Performance optimization of capacitive motion sensing (CMS) system for intra-fraction motion detection during stereotactic radiosurgery
Bibliographic record
Abstract
The purpose of this investigation is to improve intra-fractional motion detection during cranial stereotactic radiosurgery with a novel capacitive motion sensing (CMS) system. Previous work showed that a capacitive detection system, based on a MPR121 capacitance-to-digital converter, provided a number of advantages over existing patient imaging systems used in the clinic, by uniquely offering ionizing-radiation-free and continuous monitoring without modification to the immobilization mask or treatment room. However, in order to provide submillimeter detection accuracy, the MPR121-based CMS system required relatively large sensors in close proximity to the patient. Therefore, the aim of this investigation was to improve sensitivity of the system, allowing reduction in sensor size and preserving its stable operation in the linear accelerator environment. For this, we developed, characterized and compared motion detection capabilities of four CMS systems based on different capacitance-to-digital converters: MPR121, CPT212B, FDC1004 and FDC2214. Among all candidates, the FDC2214-based system was found to uniquely combine accurate 3D motion detection in real time, with stable performance under ionizing radiation. It exhibited an order of magnitude improvement in sensitivity in comparison with the proof-of-study system, allowing a spatial precision as low as 0.3 mm, and its overall performance was found to satisfy the AAPM practice guidelines of positioning tolerance within 1 mm. Furthermore, the high sensitivity of the system allows both reduction of the sensor area and location more distant from the patient surface, which are key improvements with regard to development of a clinical device.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".