WE-E-BRB-09: A GPU-Based Monte Carlo QA Tool for IMRT and VMAT
Bibliographic record
Abstract
PURPOSE: To develop a GPU-based Monte Carlo (MC) 3D dosimetry quality assurance (QA) tool employing patient geometry and actual delivery information. METHODS: First, we generate fluence maps at all beam angles from the initial treatment plan. A GPU-based MC dose engine, gDPM, is employed for the secondary dose calculation (SDC) on patient CT. This SDC is used to verify the TPS plan dose (PD) accuracy. Before the 1st treatment fraction, we deliver the treatment plan on a Linac without any phantom setup to obtain machine log files. With the log files, we extract actually delivered fluence maps at all beam angles and perform delivered dose calculation (DDC) using gDPM. The difference between DDC and SDC indicates possible errors in data transferring and machine delivery. Lastly, the comparison between DDC and PD shows the accumulative errors from all the possible sources. Moreover, a web application for this QA tool is developed for clinical use. We have tested this QA tool on 6 patients, 4 VMAT and 2 IMRT patients. We reported mean gamma values and passing rates inside the 20% isodose line; DVH plot and dose difference matrix are also documented. RESULTS: For all six patients, the gamma passing rates within the 20% isodose line for SDC, DDC and PD comparisons are all higher than 95%. In the DVH plot, the three dose distributions were found to be very close. A typical IMRT or VMAT case takes less than one minute to run the whole QA tool. CONCLUSIONS: We have developed a GPU-based MC QA tool which can be used for efficient and easy IMRT and VMAT QA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".