SU‐E‐T‐582: On‐Line Dosimetric Verification of Respiratory Gated Volumetric Modulated Arc Therapy Using the Electronic Portal Imaging Device
Bibliographic record
Abstract
Purpose: To investigate the clinical utility of on‐line verification of respiratory gated VMAT dosimetry during treatment. Methods: Portal dose images were acquired during treatment in integrated mode on a Varian TrueBeam (v. 1.6) linear accelerator for gated lung and liver patients that used flattening filtered beams. The source to imager distance (SID) was set to 160 cm to ensure imager clearance in case the isocenter was off midline. Note that acquisition of integrated images resulted in no extra dose to the patient. Fraction 1 was taken as baseline and all portal dose images were compared to that of the baseline, where the gamma comparison and dose difference were used to measure day‐to‐day exit dose variation. All images were analyzed in the Portal Dosimetry module of Aria (v. 10). The portal imager on the TrueBeam was calibrated by following the instructions for dosimetry calibration in service mode, where we define 1 calibrated unit (CU) equal to 1 Gy for 10×10 cm field size at 100 cm SID. This reference condition was measured frequently to verify imager calibration. Results: The gamma value (3%, 3 mm, 5% threshold) ranged between 92% and 100% for the lung and liver cases studied. The exit dose can vary by as much as 10% of the maximum dose for an individual fraction. The integrated images combined with the information given by the corresponding on‐line soft tissue matched cone‐beam computed tomography (CBCT) images were useful in explaining dose variation. For gated lung treatment, dose variation was mainly due to the diaphragm position. For gated liver treatment, the dose variation was due to both diaphragm position and weight loss. Conclusion: Integrated images can be useful in verifying dose delivery consistency during respiratory gated VMAT, although the CBCT information is needed to explain dose differences due to anatomical changes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".