SNR Variability with Frontal Coil Plate Displacement in 3T Head MRI
Bibliographic record
Abstract
In head MRI applications, flexible RF receive-only head coils can offer improved SNR and patient comfort. In our work, we achieve partial, but sufficient, flexibility by decomposing an average head surface into movable, semi-flexible plates that comfortably adapt to the surface of the head. As a result, we can include a larger number of smaller elements, pushing the boundary of achievable SNR while still being able to resolve brain areas. In order to test the viability of the design, we investigate the SNR variability of two 12-channel frontal plate arrays when they are adapted to fit differently sized heads. The elements were constructed on a 3D-printed thermoplastic polyurethane (TPU) flexible substrate, with conductive loops on the inside surface to minimize their distance to the head. Experiments were conducted in an MRI scanner in which a spherical phantom was imaged by the frontal plate arrays together at four positions. We demonstrate that SNR decreases, as expected, as the gap between the plates increases, but in an acceptable range. The overall range of inter-plate distance results in SNR variability of approximately 15%. These results support the overall feasibility of the larger head coil composed of eight movable plates: the mechanical gaps that would appear for larger heads would not compromise the SNR and, therefore, signal integrity and would still outperform a similar fixed-size head coil.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".