SU‐F‐J‐146: Experimental Validation of 6 MV Photon PDD in Parallel Magnetic Field Calculated by EGSnrc
Bibliographic record
Abstract
Purpose: Integrated linac‐MR systems are uniquely suited for real time tumor tracking during radiation treatment. Understanding the magnetic field dose effects and incorporating them in treatment planning is paramount for linac‐MR clinical implementation. We experimentally validated the EGSnrc dose calculations in the presence of a magnetic field parallel to the radiation beam travel. Methods: Two cylindrical bore electromagnets produced a 0.21 T magnetic field parallel to the central axis of a 6 MV photon beam. A parallel plate ion chamber was used to measure the PDD in a polystyrene phantom, placed inside the bore in two setups: phantom top surface coinciding with the magnet bore center (183 cm SSD), and with the magnet bore's top surface (170 cm SSD). We measured the field of the magnet at several points and included the exact dimensions of the coils to generate a 3D magnetic field map in a finite element model. BEAMnrc and DOSXYZnrc simulated the PDD experiments in parallel magnetic field (i.e. 3D magnetic field included) and with no magnetic field. Results: With the phantom surface at the top of the electromagnet, the surface dose increased by 10% (compared to no‐magnetic field), due to electrons being focused by the smaller fringe fields of the electromagnet. With the phantom surface at the bore center, the surface dose increased by 30% since extra 13 cm of air column was in relatively higher magnetic field (>0.13T) in the magnet bore. EGSnrc Monte Carlo code correctly calculated the radiation dose with and without the magnetic field, and all points passed the 2%, 2 mm Gamma criterion when the ion chamber's entrance window and air cavity were included in the simulated phantom. Conclusion: A parallel magnetic field increases the surface and buildup dose during irradiation. The EGSnrc package can model these magnetic field dose effects accurately. Dr. Fallone is a co‐founder and CEO of MagnetTx Oncology Solutions (under discussions to license Alberta bi‐planar linac MR for commercialization).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".