Performance characterization of an integrated cone‐beam <scp>CT</scp> system for dedicated gamma radiosurgery
Bibliographic record
Abstract
PURPOSE: Icon™. METHODS: The performance tests have been categorized into: (a) image quality and mechanical integrity; (b) image coregistration fidelity; (c) adaptive treatment delivery quality; (d) high definition motion management performance characterization; (e) software communication performance testing of the integrated cone-beam CT (CBCT) system. RESULTS: All image quality performance characterization satisfied or exceeded manufacturer specifications. The image quality and mechanical stability of the CBCT system over a 3-month period was within tolerance with negligible (<0.1°) detector tilt angle. The CBCT definition of the stereotactic space had a measured average discrepancy of 0.15-0.16 mm in x, y, and z directions. On average, the high definition motion management system performance was within 0.05 mm with a residual offset of 0.15 mm when large displacements in a given direction were taken. The adaptive treatment delivery component as measured with CBCT coregistration of daily setups against reference setup images was accurate to within 0.2°. Comprehensive end-to-end testing showed a total uncertainty of better than 0.2 mm in positioning and 0.4% in dosimetry for treatment of centrally located lesions. CONCLUSIONS: A set of system performance characterization tests spanning all aspects of the Gamma Knife Icon are presented. Overall, the system performance was in line with manufacturer specifications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".