Dual-tuned floating solenoid balun for multi-nuclear MRI and MRS
Bibliographic record
Abstract
Common-mode currents can degrade the RF coil performance and introduce potential safety hazards in MRI. Baluns are the standard method to suppress these undesired common-mode currents. Specifically, floating baluns are preferred in many applications because they are removable, allow post-installation adjustment and avoid direct soldering on the cable. However, floating baluns are typically bulky to achieve excellent common-mode suppression, taking up valuable space in the MRI bore. This is particularly severe for multi-nuclear MRI/MRS applications, as two RF systems exist. In this work, we present a novel dual-tuned floating balun that is fully removable, does not require any physical connection to the coaxial cable , and has a significantly reduced footprint. The floating design employs an inductive coupling between the cable solenoid and a floating solenoid resonator rather than a direct physical connection. Unlike the previous floating solenoid balun, this balun employs a two-layer design further to improve the mutual coupling between the two solenoids. A pole-insertion method is used to suppress common-mode currents at two user-selectable frequencies simultaneously. Bench testing of the fabricated device at 7 T demonstrated high common-mode rejection ratios at Larmor frequencies of both 1 H and 23 Na, even with a compact dimension (diameter 18 mm and length 12 mm). This balun's removable, compact, and multi-resonant nature enables light-weighting, allows more coil elements , and improves cable management for advanced multi-nuclear MRI/MRS systems.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".