SU-G-JeP2-15: Proton Beam Behavior in the Presence of Realistic Magnet Fields
Bibliographic record
Abstract
Purpose: To investigate the effects of magnetic fields on proton therapy beams for integration with MRI. Methods: 3D magnetic fields from an open-bore superconducting MRI model (previously developed by our group) and 3D magnetic fields from an in-house gradient coil design were applied to various mono energetic proton pencil beam (80MeV to 250MeV) simulations. In all simulations, the z-axis of the simulation geometry coincided with the direction of the B0 field and magnet isocentre. In each simulation, the initial beam trajectory was varied. The first set of simulations performed was based on analytic magnetic force equations (analytic simulations), which could be rapidly calculated yet were limited to propagating proton beams in vacuum. The second set is full Monte Carlo (MC) simulations, which used GEANT4 MC toolkit. Metrics such as the beam position and dose profiles were extracted. Comparisons between the cases with and without magnetic fields present were made. Results: The analytic simulations served as verification checks for the MC simulations when the same simulation geometries were used. The results of the analytic simulations agreed with the MC simulations performed in vacuum. The presence of the MRI's static magnetic field causes proton pencil beams to follow a slight helical trajectory when there were some initial off-axis components. The 80MeV, 150MeV, and 250MeV proton beams rotated by 4.9o, 3.6o, and 2.8o, respectively, when they reached z=0cm. The deflections caused by gradient coils’ magnetic fields show spatially invariant patterns with a maximum range of 0.5mm at z=0cm. Conclusion: This investigation reveals that both the MRI's B0 and gradient magnetic fields can cause small but observable deflections of proton beams at energies studied. The MRI's static field caused a rotation of the beam while the gradient coils’ fields effects were spatially invariant. Dr. B Gino Fallone is a co-founder and CEO of MagnetTx Oncology Solutions (under discussions to license Alberta bi-planar linac MR for commercialization)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".