Measuring the Temperature Increase of an Ultrasonic Motor in a 3-Tesla Magnetic Resonance Imaging System
Bibliographic record
Abstract
This paper aims to evaluate the temperature increase caused by a 3.0-T magnetic resonance imaging (MRI) system on an ultrasonic motor (USM) used to actuate surgical robots in the MRI environment. Four fiber-optic temperature sensors were attached to the USM. Temperature was monitored outside the five-Gauss boundary and then inside the bore for 20 min while the USM was powered on. The USM temperature was tested for two states of the scanner, “off” and “on”, by employing common clinical imaging sequences and echo planar imaging sequences. The USM showed a slight temperature increase while operating in the static field of the MRI. A considerable temperature increase (~10 °C) was observed when the scanner was on. The temperature increased to 60 °C, which is beyond the acceptable safe temperature and can result in thermal burns. Most of the temperature increase (80%) was due to effects of the static field on the motion of the rotating parts of the motor, while the remainder (20%) derived from heat deposited in the conductive components of the USM due to radiofrequency pulses and gradient field changes. To solve the temperature increase, the metal components of the USM’s case can be replaced by silicon carbide.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".