A fully actuated robotic assistant for MRI-guided prostate biopsy and brachytherapy
Bibliographic record
Abstract
Intra-operative medical imaging enables incorporation of human experience and intelligence in a controlled, closed-loop fashion. Magnetic resonance imaging (MRI) is an ideal modality for surgical guidance of diagnostic and therapeutic procedures, with its ability to perform high resolution, real-time, high soft tissue contrast imaging without ionizing radiation. However, for most current image-guided approaches only static pre-operative images are accessible for guidance, which are unable to provide updated information during a surgical procedure. The high magnetic field, electrical interference, and limited access of closed-bore MRI render great challenges to developing robotic systems that can perform inside a diagnostic high-field MRI while obtaining interactively updated MR images. To overcome these limitations, we are developing a piezoelectrically actuated robotic assistant for actuated percutaneous prostate interventions under real-time MRI guidance. Utilizing a modular design, the system enables coherent and straight forward workflow for various percutaneous interventions, including prostate biopsy sampling and brachytherapy seed placement, using various needle driver configurations. The unified workflow compromises: 1) system hardware and software initialization, 2) fiducial frame registration, 3) target selection and motion planning, 4) moving to the target and performing the intervention (e.g. taking a biopsy sample) under live imaging, and 5) visualization and verification. Phantom experiments of prostate biopsy and brachytherapy were executed under MRI-guidance to evaluate the feasibility of the workflow. The robot successfully performed fully actuated biopsy sampling and delivery of simulated brachytherapy seeds under live MR imaging, as well as precise delivery of a prostate brachytherapy seed distribution with an RMS accuracy of 0.98mm.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".