How thin can you go? Performance of thin copper and aluminum RF coil conductors
Bibliographic record
Abstract
PURPOSE: To evaluate the impact of emerging conductor technology on RF coils. Performance and resulting image quality of thin or alternate conductors (eg, aluminum instead of copper) and thicknesses (9-600 μm) are compared in terms of SNR. METHODS: Eight prototype RF coils (15 cm × 15 cm square loops) were constructed and bench-tested to measure quality factor. The coils used 6-mm-wide conducting strips of either copper or aluminum of a few different thicknesses (copper: 17, 32, 35, 127, 600 μm; aluminum: 9, 13, 20, 127 μm) on acetate projector sheets for backing. Corresponding image SNR was measured at 0.48 tesla (20.56 MHz). RESULTS: The coils spanned a range of unloaded quality factors from 89 to 390 and a fivefold range of losses. The image SNRs were consistent with the coils' bench-measured efficiencies (0.33-0.73). Thin aluminum conductors (9 μm) led to the highest reduction in SNR (65% that of 127 μm copper). Thin copper (<32 μm) conductors lead to a much smaller decrease in SNR (approximately 10%) compared to 127 μm copper. No performance difference was observed between 127 μm thick copper and aluminum. The much thicker 600 μm copper bars only yield a 5% improvement in SNR. CONCLUSION: Even at 0.48 tesla, copper RF coil conductors much thinner than those in conventional construction can be used while maintaining SNR greater than 50% that of thick copper. These emerging coil conductor technologies enable RF coil functionality that cannot be achieved otherwise.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".