Magnetic Fringe Field Navigation of a guidewire based on Thin Plate Spline modeling
Bibliographic record
Abstract
Fringe Field Navigation (FFN), a method first introduced by our group, aims at providing a high directional pulling force on a magnetic object such as a magnetic tip of a guidewire or other instruments. In a clinical setting, the pulling force is typically produced by the strong fringe field generated by the superconducting magnet of a clinical Magnetic Resonance Imaging (MRI) scanner. Because it is impossible or practical to move such a bulky magnet, directional changes are performed by robotically moving the patient in the magnetic fringe field outside the scanner. To do so, a homogenous transformation for each point in a set of discrete points in the magnetic field space must first be done and used to determine the position of the robotic manipulator to enable the steering of a guidewire equipped with a magnetic tip towards the desired direction. We used the Thin Plate Spline (TPS) method to model the magnetic field and to estimate the direction of the magnetic field required to navigate the guidewire along a predetermined path. We also propose guidelines for the sampling of the magnetic field to produce a more accurate TPS function. To prove the concept, we applied FFN on a small experimental prototype using a small permanent magnet and a robotic manipulator to steer a guidewire inside a phantom. The preliminary results suggest that the same approach could be scaled up for clinical applications taking advantage of the much stronger magnetic field generated by the superconducting magnet of already available MRI scanners.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".