An MRI-Compatible Needle Manipulator Concept Based on Elastically Averaged Dielectric Elastomer Actuators for Prostate Cancer Treatment: An Accuracy and MR-Compatibility Evaluation in Phantoms
Bibliographic record
Abstract
A parallel manipulator concept using bistable polymer actuators has been developed to perform prostate cancer biopsy, and deliver therapy within the bore of a magnetic resonance imaging (MRI) scanner. The dielectric elastomer actuators (DEAs) used in this manipulator concept are promising for MRI-compatible robotics because they do not interfere with the high magnetic fields of MRI while having good mechanical performance and being low cost. In the past, these actuators have been plagued by robustness problems when used in a continuous manner. Recent studies show that reliability significantly improves when DEAs are used in a bistable manner, as proposed here. This paper investigates the potential of the proposed manipulator concept by evaluating the positioning accuracy and MRI compatibility of a laboratory prototype, developed for clinically relevant design criteria. An analytical model of the manipulator kinematics is presented. Analytical and experimental results validate that the proposed technology can provide an accurate needle placement required to perform prostate cancer treatments. The prototype’s MRI compatibility is validated in a 3 T clinical MRI scanner. The parallel manipulator concept using bistable polymer actuators is shown to be a viable approach to perform MRI-guided needle insertions for prostate cancer biopsy and therapy.
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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) | 0.001 | 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 | 0.001 |
| 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".