High‐contrast 3D neonatal brain imaging with combined <i>T</i><sub>1</sub>‐ and <i>T</i><sub>2</sub>‐weighted MP‐RAGE
Bibliographic record
Abstract
Optimization of magnetization-prepared rapid gradient-echo (MP-RAGE) sequence variations for maximum white matter (WM) versus gray matter (GM) contrast in neonates at 3T was investigated. Numerical simulations were applied to optimize and compare three contrast preparation modules and to assess the effect of phase encoding (PE) order on contrast between WM and thin cortical GM layers. Simulations predict that a new sequence, which combines both T(1)- and T(2)-weighting into the contrast preparation and utilizes an interleaved elliptical-spiral PE order, should provide the strongest contrast between neonatal WM and cortical GM. This sequence was compared to a conventional MP-RAGE acquisition (i.e., T(1)-weighted preparation, centric PE order) for in vivo imaging of seven preterm newborn infants. Regional measurements of the contrast-to-noise ratio (CNR) between WM and GM demonstrated an increase of 50-70% (depending on GM region) using the new sequence, in good agreement with theoretical predictions. This improved contrast resulted in superior WM versus GM discrimination in intensity-based brain tissue segmentations.
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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 | 0.001 | 0.002 |
| 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) | 0.000 | 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".