Magnetic Resonance Navigation of a Bead Inside a Three-Bifurcation PMMA Phantom Using an Imaging Gradient Coil Insert
Bibliographic record
Abstract
This paper reports the successful navigation of a 1-mm Chrome-Steel bead along three consecutive polymethyl methacrylate channels inside the bore of a 1.5-T magnetic resonance imaging (MRI) scanner. The bead traveled at a mean velocity of 14 cm·s-1. This was accomplished using an imaging gradient coil (IGC) insert located inside the MRI tube. While targeting one side of a bifurcation has been previously demonstrated using unidirectional gradient coils, this is the first time that magnetic resonance navigation (MRN) of a bead along consecutive channels is reported. Experimental results confirm that a clinical regular MRI can be used to propel a 1-mm device. In addition, when used at maximum power, IGC temperature rise becomes a serious issue that can ultimately damage the insert and limit the overall performance. Consequently, this paper aims to give some insight into coil temperature management for IGC-assisted procedures. A 33-min thermal stress test was carried out using 100% of the IGC power. Steady-state oscillation can be reached by interleaving propulsion periods with cooling periods, thus enabling longer propulsion procedures. Experimental data showed that the cooling time can be used for imaging purposes with no performance loss, thus enabling MRN-assisted procedures with multiplexed particle distribution assessment.
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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 | 0.000 | 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".