SU‐CC‐J‐6C‐03: Factors Affecting Remote Control Endovascular Catheter Steering for IMRI
Bibliographic record
Abstract
Purpose: Current applied to a wire solenoid wound at the tip of an endovascular catheter can be used to remotely steer a catheter in interventional MRI. In this study, we attempted to 1) optimize catheter visualization by comparing “real‐time” pulse sequences, 2) derive and verify an equation that characterizes the relationship between the number of solenoid turns, applied current, catheter stiffness, and resulting catheter tip deflections. Method and Materials: Solenoids of 50, 100, 150 turns were wound on separate 1.8 F (using 44 AWG magnet wire) and 5F catheters (using 37 AWG magnet wire). Varying currents were applied using a DC power supply in the MRI control room. Images were obtained with 1.5 T scanner with the distal catheter suspended at 90 degrees to the main magnetic field in a water bath on the scanner bed, using ssFSE, spiral SPGR, FSPGR, FIESTA, and GRE‐EPI pulse sequences. Deflection angles were measured on acquired sagittal images using eFilm. Results: ssFSE and FIESTA images had the highest SNR and the lowest sensitivity to local field inhomogeneity artifact. The deflection angleθ was predicted by the equation, θ = [nIAB/k]sin(γ‐θ), where n is solenoidal turns, I is current, A is area, B is the scanner magnetic field, k is related to the catheter elastic modulus, and γ is the initial angle between the catheter and B (R2 = 0.9671−0.9875). For a 1.8 F catheter and 60 mA applied, deflection was 31.5, 36, and 45.5 degrees from baseline for 50, 100, and 150 turns, respectively. Less flexible catheters required currents over 800 mA to cause deflection.over 20 degrees. Conclusion: SsFSE and FIESTA real‐time pulse sequences are optimal for visualization of catheter tip deflection. The number of solenoidal turns, applied current, and catheter stiffness are important considerations for remote steering of endovascular catheters in iMRI.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".