MRI-compatibility enhancements to USM performance for interventional devices in MR environments
Bibliographic record
Abstract
The safe performance of magnetic resonance imaging (MRI)-guided robot-assisted interventions requires full control and high precision of assistive devices and tools. Because many currently available tools are not MRI-compatible, the characterization of existing tools and development of new ones are necessary. The behaviour of an ultrasonic motor (USM), the most common MRI-safe actuator, in a high-field (3T) MRI scanner was investigated. To characterize the axial force generated by the USM, a generic MRI-compatible force sensor (MCFS) was developed. USM effects on MCFS performance were examined under various sensor load and motion states while the scanner was on and off. The effects of the USM on MR images were investigated. The shift in the resonance frequency of water protons induced by the USM was measured. Image artifacts caused by the USM were classified. Geometric distortions on images and degradation of the signal-to-noise ratio were assessed. Compensation methods to reduce image artifacts were developed. To ascertain the potential risks of USM and the degree of MRI compatibility, the displacement force and deflection torque generated by the scanner on the USM were characterized. The effect of temperature increase caused by the scanner was also evaluated. Temperature increase causing degradation of USM output characteristics, can be reduced by the use of the developed USM case. In conclusion, the MCFS can precisely operate in the vicinity of USMs and in a high-field scanner. This research shows that it is not necessary to keep the USMs at a distance from the MR scanner to address compatibility issues. Furthermore, this thesis demonstrates that the use of compensation methods to reduce image artifacts and the recommended use of silicon carbide to reduce temperature increase due the magnetic field enhance the USMâ s compatibility and lead to safe, accurate, and reliable operation of the USM in high field MRI.
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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 | 0.000 | 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".