MR compatible actuation for medical instrumentation
Bibliographic record
Abstract
Surgical robots and especially their subclass of image-guided systems require special design, construction and control compared to industrial types, due to the special requirements of the medical and imaging environments. Imager compatibility raises significant engineering challenges for the development of robotic manipulators with respect to imager access, safety, ergonomics, and above all the non-interference with the functionality of the imager. These apply to all known medical imaging types, but are especially challenging for achieving compatibility with the class of magnetic resonance imaging (MRI) systems. Even though a large majority of robotic components may be redesigned to be constructed of MRI compatible materials, for other components such as the motors used in actuation, prescribing MRI compatible materials alone is not sufficient. The electromagnetic motors most commonly used in robotic actuation, for example, are incompatible by principle. We report here an important achievement in MRI instrumentation, a new type of pneumatic motor, PneuStep (Stoianovici et al., 2007), specifically developed for MrBot, the first pneumatic, fully actuated MR robotic system that can be located within the scanner alongside the patient and operating under remote control based on the images.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".