Quantification of Force and Torque Applied by a High-Field Magnetic Resonance Imaging System on an Ultrasonic Motor for MRI-Guided Robot-Assisted Interventions
Bibliographic record
Abstract
The risk of accidental dislodgement of robot-operated surgical mechanisms can lead to morbidity or mortality. The force and torque applied by a 3.0-tesla scanner on an ultrasonic motor are not fully known. The force and torque may displace the motor, which is not fully magnetic resonance imaging (MRI)-compatible but can be safely used in MR environments. A suspension apparatus was designed to measure the angles of deflection and rotation applied to the motor by MR magnetic fields. Three orientations and two power states of the motor were assessed inside the MR bore. The displacement force and torque were measured at eight locations with respect to the bore. The displacement force on the motor from 10 cm outside the magnet bore to 20 cm inside the bore ranged from 3 to 7 gF. The experimental measurements are in agreement with the theoretical values. Running the motor altered the force by 1 gF. The force does not significantly change when the MRI scanner is on. Considerable displacement force is applied to the motor, and no deflection torque is observed. Quantified values can be used to solve dynamic equations for robotic mechanisms intended for MRI-guided operations.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".