The effect of magnetic field on Linac based Stereotactic Radiosurgery dosimetric parameters
Bibliographic record
Abstract
Abstract Objective : MR-linac machines are being developed for image-guided radiation therapy but the magnetic field of such machines could affect dose distributions. The purpose of this work was to evaluate the effect of a magnetic field on linac beam dosimetric parameters including penumbra for circular cones used in radiosurgery. Methods : Monte Carlo simulation was conducted for a linac machine with circular cones at 6 MV beam. A homogenous magnetic field of 1.5 T was applied transversely and parallel to the radiation beam. Percentage depth dose (PDD) and beam profiles in a water phantom with and without the magnetic field were calculated. Results : The results have shown that when the magnetic field is applied transversely, the PDDs in the water phantom differ in the buildup region and distant part of PDD curves. The beam profiles at three different depths are all significantly different from those without the magnetic field. The penumbra is greater when a magnetic field has been applied. Conclusion : Linear accelerator-based SRT and SRS use small circular cones. The beam penumbra for these cones can change in the presence of a magnetic field. The perturbation of dose distribution has been also observed in a patient plan due to the presence of a magnetic field. The results of this study show that dose distributions in the presence of a magnetic field must be considered for MR-guided radiotherapy treatments.
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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 | 0.002 |
| 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 | 0.000 | 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".