SU‐E‐T‐416: Experimental Evaluation of a Commercial GPU‐Based Monte Carlo Dose Calculation Algorithm
Bibliographic record
Abstract
Purpose: A new commercial GPU‐based Monte Carlo dose calculation algorithm (GPUMCD) developed by the vendor Elekta™ to be used in the Monaco Treatment Planning System (TPS) is capable of modeling dose for both a standard linear accelerator and for an Elekta MRI‐Linear accelerator (modeling magnetic field effects). We are evaluating this algorithm in two parts: commissioning the algorithm for an Elekta Agility linear accelerator (the focus of this work) and evaluating the algorithm's ability to model magnetic field effects for an MRI‐linear accelerator. Methods: A beam model was developed in the Monaco TPS (v.5.09.06) using the commissioned beam data for a 6MV Agility linac. A heterogeneous phantom representing tumor‐in‐lung, lung, bone‐in‐tissue, and prosthetic was designed/built. Dose calculations in Monaco were done using the current clinical algorithm (XVMC) and the new GPUMCD algorithm (1 mm3 voxel size, 0.5% statistical uncertainty) and in the Pinnacle TPS using the collapsed cone convolution (CCC) algorithm. These were compared with the measured doses using an ionization chamber (A1SL) and Gafchromic EBT3 films for 2×2 cm 2 , 5×5 cm 2 , and 10×10 cm 2 field sizes. Results: The calculated central axis percentage depth doses (PDDs) in homogeneous solid water were within 2% compared to measurements for XVMC and GPUMCD. For tumor‐in‐lung and lung phantoms, doses calculated by all of the algorithms were within the experimental uncertainty of the measurements (±2% in the homogeneous phantom and ±3% for the tumor‐in‐lung or lung phantoms), except for 2×2 cm 2 field size where only the CCC algorithm differs from film by 5% in the lung region. The analysis for bone‐in‐tissue and the prosthetic phantoms are ongoing. Conclusion: The new GPUMCD algorithm calculated dose comparable to both the XVMC algorithm and to measurements in both a homogeneous solid water medium and the heterogeneous phantom representing lung or tumor‐in‐lung for 2×2 cm 2 ‐10×10 cm 2 field sizes. Funding support was obtained from Elekta.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".