Experimental Validation of an Elastically Averaged Binary Manipulator for MRI-Guided Prostate Cancer Interventions
Bibliographic record
Abstract
Polymer-based binary robots and mechatronics devices can lead to simple, robust, and cost effective solutions for Magnetic Resonnace Image-guided (MRI) medical procedures. A binary manipulator using 12 elastically averaged air muscles has been proposed for MRI-guided biopsies and brachytherapies procedures used for prostate cancer diagnostic and treatment. In this design, radially-distributed air muscles position a needle guide relatively to the MRI table. The system constitutes an active compliant mechanism where the compliance relieves the over-constraint imposed by the redundant parallel architecture. This paper presents experimental results for repeatability, accuracy, and stiffness of a fully functional manipulator prototype. Results show an experimental repeatability of 0.1 mm for point-to-point manipulation on a workspace diameter of 80 mm. Manipulator average accuracy is 4.7 mm when based on the nominal (uncalibrated) model and improves to 2.1 mm when using a calibrated model. The estimated stiffness at the end-effector is ∼0.95 N/mm and is sufficient to withstand the needle insertion forces without major deflection. Needle trajectories during state change appear to be primarily driven by the system’s elastic energy gradient. The study shows the manipulator prototype to meet its design criteria and to have the potential of becoming an effective and low-cost manipulator for MRI-guided prostate cancer treatment.
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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".