Triple‐tuned birdcage and single‐tuned dipole array for quadri‐nuclear head MRI at 7 T
Bibliographic record
Abstract
Abstract Purpose The purpose of this work was to design and build a coil for quadri‐nuclear MRI of the human brain at 7 T. Methods We built a transmit/receive triple‐tuned (45.6 MHz for H, 78.6 MHz for Na, and 120.3 MHz for P) quadrature four‐rod birdcage that was geometrically interleaved with a transmit/receive four‐channel dipole array (297.2 MHz for H). The birdcage rods contained passive, two‐pole resonant circuits that emulated capacitors required for single‐tuning at three frequencies. The birdcage assembly also included triple‐tuned matching networks, baluns, and transmit/receive switches. We assessed the performance of the coil with quality factor (Q) and signal‐to‐noise ratio (SNR) measurements, and performed in vivo multinuclear MRI and MR spectroscopic imaging (MRSI). Results Q measurements showed that the triple‐tuned birdcage efficiency was within 33% of that of single‐tuned baseline birdcages at all three frequencies. The quadri‐tuned coil SNR was 78%, 59%, 44%, and 48% lower than that of single or dual‐tuned reference coils for H, H, Na, and P, respectively. Quadri‐nuclear MRI and MRSI was demonstrated in brain in vivo in about 30 min. Conclusion While the SNR of the quadruple tuned coil was significantly lower than dual‐ and single‐tuned reference coils, it represents a step toward truly simultaneous quadri‐nuclear measurements.
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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) | 0.000 | 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) | 0.001 | 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".