A study on observed ultrasonic motor-induced magnetic resonance imaging (MRI) artifacts
Bibliographic record
Abstract
BACKGROUND: The safe performance of magnetic resonance imaging (MRI)-guided robot-assisted interventions requires full control and high precision of assistive devices. Because many currently available tools are not MRI-compatible, the characterization of existing tools and development of new ones are necessary. The purpose of this research is to identify and minimize the image artifacts generated by a USM in MR images. METHODS: The behavior of an ultrasonic motor (USM), the most common MRI-safe actuator, in a high-field scanner was investigated. The motor was located in three orientations with respect to the bore axis with the power on or off. The induced image artifacts were compared across four sequences. Three artifact reduction methods (employing ultrashort sequences, slice thickness reductions, and bandwidth increments) were tested. RESULTS: Signal voids, pileups, and geometric distortions were observed when the motor was off. The artifact size was minimal when the motor shaft was aligned with the bore axis. In addition to the above artifacts, zipper and motion artifacts were noted when the motor was running, and these artifacts increased with increasing motor speed. Increasing the bandwidth slightly reduced the artifacts. However, decreasing the slice thickness from 5 mm to 3 mm and from 5 mm to 1 mm reduced artifact size from 30% to 40% and from 60% to 75%, respectively. CONCLUSION: The image artifacts were due to the non-homogenous nature of the static and gradient fields caused by the motor structure. The operating motor interferes with the RF field, causing zipper and motion artifacts.
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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 | 0.004 |
| 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 | 0.000 | 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".