Design and Experimental Assessment of an Elastically Averaged Binary Manipulator Using Pneumatic Air Muscles for Magnetic Resonance Imaging Guided Prostate Interventions
Bibliographic record
Abstract
Early diagnostic and treatment of prostate cancer could be achieved using magnetic resonance imaging (MRI) to improve tumor perceptibility. Nonetheless, performing intra-MRI interventions present significant challenges due to intense magnetic fields and limited patient access. This paper presents an MRI-compatible manipulator using elastically averaged binary pneumatic air muscles (PAMs) to orient a needle into a targeted region of the prostate under the command of a physician. The proposed manipulator is based on an all-polymer compliant mechanism designed to make a completely MRI-compatible positioning system. A model based on the PAMs deformation energy is used to design the manipulator so that its discrete workspace, stiffness, and size meet clinically relevant design requirements. The model is also used to study the motion of the device during a state shift. A laboratory prototype of the device shows that the covered workspace, stiffness, and size of the manipulator can meet clinical requirements. Repeatability and accuracy are also acceptable with values of 0.5 mm and 1.7 mm, respectively. Finally, the manipulator’s behavior during state shift describes a hook-shaped motion that is both analytically predicted and experimentally observed.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".