A Highly Compact, Multi-Material, Fluid Powered Actuation System for MRI-Guided Surgical Intervention
Bibliographic record
Abstract
Abstract This paper presents an inherently safe, compact, 3D-printed, fluid-powered stepper actuation system enabling surgical precision within the demanding and confined space of a magnetic resonance imaging (MRI) scanner. The intense magnetic field and limited workspace of an MRI excludes the use of traditional, ferromagnetic robotics. Additionally, scanner image quality is sensitive to interference, creating a strict constraint on the electromagnetic and ferromagnetic signature of the actuator. While non-ferromagnetic, fluid powered actuators exist, they are often bulky and difficult to control. Using high resolution, material jetting technology, we’re able to 3D print small, standalone multi-material designs with variable rigidity. Leveraging these advances in additive manufacturing technology, we have developed a modular set of miniature flexible fluidic actuators (FFAs). These actuators are capable of translating, rotating, and gripping a slender rod and are inherently safe to valve, control, or pressure faults, due to the stepping sequence. Using a specific clinical application as a use case, we assembled these components into a highly compact needle steering system for MRI-guided neurosurgery. This two-degree-of-freedom actuation system is driven pneumatically, taking advantage of sterile, hospital instrument air and is electromagnetically transparent to the MRI scanner. In addition to detailing the actuator system design, this paper also demonstrates a robust, nonlinear control strategy for precision sub-step motion control. This paper reports the actuator system’s operating pressure and bandwidth, translational and rotational accuracy, and maximum force and torque capabilities.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".