A radiofrequency coil for infants and toddlers
Bibliographic record
Abstract
Abstract Background Infants and toddlers are a challenging population on which to perform MRI of the brain, both in research and clinical settings. Due to the large range in head size during the early years of development, paediatric neuro-MRI requires a radiofrequency (RF) coil, or set of coils, that is tailored to head size to provide the highest image quality. Mitigating techniques must also be employed to reduce and correct for subject motion. Objective To develop an RF coil with a tailored mechanical-electrical design that can adapt to the head size of three-month-old infants to three-year-old toddlers. Materials and methods An RF coil was designed with tight-fitting coil elements to improve SNR in comparison to commercially available adult head coils, while simultaneously aiding in immobilization. The coil was designed without visual obstruction to facilitate an unimpeded view of the child’s face and the potential application of camera or motion-tracking systems. Results Despite the lack of elements over the face, the paediatric coil produced higher SNR over most of the brain compared to adult coils, including more than 2-fold in the periphery. Acceleration rates of 4-fold in each Cartesian direction could be achieved. Higher SNR allowed for shorter acquisition times through accelerated imaging protocols, reducing the probability of motion during a scan. Conclusion Modification to the acquisition protocol, with immobilization of the head through the adjustable coil geometry, and subsequently combined with a motion tracking system provides a compelling platform for scanning paediatric populations without sedation and with improved image quality.
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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".