An 8-Channel Transceiver Coil for Carotid Artery Imaging at 7T Using an Optimized Shield Design
Bibliographic record
Abstract
Objective:To design and fabricate a transmit/receive (T/R) radiofrequency (RF) coil array for MRI of the carotid arteries at 7T with optimal shielding to improve transmit performance in parallel transmit (pTx) mode.Methods:The carotid coil included 8 total RF elements, with left and right subarrays, each consisting of 4 overlapping loops with RF shields. Electromagnetic (EM) simulations were performed to optimize and improve the transmit performance of the array by determining the optimal distance between the RF shield and each subarray. EM simulations were further used to calculate local specific absorption rate (SAR) matrices. Based on the SAR matrices, virtual observation points (VOPs) were applied to ensure safety during parallel transmission. The efficacy of the coil design was evaluated by measuring coil performance metrics when imaging a phantom and by acquiring in-vivo images.Results:The optimal distance between the RF shield and each subarray was determined to be 45 mm. This resulted in a maximumB1+efficiency of 1.23 μT/$\surd W$in the carotid arteries and a peak, 10-g-average SAR per Watt of 0.86 kg−1when transmitting in the nominal CP+mode. Optimizing the RF shield resulted in up to 37% improvement inB1+efficiency and 14% improvement in SAR efficiency compared to an unshielded design.Conclusion and significance:Optimizing the distance between the RF shield and coil array provided significant improvement in the transmit characteristics of the bilateral carotid coil. The bilateral coil topology provides a compelling platform for imaging the carotid arteries with high field MRI.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".