Radiofrequency coil for routine ultra‐high‐field imaging with an unobstructed visual field
Bibliographic record
Abstract
Many neuroscience applications have adopted functional MRI as a tool to investigate the healthy and diseased brain during the completion of a task. While ultra-high-field MRI has allowed for improved contrast and signal-to-noise ratios during functional MRI studies, it remains a challenge to create local radiofrequency coils that can accommodate an unobstructed visual field and be suitable for routine use, while at the same time not compromise performance. Performance (both during transmission and reception) can be improved by using close-fitting coils; however, maintaining sensitivity over the whole brain often requires the introduction of coil elements proximal to the eyes, thereby partially occluding the subject's visual field. This study presents a 7 T head coil, with eight transmit dipoles and 32 receive loops, that is designed to remove visual obstructions from the subject's line of sight, allowing for an unencumbered view of visual stimuli, the reduction of anxiety induced from small enclosures, and the potential for eye-tracking measurements. The coil provides a practical solution for routine imaging, including a split design (anterior and posterior halves) that facilitates subject positioning, including those with impaired mobility, and the placement of devices required for patient comfort and motion reduction. The transmit and receive coils displayed no degradation of performance due to adaptions to the design topology (both mechanical and electrical) required to create an unobstructed visual field. All computer-aided design files, electromagnetic simulation models, transmit field maps and local specific absorption rate matrices are provided to promote reproduction.
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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) | 0.006 | 0.003 |
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".