Ultrasound-Guided Model Predictive Control of Needle Steering in Biological Tissue
Bibliographic record
Abstract
In needle-based medical procedures, beveled tip flexible needles are steered inside soft tissue to reach the desired target locations. In this paper, we have developed an autonomous image-guided needle steering system that enhances targeting accuracy in needle insertion while minimizing tissue trauma. The system has three main components. First is a novel mechanics-based needle steering model that predicts needle deflection and accepts needle tip rotation as an input for needle steering. The second is a needle tip tracking system that determines needle deflection from the ultrasound images. The needle steering model employs the estimated needle deflection at the present time to predict needle tip trajectory in the future steps. The third component is a nonlinear model predictive controller (NMPC) that steers the needle inside the tissue by rotating the needle beveled tip. The MPC controller calculates control decisions based on iterative optimization of the predictions of the needle steering model. To validate the proposed ultrasound-guided needle steering system, needle insertion experiments in biological tissue phantoms are performed in two cases–with and without obstacle. The results demonstrate that our needle steering strategy guides the needle to the desired targets with the maximum error of 2.85[Formula: see text]mm.
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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".