Poster ‐ 50: The effect of metallic artifacts and their correction on CyberKnife skull and spine tracking
Bibliographic record
Abstract
Purpose: CyberKnife tracking compares DRRs and periodic orthogonal x‐rays for patient localization, couch adjustments, and position adjustment of the linac head. Metallic artifacts in CT scans can potentially create discrepancies between DRRs and x‐rays. Additionally, suboptimal correction of artifacts may lead to inaccurate reconstruction of anatomy. This study investigates the effects that metallic artifacts and their correction have on CyberKnife skull and spine tracking. Methods: Skull tracking was tested with an anthropomorphic head‐neck phantom, using a Philips Brilliance Big Bore 16‐slice CT‐simulator with and without gold placed on the eye to simulate an external eyelid weight. Metallic artifacts in images with gold were corrected with orthopedic metal artifact reduction (O‐MAR). For each scan set, treatment plans were created and corrected couch positions for 10 phantom setups were recorded. To test spine tracking where bony anatomy is distorted due to O‐MAR for spinal screws, a spinous process was over‐ridden with a CT number of 55 HU. DRRs for this spine were compared with anatomically‐correct x‐rays to test for errors in spine matching. Results: Standard deviations in each direction and rotation for the no‐metal, metal, and metal with O‐MAR setups were comparable. Differences in average setup between the uncorrected and O‐MAR corrected plans were less than 0.8 times the no‐metal standard deviations. For the removed spinous process, the skeletal matching mesh indicated minimal error in anatomy: within daily patient variation. Conclusions: CyberKnife skull and spine tracking performed robustly against clinically realistic metallic artifacts and bony anatomy errors possibly introduced by O‐MAR.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".