SU‐E‐T‐213: Comparison of Two Methods for Patient Specific VMAT QA Using Portal Dosimetry and IBA MatriXX
Bibliographic record
Abstract
Purpose: To quantitatively evaluate two Methods: portal dosimetry and MatriXX placed in a dedicated holder, for clinical VMAT patient specific QA using gamma analysis. Methods: 21 prostate VMAT double‐arc plans were generated in Eclipse. We used portal dosimetry and MatriXX to conduct plan verifications. Portal dosimetry, including the absolute dose mode, was commissioned according to Varians guide. The MatriXX was commissioned as per vendor manual. In our study, Matrixx was inserted in a dedicated holder that was mounted on a linac treatment head with 5.0cm effective depth from the surface to the detector plane. In this geometry, the MatriXX detector plane is always facing the source perpendicularly. Thus the angular corrections for MatriXX detectors were not required. Gamma index was calculated using appropriate values of dose difference and distance‐to‐agreement. The threshold value, used for excluding low dose regions, was set to 5%. In portal dosimetry, calculated dose and measured dose were auto‐aligned and gamma index was obtained in the absolute dose mode. Portal dosimetry system was calibrated weekly or as needed to ensure its optimal working conditions. Results: In the MatriXX measurement, the Bland Altman plot was used to show that, with 95% confidence level, the measured Dmax agreed with the calculated Dmax. Then the dose distributions measured from MatriXX were normalized to Dmax and IBA so±ftware was used for gamma analysis. For gamma index calculated with 3%/3mm, both portal dosimetry (98.6%±1.1%) and MatriXX (97.0%±1.1%) exhibit similar passing rates, with portal doismetry demonstrating slightly higher passing rates than MatriXX. Conclusion: This work indicates that for VMAT patient specific QA, a well calibrated portal dosimetry system can be equivalent to or better than the MatriXX system. Future endeavors may extend to the analysis of VMAT plans for other sites and exploration of the method using the MatriXX inserted in a MultiCube phantom.
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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 | 0.003 | 0.006 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".