Design and Manufacturing of Embedded Air-Muscles for a Magnetic Resonance Imaging Compatible Prostate Cancer Binary Manipulator
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) compatible robots can assist physicians with the insertion of biopsy needles and needle-like therapeutic instruments directly into millimeter-size tumors, using MR images as feedback. However, MRI systems present a challenging environment with high magnetic fields and limited space, making the development of MRI-compatible robots complex. This paper presents an MRI-compatible pneumatic actuation technology consisting of molded polymer structures with embedded air-muscles operated in a binary fashion. Along with its good positioning accuracy, the technology presents advantages of compactness, perfect MRI-compatibility, simplicity and low cost. Here, we specifically report the design and validation of a transperineal prostate cancer manipulator prototype that has 20 embedded air-muscles distributed in four star-like polymer structures. These compliant structures are made of silicone elastomer, using lost-core injection molding. Low motion hysteresis and good precision are achieved by designing molded joints that eliminate sliding surfaces. An effective design method for such embedded polymer air-muscles is proposed, using a manipulator model and four air-muscle design models: geometrical, finite elements, uniaxial analytic, and experimental. Binary control of each air-muscle ensures stability and accuracy with minimized costs and complexity. The prototype is found MRI-compatible with no observable effects on the signal-to-noise ratio and, with appropriate image feedback, is found to reach targets with precision and accuracy under 0.5 mm. The embedded approach reveals to be a key feature since it reduces hysteresis errors by a factor of ≈7 compared to a previous nonembedded version of the manipulator. The successful validation of this binary manipulator opens the door to a new design paradigm for low cost and highly capable pneumatic robots.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".