Using the fringe field of a clinical MRI scanner enables robotic navigation of tethered instruments in deeper vascular regions
Why this work is in the frame
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Bibliographic record
Abstract
Navigating tethered instruments through the vasculatures to reach deeper physiological locations presently inaccessible would extend the applicability of many medical interventions, including but not limited to local diagnostics, imaging, and therapies. Navigation through narrower vessels requires minimizing the diameter of the instrument, resulting in a decrease of its stiffness until steerability becomes unpractical, while pushing the instrument at the insertion site to counteract the friction forces from the vessel walls caused by the bending of the instrument. To reach beyond the limit of using a pushing force alone, we report a method relying on a complementary directional pulling force at the tip created by gradients resulting from the magnetic fringe field emanating outside a clinical magnetic resonance imaging (MRI) scanner. The pulling force resulting from gradients exceeding 2 tesla per meter in a space that supports human-scale interventions allows the use of smaller magnets, such as the deformable spring as described here, at the tip of the instrument. Directional forces are achieved by robotically positioning the patient at predetermined successive locations inside the fringe field, a method that we refer to as fringe field navigation (FFN). We show through in vitro and in vivo experiments that x-ray-guided FFN could navigate microguidewires through complex vasculatures well beyond the limit of manual procedures and existing magnetic platforms. Our approach facilitated miniaturization of the instrument by replacing the torque from a relatively weak magnetic field with a configuration designed to exploit the superconducting magnet-based directional forces available in clinical MRI rooms.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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