Design of a Binary Needle Manipulator Using Elastically Averaged Air Muscles for Prostate Cancer Treatments
Bibliographic record
Abstract
Affecting 1 out of 8 subjects in the U.S., prostate cancer is the most common form of cancer in men. Current medical procedures could be improved by the development of an MRI compatible (Magnetic Resonance Imaging) needle manipulator system, to precisely reach small tumors (<5 mm) inside the prostate. This paper presents and analyzes the potential of such a needle manipulator concept, based on hyper-redundant binary air muscles, all controlled by MRI compatible valves (e.g. piezoelectric or dielectric elastomer actuators). The proposed manipulator uses 12 polymer air muscles, each driven by 2 different actuation pressures, offering a total of 4096 (212) discrete needle positions. Based on a hyperelastic continuum mechanics air muscle model, a theoretical manipulator design is used to evaluate clinically-relevant design metrics, such as size, stiffness, workspace, accuracy and sensitivity. In this model, the manipulator’s equilibrium configuration (for a given set of input pressures and applied forces) is found by minimizing the system’s potential energy. The model capability is verified experimentally by a one degree of freedom (DOF) prototype. Simulation results show that the proposed elastically averaged air muscle concept can meet all design requirements. In particular, the needle workspace of about 70 mm by 80 mm entirely covers the prostate area, where targets are accurately reachable within 0.7 mm. Also, the pneumatic actuators can generate high forces leading to a system stiffness of ∼4.6 N/mm at the needle tip. Such stiffness can adequately sustain the needle during insertion with minimal deflection to guaranty accurate positioning.
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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".