Combining Pulsed and DC Gradients in a Clinical MRI-Based Microrobotic Platform to Guide Therapeutic Magnetic Agents in the Vascular Network
Bibliographic record
Abstract
Magnetic Resonance Navigation (MRN) relies on the use of an upgraded clinical Magnetic Resonance Imaging (MRI) scanner to navigate therapeutic, imaging, or diagnostic magnetic micro-agents in the vascular network. Although the high homogeneous field in the tunnel of the MRI scanner increases the magnetization of the navigable agents towards full saturation, the magnetic gradients superposed on such a high homogeneous field, generated by the Imaging Gradient Coils (IGC) typically used for MR-image slice selection, allow the induction of pulling forces to steer such agents in the targeted branches at the vessel's bifurcations. However, increasing the magnitude of such gradients leads to a significant decrease of the duty cycle, leading to a substantial reduction of the effective steering force being applied. To increase such a duty cycle, a Steering Gradient Coils (SGC) assembly capable of higher magnitudes while maintaining a 100% duty cycle can be installed at the cost of a much slower slew rate. Here, the use and the potential effectiveness of IGC and/or SGC for guiding such agents are briefly investigated on the basis of known specifications and experimental data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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 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".