A conformal transceive array for 7 T neuroimaging
Bibliographic record
Abstract
The first 16-channel transceive surface-coil array that conforms to the human head and operates at 298 MHz (7 T) is described. Individual coil elements were decoupled using circumferential shields around each element that extended orthogonally from the former. This decoupling method allowed elements to be constructed with arbitrary shape, size, and location to create a three-dimensional array. Radiofrequency shimming achieved a transmit-field uniformity of 20% over the whole brain and 14% over a single axial slice. During radiofrequency transmission, coil elements couple tightly to the head and reduce the amount of power necessary to achieve a mean 90° flip angle (660-μs and 480-μs pulse lengths were required for a 1-kW hard pulse when shimming over the whole brain and a single axial slice, respectively). During reception, the close proximity of coil elements to the head increases the signal-to-noise ratio in the periphery of the brain, most notably at the superior aspect of the head. The sensitivity profile of each element is localized beneath the respective shield. When combined with the achieved isolation between elements, this results in the capacity for low geometry factors during both transmit and receive: 1.04/1.06 (mean) and 1.25/1.54 (maximum) for 3-by-3 acceleration in the axial/sagittal plane. High cortical signal-to-noise ratio and parallel imaging performance make the conformal coil ideal for the study of high temporal and/or spatial cortical architecture and function.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".