Real-time tracking of a bevel-tip needle with varying insertion depth: Toward teleoperated MRI-guided needle steering
Bibliographic record
Abstract
This study presents one of the enabling technologies for teleoperated bevel-tip needle steering under real-time MRI guidance i.e. capability of tracking the needle with higher accuracy and bandwidth than real-time MRI. Three fibers, each with three Fiber Bragg Gratings (FBG) were embedded into a 0.6 mm inner stylet of a 20G MRI-compatible biopsy needle. The axial force caused by the bevel-tip was considered in the analysis using beam-column theory. Since the insertion depth is varying, the minimum number of sensors and their optimal locations in the fibers were determined such that the tip position error estimation is below 0.5 mm for all insertion depths. A practical and accurate calibration method for the apparatus is presented. The instrumented needle was fabricated to fit in the needle driver unit of a MRI-compatible needle steering robot. The tracking apparatus was calibrated, including compensation for temperature changes in tissue during insertion. Experimental results showed needle tip tracking error below 0.5 mm at different insertion depths. Real-time 3D shape of the needle was visualized in 3D Slicer yielding navigation of the needle in real-time.
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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 | 0.001 |
| 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".