Wireless Resonators With Coupled Versus Decoupled Units: Which Enhances Local <scp>SNR</scp> of <scp>RF</scp> Receive Arrays Better?
Bibliographic record
Abstract
PURPOSE: To compare two identically sized wireless resonator designs, one with strongly coupled units and the other with decoupled units, for their ability to enhance receive performance in MRI when used with local receive arrays. METHODS: Both wireless resonator designs were fabricated and experimentally evaluated for detuning efficiency, SNR improvements, and parallel imaging performance (g-factor) at 1.5 T. They were used alongside a 12-channel head receive array, with the standard body coil serving as the RF transmitter. RESULTS: Experimental data showed that the wireless resonator with decoupled units consistently outperformed that with coupled units, with up to threefold improvement in SNR and a reduction of maximum/average g-factor from 4.6/1.8 to 3.1/1.3. Notably, compared to the original receive array (maximum/average: 3.9/1.7), the decoupled design further improved the g-factor, highlighting superior performance in accelerated imaging. CONCLUSION: Wireless resonators with decoupled units offer significant advantages in improving MRI image quality and parallel imaging performance over their coupled counterparts. Their ease of detuning and pronounced gains in SNR and g-factor make them a compelling choice for wireless resonator designs.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".