Investigation of Magnetic Field Gradient Waveforms in the Presence of a Metallic Vessel in Magnetic Resonance Imaging Through Simulation
Bibliographic record
Abstract
We have previously established the ability to characterize eddy currents and their impact on the magnetic fields in metal vessel magnetic resonance imaging applications through comparison with measured data. We now use simulation to investigate the spatiotemporal characteristics of eddy currents and their magnetic fields encountered in metal vessel magnetic resonance imaging that may not easily be experimentally verified. Simulations using CST EM Studio have revealed the impact of an offset in the positioning of the metal vessel in a superconducting magnet bore as well as the consequences of the different amplitude magnetic gradients employed during imaging experiments. Furthermore, we investigate the response of the metal vessel to magnetic field gradients generated from nonplanar and planar gradient coil geometries (encountered in superconducting and permanent magnet-based systems). Establishing the basic electromagnetic properties of the metal vessel in a magnetic resonance sense through simulation permits us to replace our simple metal vessel with a more sophisticated rock core holder design and investigate its properties.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".