Po‐Poster ‐ 07: Commissioning of virtual linacs for Monte Carlo simulations by optimizing photon source characteristics
Bibliographic record
Abstract
In conjunction with rapidly expanding clinical Monte Carlo (MC) implementation, MC users are faced with the difficult and time consuming commissioning process of their virtual linac. After accurately configuring the treatment head according to manufacturer specifications, a rigorous and extensive benchmarking process is required to ensure that the MC virtual linac produces beams of essentially the same quality as those of the real treatment unit being modeled. Often, even after systematically varying the input parameters over a suitably chosen range, it is found that the shape of the measured profiles cannot be exactly matched. This limitation is attributed to the lack of accurate knowledge of the geometry and materials of some linac components, especially, the flattening filter. We have developed an automatic optimization method that allows a user with an arbitrary linear accelerator to commission a MC dose calculation engine that accurately reproduces the measured output of the accelerator in water. Using a simulated annealing optimization algorithm our method converges MC dose distributions to experimental measurements by optimizing the weights of particles in a phase space. To achieve this, a phase space is divided up using LATCH variable assignment, each beamlet is transported into a water tank phantom, and the dose deposition is scored separately. Individual beamlet weights are then optimized such that the weighted sum of beamlet dose depositions converge toward our target dose distribution. The resulting beamlet weights are then assigned to all particles in the original phase space where they are incorporated into all future simulations.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".