Event-Triggered 3D Needle Control Using a Reduced-Order Computationally Efficient Bicycle Model in a Constrained Optimization Framework
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Bibliographic record
Abstract
Long flexible needles used in percutaneous procedures such as biopsy and brachytherapy deflect during insertion, thus reducing needle tip placement accuracy. This paper presents a surgeon-in-the-loop system to automatically steer the needle during manual insertion and compensate for needle deflection using an event-triggered controller. A reduced-order kinematic bicycle model incorporating needle tip measurement data from ultrasound images is used to determine steering actions required to minimize needle deflection. To this end, an analytic solution to the reduced-order bicycle model, which is shown to be more computationally efficient than a discrete-step implementation of the same model, is derived and utilized for needle tip trajectory prediction. These needle tip trajectory predictions are used online to optimize the insertion depths (event-trigger points) for steering actions such that needle deflection is minimized. The use of the analytic model and the event-triggered controller also allows for limiting the number and extent of needle rotations (to reduce tissue trauma) in a constrained optimization framework. The system was tested experimentally in three different ex-vivo tissue phantoms with a surgeon-in-the-loop needle insertion device. The proposed needle steering controller was shown to keep the average needle deflection within 0.47 [Formula: see text] 0.21[Formula: see text]mm at the final insertion depth of 120[Formula: see text]mm.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it