Seeking optimal magnetic core shapes for strong gradient generation in Dipole Field Navigation
Bibliographic record
Abstract
Dipole Field Navigation (DFN) has been proposed previously as a promising remote magnetic actuation method for the navigation of microscale agents in vascular networks for the targeted delivery of therapeutics. This method exploits the strong magnetic field of a magnetic resonance imaging scanner to bring the agents at saturation magnetization, and relies on the proper positioning of ferromagnetic cores around the patient in the scanner to induce strong magnetic gradients for navigation. It is currently the only method providing both the high field and high gradient strengths required for the navigation of microparticles at the human scale. Because of the simpler magnetic models of this shape, previous works on DFN considered only spherical ferromagnetic cores. This work investigates different core shapes and shows that the sphere can be outperformed for deep tissue interventions. The gradients around different shapes, calculated by finite element modeling, are compared in the context of typical DFN conditions. Results show that, for the same amount of ferromagnetic material, the hemisphere and the disc generate significantly higher gradients (>50% gains) in deep tissues. Using those shapes instead of spheres would therefore improve the performances of DFN for targeting deep regions in the body.
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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".