A size‐adaptive RF coil for MRI of the pediatric human brain at 7 T
Bibliographic record
Abstract
Abstract Purpose The purpose of this work was to design and build a size‐adaptive pediatric RF head coil for 7 T neuroimaging. The coil can be safely applied for imaging children 4–9 years old. Methods The pediatric head coil incorporates eight, transmit dipole elements for operation in parallel transmit (pTx) mode. The receive architecture is comprised of a 32‐channel conformal, size‐adaptive receive array. Receive elements were arranged into five sections of a mechanically adjustable 3D printed head former, allowing adjustment of the receive array according to child head size. The transmit coil was carefully simulated to calculate specific absorption rate (SAR) and B1+ efficiency. Coil performance was then evaluated with a pediatric head phantom at both the largest and smallest dimensions of the receive former. In vivo imaging was carried out in 3 pediatric subjects (aged 5, 6, and 9 years old) to acquire B1+ field maps and anatomical MP2RAGE images. Results A comparison of simulated and experimental B1+ performance in the pediatric head phantom was used to validate SAR models and to demonstrate that the coil was safe for pediatric imaging. The SNR performance in the pediatric phantom was improved by adjusting the position of the receive array to the smallest possible position. The in vivo B1+ efficiency agreed with expectations, and the coil provided precise anatomical images of the brain. Conclusions The proposed size‐adaptive coil enables safe, high‐quality imaging of children at 7 T, with a range of ages and head sizes. Accurate SAR modeling enabled imaging using both combined circularly polarized and dynamic pTx modes.
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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) | 0.000 | 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) | 0.001 | 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".