SU‐E‐T‐216: Investigation of the System Performance of An Online Dose Verification Device
Bibliographic record
Abstract
Purpose: To measure the detector response function of an online dose verification device and to validate our measurement by applying the detector response to Monte Carlo (MC) derived incident fluence. Methods: A transmission detector array (IBA Dosimetry, Schwarzenbruck, Germany) was recently developed as an IMRT quality assurance tool. To understand the detector system performance for verification of IMRT fluence, the detector response function at 6MV of a single ion chamber in the detector was measured with a narrow slit collimator. The narrow slit was formed by two 24 × 72 × 152 mm3 lead blocks, which provided a slit of 0.25 × 10 mm2. The LSF of the detector was obtained by laterally translating it in 0.25 mm steps underneath the slit. Two head‐and‐neck IMRT fields were also measured using the device. The same fields were simulated in our BEAMnrc MC model, and the MC incident fluence was convolved with the 2D detector response to obtain calculated dose. The measured and calculated dose distributions were then quantitatively compared using chi comparison (3%/3mm) for in‐field points (defined as those above 10% maximum dose). Results: The FWHM of the measured detector response for a single chamber is 4.3 mm which is comparable to the chamber diameter of 3.8 mm. For both examined IMRT fields, the Chi comparison between measured and calculated data show good agreement with 100% of in‐field points below a chi of 1.0 (chi <0.6 for IMRT field 1 and <0.12 for IMRT field 2). Conclusions: The detector response function for a new novel online detector has been measured at 6 MV using a narrow slit technique. The good comparison shown between measured and calculated dose of the two IMRT fields is a validation of our response function measurement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".