetic field in EGSnrc and validate the code for the use in phantom and ion chamber simulations. METHODS: The effect of the magnetic field on the particle motion and position is determined using one- and three-point numerical integrations of the Lorentz force on the charged particle and is added to the condensed history calculation performed by the EGSnrc PRESTA-II algorithm. The code is tested with a Fano test adapted for the presence of magnetic fields. The code is compatible with all EGSnrc based applications, including egs++. Ion chamber calculations are compared to experimental measurements and the effect of the code on the efficiency and timing is determined. RESULTS: Agreement with the Fano test's theoretical value is obtained at the 0.1% level for large step-sizes and in magnetic fields as strong as 5 T. The NE2571 dose calculations achieve agreement with the experiment within 0.5% up to 1 T beyond which deviations up to 1.2% are observed. Uniform air gaps of 0.5 and 1 mm and a misalignment of the incoming photon beam with the magnetic field are found to produce variations in the normalized dose on the order of 1%. These findings necessitate a clear definition of all experimental conditions to allow for accurate Monte Carlo simulations. It is found that ion chamber simulation times are increased by only 38%, and a 10 × 10 × 6 cm(3) water phantom with (3 mm)(3) voxels experiences a 48% increase in simulation time as compared to the default EGSnrc with no magnetic field. CONCLUSIONS: The incorporation of the effect of the magnetic fields in EGSnrc provides the capability to calculate high accuracy ion chamber and phantom doses for the use in MRI-radiation systems. Further, the effect of apparently insignificant experimental details is found to be accentuated by the presence of the magnetic field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".