SU‐E‐T‐497: A Method for Automatic Commissioning of a GPU‐Based Monte Carlo Code for Clinical Photon Beam Dose Calculation
Bibliographic record
Abstract
Purpose: Monte Carlo (MC) method is recognized as the most accurate method for dose calculation. The commissioning of beam models is crucial for the clinical implementation of a MC code. We propose an automatic commissioning method for our GPU‐based MC dose engine (gDPM) using a source model based on the concept of phase‐space‐let (PSL). Methods: A PSL contains a group of particles that are of the same type and close in space and energy. PSLs for a reference phase‐space file are first generated, and dose for each PSL is pre‐calculated in water. Weighting factor of each PSL is adjusted to fine tune the fluence distribution and energy spectrum, so that the corresponding calculated dose matches the standard measured dose in water such as PDD and dose profiles. This is essentially an underdetermined least‐square minimization problem. Therefore, we add symmetric and smooth regularizations to the optimization problem, assuming that the dose profiles are symmetric and smooth. A split Bregman method is adopted to solve this optimization problem. Results: The phase‐space file of a Varian TrueBeam 6MV beam was used to generate PSLs for all 6MV beams. First, for a simulation study, the ‘measurement’ data was obtained from MC dose calculation using a set of field‐size‐dependent phase‐space files of a Siemens 6MV beam. The PDD and dose profiles calculated from the commissioned beam model agree well (within 1%) with the ‘measurement’ data. Second, realistic clinical data of Varian, Siemens, and Elekta machines were also used to test our method and achieved similar commissioning accuracy of less than 1% dose difference. Conclusion: Using the PSL source model, the proposed commissioning method can automatically fine tune the fluence distribution and energy spectrum of the reference phase‐space file to match measured data for clinical beam to be commissioned, relieving clinical MC users from conventional cumbersome commissioning process.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".