Real-time tracking of a bevel-tip needle with varying insertion depth: Toward teleoperated MRI-guided needle steering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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