<scp>In vivo T<sub>1</sub></scp> mapping of neonatal brain tissue at <scp>64 mT</scp>
Bibliographic record
Abstract
Purpose Ultralow‐field (ULF) point‐of‐care MRI systems allow image acquisition without interrupting medical provision, with neonatal clinical care being an important potential application. The ability to measure neonatal brain tissue T 1 is a key enabling technology for subsequent structural image contrast optimization, as well as being a potential biomarker for brain development. Here we describe an optimized strategy for neonatal T 1 mapping at ULF. Methods Examinations were performed on a 64‐mT portable MRI system. A phantom validation experiment was performed, and a total of 33 in vivo exams were acquired from 28 neonates with postmenstrual age ranging from 31 +4 to 49 +0 weeks. Multiple inversion‐recovery turbo spin‐echo sequences were acquired with differing inversion and repetition times. An analysis pipeline incorporating inter‐sequence motion correction generated proton density and T 1 maps. Regions of interest were placed in the cerebral deep gray matter, frontal white matter, and cerebellum. Weighted linear regression was used to predict T 1 as a function of postmenstrual age. Results Reduction of T 1 with postmenstrual age is observed in all measured brain tissue; the change in T 1 per week and 95% confidence intervals is given by dT 1 = −21 ms/week [−25, −16] (cerebellum), dT 1 = −14 ms/week [−18, −10] (deep gray matter), and dT 1 = −35 ms/week [−45, −25] (white matter). Conclusion Neonatal T 1 values at ULF are shorter than those previously described at standard clinical field strengths, but longer than those of adults at ULF. T 1 reduces with postmenstrual age and is therefore a candidate biomarker for perinatal brain development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".