Sci—Fri PM: Delivery — 01: Approach to evaluate the accuracy of individual control‐points of a VMAT delivery measured by an EPID
Bibliographic record
Abstract
A unique approach that uses Monte Carlo (MC) methods to validate time-resolved measured dose by an electronic portal imaging device (EPID) during delivery of volumetric modulated arc therapy (VMAT) treatments was investigated. Time-resolved dose is simultaneously scored in both the patient and EPID geometries. A RapidArc© verification plan was generated and delivered on a homogeneous cylindrical phantom. Portal images were collected using an aS1000 amorphous-silicon EPID attached to a Varian Clinac 2100ix. The images were acquired in the continuous acquisition mode and in-house analysis software was used to obtain images for each control point. We performed MC simulation and dose calculation of the verification plan using the EGSnrc MC package. The MC phantom file contains both the EPID model and the cylindrical phantom derived from the CT data sets. For the individual control point simulation, we rotate the phantom only using the gantry angle information. For this work, we calculated dose distributions for five control points (out of a total of 177) in the verification plan. The normalized measured and calculated data were compared using the chi comparison (computationally efficient implementation of gamma). The chi comparison between measured and calculated doses for the five control points using criteria of 3% and 3mm revealed an average of 88.7% of all pixels having χ<1. The agreement will improve when the measured portal images are corrected for flood field and support-arm backscatter effects. This approach allows us to obtain both the EPID dose and the phantom/patient dose. These preliminary results demonstrate promising accuracy. With further improvements, this approach will be useful for benchmarking of time-resolved EPID dose applications intended for rotational IMRT QA and adaptive radiation therapy.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".