A novel intra-fraction motion monitoring system for stereotactic radiosurgery: proof of concept
Bibliographic record
Abstract
The purpose of this work is to develop a prototype system for continuous, three-dimensional (3D) monitoring of patient cranial motion during stereotactic radiosurgery. Using novel capacitive detector plates, the goal was to provide detection of cranial position inside a thermoplastic immobilizing mask, without relying on skin monitoring or use of ionizing radiation. A novel capacitive detector array was used to detect cranial translations with sub-millimeter accuracy. The array was comprised of four conductive plates arranged around the cranium. One superior plate was positioned at the cranial vertex, two lateral plates were positioned in sagittal planes at the lateral aspects of the cranium and one plate was located in a coronal plane anterior to the face. The system was calibrated by parameterizing a capacitive signal for each dimension as a function of spatial translation. The detector array performance was evaluated with the help of a volunteer in the absence of radiation. Separately, possible effects of electromagnetic interference and irradiation in the linac suite were assessed. Detector plates mounted at 1 cm original distance to the thermoplastic mask can detect sub-millimeter lateral and superior cranial motion. Detection of sub-millimeter anterior motion is possible when the plate is mounted closer to the patient (5-10 mm). No signal interference was observed when the capacitive array was irradiated. Our prototype detector array provides continuous, 3D translation detection with sub-millimeter precision. The signal provides sufficient signal to noise ratio and is stable in linac room environment and in direct radiation beam. The detector plate is sensitive to the position of the cranium inside a mask and offers the advantage of being insensitive to the mask itself. Future work will involve modifying the array to detect patient rotation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".