Application and experimental validation of an integral method for simulation of gradient-induced eddy currents on conducting surfaces during magnetic resonance imaging
Bibliographic record
Abstract
The time-varying magnetic fields created by the gradient coils in magnetic resonance imaging can produce negative effects on image quality and the system itself. Additionally, they can be a limiting factor to the introduction of non-MR devices such as cardiac pacemakers, orthopedic implants, and surgical robotics. The ability to model the induced currents produced by the switching gradient fields is key to developing methods for reducing these unwanted interactions. In this work, a framework for the calculation of induced currents on conducting surface geometries is summarized. This procedure is then compared to two separate experiments: (1) the analysis of the decay of currents induced upon a conducting cylinder by an insert gradient set within a head only 7 T MR scanner; and (2) analysis of the heat deposited into a small conductor by a uniform switching magnetic field at multiple frequencies and two distinct conductor thicknesses. The method was shown to allow the accurate modeling of the induced time-varying field decay in the first case, and was able to provide accurate estimation of the rise in temperature in the second experiment to within 30% when the skin depth was greater than or equal to the thickness of the conductor.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".