Dual optimization method of radiofrequency and quasistatic field simulations for reduction of eddy currents generated on 7T radiofrequency coil shielding
Bibliographic record
Abstract
PURPOSE: To optimize the design of radiofrequency (RF) shielding of transmit coils at 7T and reduce eddy currents generated on the RF shielding when imaging with rapid gradient waveforms. METHODS: One set of a four-element, 2 × 2 Tic-Tac-Toe head coil structure was selected and constructed to study eddy currents on the RF coil shielding. The generated eddy currents were quantitatively studied in the time and frequency domains. The RF characteristics were studied using the finite difference time domain method. Five different kinds of RF shielding were tested on a 7T MRI scanner with phantoms and in vivo human subjects. RESULTS: The eddy current simulation method was verified by the measurement results. Eddy currents induced by solid/intact and simple-structured slotted RF shielding significantly distorted the gradient fields. Echo-planar images, B1+ maps, and S matrix measurements verified that the proposed slot pattern suppressed the eddy currents while maintaining the RF characteristics of the transmit coil. CONCLUSION: The presented dual-optimization method could be used to design RF shielding and reduce the gradient field-induced eddy currents while maintaining the RF characteristics of the transmit coil.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".