SU‐E‐T‐469: The Effects of Patient Anatomy and Parallel Magnetic Fields on Beamlet Dose Distributions
Bibliographic record
Abstract
Purpose: Beamlets are generated in a patient geometry in the presence of a magnetic field to investigate the effects of tissue density and magnetic field on beamlet dose distributions, which is important for the optimization of photon fluence to be delivered by a linac‐MR system. Methods: 50×50 mm2 fields were placed with isocenter in the middle of a patient's right lung. Each treatment field was decomposed into 100 beamlets (each 5×5 mm2). BEAMnrc scored the particle phase space at 100.2 cm from the source in the linac‐MR geometry (isocentre at 126 cm) with parallel magnetic fields of 0, 0.56, and 3T. DOSXYZnrc was modified to score the energy deposited by particles from this phase space as a function of the beamlet the particle passed through. The calculation volume of 70×46×64 voxels encompassed the patient with a voxel size of 3×3×3 mm3. Each beamlet was normalized to the dose calculated to a 3×3×3 mm3 voxel with isocenter at 5cm depth in a flat water tank without a magnetic field. Results: Beamlet files were calculated on Western Canada's high performance computing cluster (Westgrid) using 100 processors, enabling simulation of 109 histories in less than 3 hours. The resulting files, which contained 3D dose distributions for all 100 beamlets, were 81 MB per field. The Monte Carlo uncertainty was also stored. The gyroradii for 1 MeV electron traversing field lines at 20 degrees are 2.9mm and 0.5mm for 0.56 and 3T fields respectively. The 0.56T parallel magnetic field has a small effect compared to the distortion of the beamlet introduced by the presence of lung. Conclusions: The effect of tissue heterogeneities is more significant than the effect of a 0.56T parallel magnetic field. A 3T field refocuses the dose in lung to the beamlet path and significantly reduces the lateral electron scatter.
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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 | 0.000 | 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 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".