Size‐adaptable 13‐channel receive array for brain MRI in human neonates at 3 T
Bibliographic record
Abstract
Neonatal brain injury suffered by preterm infants and newborns with some medical conditions can cause significant neurodevelopmental disabilities. MRI is a preferred method to detect these accidents and perform in vivo evaluation of the brain. However, the commercial availability and optimality of receive coils for the neonatal brain is limited, which in many cases leads to images lacking in quality. As extensively demonstrated, receive arrays closely positioned around the scanned part provide images with high signal-to-noise ratios (SNRs). The present work proposes a pneumatic-based MRI receive array that can physically adapt to infant head dimensions from 27-week premature to 1.5 months old. Average SNR increases of up to 68% in the head region and 122% in the cortex region, compared with a 32-channel commercial head coil, were achieved at 3 T. The consistent SNR distribution obtained through the complete coil size range, specifically in the cortex, allows the acquisition of images with similar quality across a range of head dimensions, which is not possible with fixed-size coils due to the variable coil-to-head distance. The risks associated with mechanical pressure on the neonatal head are minimal and the head motion is restricted. The method could be used in coil designs for other age groups, body parts and subjects.
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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) | 0.002 | 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".