Design and Manufacturing of Embedded Pneumatic Actuators for an MRI-Compatible Prostate Cancer Binary Manipulator
Bibliographic record
Abstract
Magnetic Resonance Imaging (MRI) compatible robots can assist physicians in precisely inserting biopsy needles or therapeutic instruments directly into millimeter-size tumors using MR imaging feedback. MRI systems although present a challenging environment, including 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-muscle, operated in a binary fashion. While having 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 having 20 embedded air-muscles distributed in four star-like polymer structures. Structures are made of silicone elastomer, using lost-core injection molding. The molded compliant joints of the muscles eliminate sliding surfaces, for low motion hysteresis and good repeatability. A simple and effective two-level design method for polymer air-muscles is proposed, using a manipulator model and three muscle models: geometrical, finite elements and uniaxial analytic. Binary control of each air-muscle assures stability and accuracy with minimized costs and complexity. The manipulator is tested MRI-compatible with no effects on the signal-to-noise ratio and, with appropriate image feedback, reaches targets with repeatability and accuracy under 0.5 mm. The embedded approach reveals to be a key feature since it reduces hysteresis errors by a factor of 6.6 compared to a previous non-embedded 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, specifically for the intra-MRI manipulation.
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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 | 0.000 | 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 | 0.000 | 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".