Dense, shape‐optimized posterior 32‐channel coil for submillimeter functional imaging of visual cortex at 3T
Bibliographic record
Abstract
PURPOSE: Functional neuroimaging of small cortical patches such as columns is essential for testing computational models of vision, but imaging from cortical columns at conventional 3T fields is exceedingly difficult. By targeting the visual cortex exclusively, we tested whether combined optimization of shape, coil placement, and electronics would yield the necessary gains in signal-to-noise ratio (SNR) for submillimeter visual cortex functional MRI (fMRI). METHOD: We optimized the shape of the housing to a population-averaged atlas. The shape was comfortable without cushions and resulted in the maximally proximal placement of the coil elements. By using small wire loops with the least number of solder joints, we were able to maximize the Q factor of the individual elements. Finally, by planning the placement of the coils using the brain atlas, we were able to target the arrangement of the coil elements to the extent of the visual cortex. RESULTS: The combined optimizations led to as much as two-fold SNR gain compared with a whole-head 32-channel coil. This gain was reflected in temporal SNR as well and enabled fMRI mapping at 0.75 mm resolutions using a conventional GRAPPA-accelerated gradient echo echo planar imaging. CONCLUSION: Integrated optimization of shape, electronics, and element placement can lead to large gains in SNR and empower submillimeter fMRI at 3T. Magn Reson Med 76:321-328, 2016. © 2015 Wiley Periodicals, Inc.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".