SU‐E‐J‐41: Is There An Optimal X‐Ray Technique for Prostate Treatment On CyberKnife?
Bibliographic record
Abstract
Purpose To establish guidelines for setting X‐ray technique for CyberKnife (Accuray) prostate treatments to keep imaging dose as low as reasonably achievable while maintaining consistent fiducial identification. Methods Four gold seed fiducial markers were inserted into the prostate region of a Rando anthropomorphic phantom, following guidelines for fiducial placement provided by Accuray. A CT scan of the phantom was obtained using our standard clinical protocol and a treatment plan (Fiducial Tracking) was created. In order to simulate prostate treatment on the CyberKnife G4 (TDS v9.6), the phantom was aligned to within 0.2 degrees and 0.2mm of the planned position using X‐ray technique settings of 140kVp, 320mA and 100ms. The phantom was then shifted and rotated to 8 different positions. At each of these 8 positions, 5 orthogonal X‐ray image pairs were acquired at combinations of 3 kVp settings (110kVp, 125kVp, 140kVp), and 5 mAs settings (5mAs, 10mAs, 20mAs, 32mAs), and the frequency of the tracking software correctly identifying all four fiducials per technique setting was recorded. The experiment was repeated for three different simulated patient sizes by wrapping the phantom in layers of bolus. Results Regardless of phantom size, there were clear trends toward increased reliability of fiducial detection with increased X‐ray technique (both kVp and mAs), however gains achieved by increasing technique beyond 125kVp and 10mAs are modest. An increase in technique was increasingly ineffective in improving fiducial detection as the phantom was rotated away from the planned position (eg with increasing pitch). A dependence of tracking reliability on the planning DRRs was also noted. Conclusion This method is a useful training tool for evaluating the effect of X‐ray technique on fiducial tracking. When fiducial tracking results are not reproducible, we recommend interventions such as eliminating an unreliable fiducial from tracking or adjusting patient position before increasing X‐ray technique.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".