A Two-Body Rigid/Flexible Model of Needle Steering Dynamics in Soft Tissue
Bibliographic record
Abstract
Robotics-assisted needle steering can enhance targeting accuracy in percutaneous interventions. This paper presents a novel dynamical model for robotically controlled needle steering. This is the first model that predicts both needle shape and tip position in soft tissue, and accepts needle insertion velocity, needle 180° axial rotation, and needle base force/torque as inputs. A hybrid formulation of needle steering dynamics in soft tissue is presented, which considers the needle as a two-body rigid/flexible coupled system composed of a moving, discrete, and rigid part attached to a vibrating compliant part that is subject to external excitation forces. The former is the carrier representing the surgeon's hand or the needle inserting robot, while the latter is a beam modeling the continuous deflection of the needle inside tissue. A novel time-delayed tissue model and a fracture mechanics-based model are developed to model the tissue reaction forces and cutting force at the needle tip, respectively. Experiments are performed on synthetic and ex vivo animal tissues to identify the model parameters and validate the needle steering model. The maximum error of the 2-D model in predicting the needle tip position in the insertion plane was 1.59 mm in the case of no axial rotation and 0.74 mm with axial rotation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".