TU‐E‐BRB‐04: Fast Monte Carlo Calculations in Magnetic Fields with GPUMCD for the MRI‐Linac
Bibliographic record
Abstract
Purpose: To develop a fast Monte Carlo code dedicated to a new hybrid imaging‐treatment modality, the MRI‐Linac, involving the irradiation of a patient in the presence of a strong magnetic field (MF). These conditions require a dose calculation engine capable of coping with the effect of the MF on the dose distribution during the planning stage. It is not likely that analytical solutions such as pencil beams calculations can be efficiently adapted for dose calculations within a MF. Monte Carlo simulations seem more appropriate but are typically too slow for a routine clinical utilization, let alone an online image‐based replanning approach. Methods: GPUMCD, a fast GPU‐based Monte Carlo dose calculation platform, was adapted to handle dose calculations within a magnetic field. The code was benchmarked and validated through comparisons with experimental measurements in two cases where large magnetic field induce significant dose effects at air‐tissue interfaces. The validation also included a timing benchmark in a clinically representative prostate case to evaluate if online dose calculation is possible. Results: GPUMCD was found to produce accurate dose distributions, according to a 2%‐2mm gamma analysis, when compared to experimental measurements. Furthermore, execution times of less than 0.4 seconds were achieved for one beam in a prostate case phantom for a 2% statistical uncertainty while 0.8 seconds are required for a 7 beams plan having the same uncertainty. Conclusions: These results suggest that GPUMCD is an interesting candidate for dose calculations for a hybrid MRI‐Linac modality. Its accuracy has been validated against experimental measurements and its speed let envision online replanning based on changes in anatomy found at the time of treatment.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.009 | 0.002 |
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".