SU-D-141-04: Patient-Specific EPID Based High Resolution 3D VMAT QA
Bibliographic record
Abstract
Purpose: Existing commercial 2D/3D array detectors for patient-specific VMAT QA are not suitable for stereotactic plans because of the poor spatial resolution of the embedded detectors. A pre-treatment QA method was developed to perform a complete 3D patient dose analysis of stereotactic VMAT plans using Sun Nuclear 3DVH software and a high resolution EPID detector in cine acquisition mode. Methods: Cine images are acquired continuously during the irradiation with the EPID panel while the delivery is monitored using the iCom messaging interface from Elekta linear accelerators to record the gantry angle and the delivered monitor units as a function of time. Then, acquired greyscale snapshots of the MLC apertures can be correlated to their associated gantry angles and converted into absolute dose. According to the sampling desired, the control points of the treatment plan are interpolated to produce planar dose maps at angles corresponding to those at which images were acquired. Disagreement between measured and calculated dose maps can be evaluated and the delivered dose distribution in the patient can be reconstructed by perturbing the calculated dose using errors detected in planar dose measurements (3DVH Planned Dose Perturbation algorithm). Results: Several stereotactic brain and lung plans from Pinnacle were analyzed using this method. Regarding the agreement between measured and calculated dose maps, the average passing rate for a 2%-2mm gamma criterion (threshold = 5%) is close to 97% ± 3%. The complete 3D patient dose analysis showed good agreement between treatment planning system and 3DVH dose volume histograms for both targets and organs at risk. Conclusion: In this study, we developed a practical approach based on EPID cine acquisition mode and the Elekta iCom interface to perform stereotactic VMAT QA. This approach takes advantage of the high spatial resolution of EPID panels to achieve an appropriate and accurate independent 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".