The sensitivity of gamma index analysis to detect multileaf collimator (MLC) positioning errors using Varian TrueBeam EPID and ArcCHECK for patient-specific prostate volumetric-modulated arc therapy (VMAT) quality assurance
Bibliographic record
Abstract
Abstract Background Due to the increased degree of modulation and complexity of volumetric-modulated arc therapy (VMAT) plans, it is necessary to have a pre-treatment patient-specific quality assurance (QA) programme. The gamma index is commonly used to quantitatively compare two dose distributions. In this study we investigated the sensitivity of single- and multi-gamma criteria techniques to detect multileaf collimator (MLC) positioning errors using the Varian TrueBeam Electronic Portal Imaging DeviceTM (EPID) dosimetry and the ArcCHECKTM device. Materials and methods All active MLC positions of seven intact prostate patients VMAT plans were randomly changed with a mean value of 0.25, 0.5, 1 and 2 mm and a standard deviation of 0.1 mm on 25, 50, 75 and 100% of the control points. The change in gamma passing rates of six gamma criteria of 3%/3 mm, 3%/2 mm, 3%/1 mm, 2%/2 mm, 2%/1 mm and 1%/1 mm were analysed individually (single-gamma criterion) and as a group (multi-gamma criteria) as a function of the simulated errors. We used the improved and global gamma calculation algorithms with a low dose threshold of 10% in the EPID and ArcCHECK software, respectively. The changes in the planning target volume dose distributions and the organs at risk due to the MLC positioning errors were also studied. Results When 25, 50, 75 and 100% of the control points were modified by the introduction of the simulated errors, the smallest detectable errors with the EPID were 2, 1, 0.5 and 0.5 mm, respectively, using the multi-gamma criteria technique. Similarly for the single-gamma criteria technique errors as small as 2, 1, 1 and 1 mm applied to 25, 50, 75 and 100% of the control points, respectively, were detectable using a 2%/2 mm criterion. However, the smallest detectable errors with the ArcCHECK when using the multi-gamma criteria technique were 2, 2 and 1 mm when MLC errors were applied on 50, 75 and 100% of the control points. When only 25% of the control points were affected the ArcCHECK were unable to detect any of the errors applied. No noticeable difference was observed in the sensitivity using the single- or the multi-gamma criteria techniques with the ArcCHECK. Conclusion The Varian TrueBeam EPID dosimetry shows a higher sensitivity in detecting MLC positioning errors compared with the ArcCHECK regardless of using the single- or the multi-gamma criteria techniques. Higher sensitivity was observed using the multi-gamma criteria technique compared with the single-criterion technique when using the EPID.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".