A prototype of injector to control and to detect the release of magnetic beads within the constraints of multibifurcation magnetic resonance navigation procedures
Bibliographic record
Abstract
Purpose An injector equipped with a bead capture and a bead detection system is presented. In the context of magnetic resonance navigation (MRN), in which MRI gradients are used to steer intravascular therapeutic carriers, fast and reliable injection is essential. In this paper, we present a prototype of injector to control and to detect the release of magnetic beads. Methods The injector relies on two distinct subsystems: (1) the capture subsystem, which creates local magnetic force to stop the flow of magnetic beads; and (2) the detection subsystem, which detects flowing beads and generates a trigger signal to start MRI gradient pulses. Both systems rely on small microcoils wound on the tubing. Results Five‐turn microcoils show the best compromise between size and performance. Less than 5 mW of power is required to capture 0.8‐mm beads moving in a flow above 5 mL min−1 or when a gradient above 200 mT m−1 is applied. The detection system is not sensitive to noise and detects every 0.8‐mm bead in flow rates up to 14 mL m−1. Conclusion The prototype of injector shows performance above the requirements inherent to magnetic resonance navigation. This system is a step toward in vivo multibifurcation MRN. Magn Reson Med 77:444–452, 2017. © 2016 Wiley Periodicals, Inc.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".