Finite element analysis of a capacitive array for 6D intrafraction motion detection during stereotactic radiosurgery
Bibliographic record
Abstract
This work presents a non-contact, non-ionizing solution for the continuous detection and characterization of intrafraction cranial motion with six-degrees of freedom (DoF). This capacitive monitoring system is a modular tool capable of detecting the cranial position through a thermoplastic mask without the use of skin as a surrogate. The purpose of this investigation is to develop an array of capacitive monitoring sensor plates capable of detecting translational and rotational cranial motion during radiotherapy. This study compares the performance of different capacitive monitoring array designs for their potential to detect intrafraction cranial translations and rotations. To this end, a finite element analysis (FEA) model of the human cranium was used to calculate the system capacitance while simulating translational (superior-inferior, lateral, anterior-posterior) and rotational (roll, pitch, yaw) cranial motion. The model was validated by comparing simulation results against experimental results acquired with the help of human volunteers. The verified FEA model was then used to compare multiple potential array designs. The arrays' sensitivities to translational and rotational motion and uniqueness of response were compared to determine the most promising design for six-DoF motion detection. The most promising array design was chosen for a clinical volunteer study.
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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 | 0.000 | 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".