A MRI-based integrated platform for the navigation of microdevices and microrobots
Bibliographic record
Abstract
Magnetic Resonance Navigation (MRN) aims at navigating artificial or synthetic untethered micro-devices and microrobots using an upgraded clinical Magnetic Resonance Imaging (MRI) system. For larger MRI-based navigated entities, past experiments proved that software-based upgrades only were sufficient. But for microrobots with an overall diameter of only a few tens of micrometers for travelling in narrower blood vessels, hardware upgrades need to be added to the MR scanner, resulting in a MRN system capable of generating 3D magnetic propulsion gradients on the microrobots well above the ones that could be generated by a clinical MRI scanner relying on software-upgrades only. But with the variety of models of clinical scanners coped with many versions of related operating software dedicated to MR imaging, implementing such upgrades that could operate with these scanners becomes a real challenge. As such, a new MRN platform architecture independent of the types of MR scanners is proposed and preliminary experimental data validating the potential of such microrobotic navigation system architecture integrated with a commercially available scanner are reported. The expected steering capabilities of the platform were evaluated initially using a special probe in the form of a magnetic catheter mimicking an anisotropic microrobot. Such special probe also allowed for easier recordings of the gradient steering force that would be induced on such microrobot while validating the technique for catheter steering which is also an important aspect since catheterization is often used for releasing the microrobots in larger arteries. Similarly, MR tracking of the same microrobot was also validated with the new system, confirming that tracking feedback data can be gathered in order to perform closed-loop navigation control.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".