SU‐F‐J‐52: A Novel Approach to X‐Ray Tube Quality Assurance for CBCT Systems in Order to Better Assess the Patient Imaging Dose in a Large, Multi‐Unit Treatment Facility
Bibliographic record
Abstract
Purpose: To standardize the tube calibration for Elekta XVI cone beam CT (CBCT) systems in order to provide a meaningful estimate of the daily imaging dose and reduce the variation between units in a large centre with multiple treatment units. Methods: Initial measurements of the output from the CBCT systems were made using a Farmer chamber and standard CTDI phantom. The correlation between the measured CTDI and the tube current was confirmed using an Unfors Xi detector which was then used to perform a tube current calibration on each unit. Results: Initial measurements showed measured tube current variations of up to 25% between units for scans with the same image settings. In order to reasonably estimate the imaging dose, a systematic approach to x‐ray generator calibration was adopted to ensure that the imaging dose was consistent across all units at the centre and was adopted as part of the routine quality assurance program. Subsequent measurements show that the variation in measured dose across nine units is on the order of 5%. Conclusion: Increasingly, patients receiving radiation therapy have extended life expectancies and therefore the cumulative dose from daily imaging should not be ignored. In theory, an estimate of imaging dose can be made from the imaging parameters. However, measurements have shown that there are large differences in the x‐ray generator calibration as installed at the clinic. Current protocols recommend routine checks of dose to ensure constancy. The present study suggests that in addition to constancy checks on a single machine, a tube current calibration should be performed on every unit to ensure agreement across multiple machines. This is crucial at a large centre with multiple units in order to provide physicians with a meaningful estimate of the daily imaging dose.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".