Development and Evaluation of an Actuated MRI-Compatible Robotic System for MRI-Guided Prostate Intervention
Bibliographic record
Abstract
This paper reports the design, development, and magnetic resonance imaging (MRI) compatibility evaluation of an actuated transrectal prostate robot for MRI-guided needle intervention in the prostate. The robot performs actuated needle MRI-guidance with the goals of providing (i) MRI compatibility, (ii) MRI-guided needle placement with accuracy sufficient for targeting clinically significant prostate cancer foci, (iii) reducing interventional procedure times (thus increasing patient comfort and reducing opportunity for needle targeting error due to patient motion), (iv) enabling real-time MRI monitoring of interventional procedures, and (v) reducing the opportunities for error that arise in manually actuated needle placement. The design of the robot, employing piezo-ceramic-motor actuated needle guide positioning and manual needle insertion, is reported. Results of a MRI compatibility study show no reduction of MRI signal-to-noise-ratio (SNR) with the motors disabled. Enabling the motors reduces the SNR by 80% without RF shielding, but SNR is only reduced by 40% to 60% with RF shielding. The addition of radio-frequency shielding is shown to significantly reduce image SNR degradation caused by the presence of the robotic device. An accuracy study of MRI-guided biopsy needle placements in a prostate phantom is reported. The study shows an average in-plane targeting error of 2.4 mm with a maximum error of 3.7 mm. These data indicate the system's needle targeting accuracy is similar to that obtained with a previously reported manually actuated system, and is sufficient to reliably sample clinically significant prostate cancer foci under MRI-guidance.
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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 | 0.000 | 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".