Nineteen-channel receive array and four-channel transmit array coil for cervical spinal cord imaging at 7T
Bibliographic record
Abstract
PURPOSE: To design and validate a radiofrequency (RF) array coil for cervical spinal cord imaging at 7T. METHODS: A 19-channel receive array with a four-channel transmit array was developed on a close-fitting coil former at 7T. Transmit efficiency and specific absorption rate were evaluated in a B1 (+) mapping study and an electromagnetic model. Receive signal-to-noise ratio (SNR) and noise amplification for parallel imaging were evaluated and compared with a commercial 3T 19-channel head-neck array and a 7T four-channel spine array. The performance of the array was qualitatively demonstrated in human volunteers using high-resolution imaging (down to 300 μm in-plane). RESULTS: The transmit and receive arrays showed good bench performance. The SNR was approximately 4.2-fold higher in the 7T receive array at the location of the cord with respect to the 3T coil. The g-factor results showed an additional acceleration was possible with the 7T array. In vivo imaging was feasible and showed high SNR and tissue contrast. CONCLUSION: The highly parallel transmit and receive arrays were demonstrated to be fit for spinal cord imaging at 7T. The high sensitivity of the receive coil combined with ultra-high field will likely improve investigations of microstructure and tissue segmentation in the healthy and pathological spinal cord.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".