Adaptive Quasi-Static Modelling of Needle Deflection During Steering in Soft Tissue
Bibliographic record
Abstract
In this letter, we present a model for needle deflection estimation in soft tissue. The needle is modelled as a vibrating compliant cantilever beam that experiences forces applied by the tissue as it is inserted. Each of the assumed vibration modes are associated with a weighting coefficient whose magnitude is calculated using the minimum potential energy method. The model only requires as input the tissue stiffness and needle-tissue cutting force. Contributions of this letter include the estimation of needle-tissue contact forces as a function of the tissue displacement along the needle shaft, while allowing for multiple bends of the needle. The model is combined with partial ultrasound image feedback in order to adaptively calculate the needle-tissue cutting force as the needle is inserted. The image feedback is obtained by an ultrasound probe that follows the needle tip and stops at an appropriate position to avoid further tissue displacement. Images obtained during early stages of the insertion are used to predict the deflection of the needle further along the insertion process. Experimental results in biological and phantom tissue show an average error in predicting needle deflection of 0.36 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".