RADPOS System as a Dose and Position Quality Assurance Tool for 4D Radiotherapy with CyberKnife
Bibliographic record
Abstract
The CyberKnife system consists of a compact linear accelerator mounted on a mobile robotic arm.It uses the Synchrony Motion Tracking System to compensate for respiratory motion.This complex radiotherapy system needs independent performance verification to assure safe treatments.In this work, we use the RADPOS 4D dosimetry system to verify CyberKnife's motion tracking and delivered dose.RADPOS motion measurements are compared with Synchrony log files containing internal metal fiducial positions determined using orthogonal kV x-ray imaging, and external LED marker positions determined using a stereoscopic camera system.RADPOS dose measurements Statement of OriginalityI performed the data analysis for the various experiments.In addition, I wrote the MATLAB programs for: importing the data from CyberKnife log files and RADPOS output files, implementing the coordinate alignment algorithm, and analyzing certain aspects of the positional and dosimetric results.The film analysis scripts, involving film calibration and dose analysis, were written by Eric Vandervoort.I was responsible for preparing the apparatus for the various experiments that were performed.This included measuring, cutting, and pre-scanning the film, and setting up the hardware and software for RADPOS and the Quasar phantoms.Quasar modifications were done by Bernie Lavigne and Ron Romain.Pre-experiment CT scans were done by Steve Andrusyk, with phantom placement and setup done by me, Eric Vandervoort, and Joanna Cygler.Experiments were performed by me, Eric Vandervoort (operated the CyberKnife) and Joanna Cygler (oversaw the experiment).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".