SU‐E‐T‐171: Pre‐Treatment Radiotherapy Dose Verification Using Monte Carlo Doselet Modulation in a Spherical Phantom
Bibliographic record
Abstract
Purpose: To design a robust pre‐treatment dose verification tool for non‐coplanar IMRT and VMAT radiotherapy treatments using electronic portal images (EPIs). Methods: The method aims to modulate pre‐calculated Monte Carlo (MC) doselets, leveraging symmetry in a spherical phantom. The patient‐independent phase‐space source MC model of a Varian 21 EX 6MV Clinac was used to transport particles. We utilized cylindrical symmetry of the phase‐space above the jaws and azimuthally rotated its particles to lie along a radial strip. The strip was divided along the radius into 2mm (as projected to isocentre) segments, and the particles within each segment were used for dose calculation in a spherical water phantom via DOSXYZnrc, producing a doselet. For patient‐dependent dose calculation in the spherical water phantom the doselets were redistributed to fill a treatment field, and then modulated based on a fluence maps derived from EPIs. Finally, gantry and couch rotations are applied. The spherical symmetry of the geometry makes this process simple and efficient. Results: A 10x10 cm2 open field was tested against BEAMnrc and DOSXYZnrc (∼1% uncertainty) in a spherical phantom with voxel size 1.25×1.25×1.25 mm3. This passed a 3D gamma‐index test with 98% agreement for 3%/3mm criteria above the 10% isodose, and calculated in 46s. A 7 field IMRT plan was calculated in 3.5 minutes on 2.5×2.5×2.5 mm3 resolution and was compared with results using the phase‐space modulation method our group has published previously. The same gamma‐index test achieved 97% agreement in this comparison. Timing results are based on a single 2.1 GHz processor. Conclusion: Results demonstrate that our pre‐treatment dose verification method can be used for efficient dose verification of non‐coplanar treatments. Significant speed enhancements were achieved by utilizing phantom spherical symmetry in dose reconstruction.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".