A Piezoelectric Robotic System for MRI Targeting Assessments of Therapeutics During Dipole Field Navigation
Bibliographic record
Abstract
Dipole field navigation (DFN) is a method that has been developed to deliver therapeutics toward tumoral regions by navigating microcarriers in the vascular network. To do so, DFN distorts the high uniform magnetic field of a clinical magnetic resonance imaging (MRI) scanner using precisely located ferromagnetic balls to create magnetic gradients to implement the directional forces required to navigate magnetically saturated therapeutic microcarriers along a planned trajectory in the vasculature. Such local distortions of the magnetic field prevent MRI-based targeting assessments. As such, a system must be put in place to precisely move the ferromagnetic balls back-and-forth to alternate between MRI targeting assessment and DFN. Here, a piezoelectric actuation system is proposed. In vitro experiments conducted inside the bore of a 3T clinical MRI scanner show the feasibility for reliable targeting assessments with magnetic distortions sufficient to achieve a 100% success rate of magnetic microparticles being navigated through a predefined target branch at a bifurcation. Results show a 21.6% decrease in SNR with a maximum value of 2.2% MR-image distortion and a faintly visible image artifact after the piezoelectric system moved the soft ferromagnetic balls in the MRI targeting assessment position.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".