Surgeon-in-the-Loop 3-D Needle Steering Through Ultrasound-Guided Feedback Control
Bibliographic record
Abstract
Needle deflection during certain minimally invasive percutaneous procedures, such as prostate biopsy or brachytherapy, is undesired and can be reduced through the use of feedback control. This letter uses a depth-dependent reduced-order three-dimensional (3-D) nonholonomic model of needle tip motion to propose a needle deflection controller that works in a surgeon-in-the-loop fashion, where the surgeon is in charge of needle insertion, and the feedback controller is responsible for keeping the needle on its desired trajectory. The controller is based on a continuous-time control law that asymptotically brings needle deflection to zero and is shown to remain effective even when the magnitude of the needle rotation velocity is limited. Limiting of the needle rotational velocity is due to practical considerations such as to reduce tissue damage during insertion and to show a measure of velocity-independence of the controller when high insertion speeds would require unfeasibly fast rotations. The velocity-limited controller is evaluated in three different exvivo tissue samples in a total of 30 needle insertion trials using real-time needle deflection measurements from ultrasound images. The exvivo results show an average absolute needle tip deflection of 0.54 mm away from the target location at a depth of 120 mm, and an average needle tip deflection of 0.36 mm away from the desired target axis throughout the entire insertion length.
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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 | 0.001 |
| 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 | 0.000 | 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".