SU‐E‐T‐404: The Effect of Field/target Size on Quality Assurance of TomoTherapy IMRT Treatments When Using a Diode Array for Absolute and Relative Dose Measurements
Bibliographic record
Abstract
Purpose: Despite the known energy sensitivity of diodes due to an over‐ response to low‐energy photons, the presence of which depends on field size, there is trend toward using diode arrays for performing dose measurements in IMRT QA. The purpose of this investigation is to quantify the dependence of diode array dose measurements on field/target size, assess machine output tracking abilities, and improve the accuracy of diode arrays measurements for helical TomoTherapy treatments. Methods: A Delta, 4 diode array was calibrated for TomoTherapy patient QA. Using the calibration set‐up, the dose from static beams was measured with both the Delta, 4 and an A1SL chamber as a function of field size. The dose tracking abilities of the Delta, 4 was then assessed with daily helical exposures of uniform dose to a cylindrical target in the phantom, followed each time by a second measurement with an A1SL chamber in a cheese phantom. Additional measurements of this type were obtained as a function of target diameter to derive field/target size scaling factors for the dose measured by Delta, 4. A1SL and Delta, 4 measurements were also obtained for series of patient DQAs. Results: Delta, 4 dose measurements with field size diverged from A1SL results by up to 2% as the field size diverged from the reference. Clinically acceptable machine output tracking was exhibited by the Delta, 4 based on daily measurements of the helical exposures to a target with fixed diameter. However, the scaling factors for the Delta, 4 based on these measurements are as high as 4% for large target sizes. When applying the scaling corrections to patient DQAs, agreement between the Delta, 4 and A1SL is improved to within 1% in most cases. Conclusions: Dose measurements with the Delta, 4 diode array are sensitive to changes in field/target size. Accuracy can be improved with the application of measured correction factors.
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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 | 0.004 | 0.011 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".