SU‐FF‐T‐417: Effect of Transverse Magnetic Fields On MV Photon Dose Distributions in Heterogeneous Media
Bibliographic record
Abstract
Purpose: To study the effect of transverse magnetic fields on dose deposition profiles in regions of mass density gradients. This work characterizes this effect with a simple Monte Carlo model of a lung tumor irradiated with a 6 MV photon beam. Method and Materials: The Geant4 Monte Carlo toolkit was used to simulate the irradiation of a simple phantom representing a lung tumor. A 4 cm spherical tumor made of soft tissue (1.06 g/cm3) was positioned at the center of a 14×14×14 cm3 box made of lung tissue (0.3 g/cm3). A uniform magnetic field was applied throughout the phantom perpendicularly to the beam axis. Simulations were conducted with field intensities ranging from 0 to 5 tesla. The phantom was irradiated with a 6 MV photon beam created with the BEAMnrc code. Results: Significant dose modulation was observed at the lung‐tumor interface. Regions of dose enhancement and symmetrically opposed regions of dose reduction were observed at the vicinity of the tumor boundary. These regions vary in shape, intensity and location with field strength in a non trivial way. As a rule of thumb, these crescent‐like regions tend to become sharper and shift rotationally with field strength. Conclusion: Magnetic fields were shown to affect significantly dose deposition profiles in heterogeneous media. Further investigations are required to understand the pattern of dose modulation as a function of magnetic field intensity, heterogeneity size and heterogeneity surface orientation relative to beam axis. Devising approaches to potentially exploit or cancel out these effects is also of interest.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".