Steering of aggregating magnetic microparticles using propulsion gradients coils in an MRI Scanner
Bibliographic record
Abstract
Upgraded gradient coils can effectively enhance the MRI steering of magnetic microparticles in a branching channel. Applications of this method include MRI targeting of magnetic embolization agents for oncologic therapy. A magnetic suspension of Fe(3)O(4) magnetic particles was injected inside a y-shaped microfluidic channel. Magnetic gradients of 0, 50, 100, 200, and 400 mT/m were applied to the magnetic particles perpendicularly to the flow by a custom-built gradient coil inside a 1.5-T MRI scanner. Measurement of the steering ratio was performed both by video analyses and quantification of the mass of the particles collected at each outlet of the microfluidic channel, using atomic absorption spectroscopy. Magnetic particles steering ratios of 0.99 and 0.75 were reached with 400 mT/m gradient amplitude and measured by video analyses and atomic absorption spectroscopy, respectively. Experimental data shows that the steering ratio increases with higher magnetic gradients. Moreover, theory suggests that larger particles (or aggregates), higher magnetizations, and lower flows can also be used to improve the steering ratio. The technological limitation of the approach is that an MRI gradient amplitude increase to a few hundred milliteslas per meter is needed. A simple analytical method based on magnetophoretic velocity predictions and geometric considerations is proposed for steering ratio calculation.
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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) | 0.000 | 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 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".