Characterization of B<sub>1</sub><sup>+</sup> field variation in brain at 3 T using 385 healthy individuals across the lifespan
Bibliographic record
Abstract
Abstract Purpose The transmit field at 3 T in brain affects the spatial uniformity and contrast of most image acquisitions. Here, spatial variation in brain at 3 T is characterized in a large healthy population. Methods Bloch‐Siegert maps were acquired at 3 T from 385 healthy subjects aged 5–90 years on a single MRI system. After transforming all maps to a standard brain atlas space, region‐of‐interest analysis was performed, and intersubject voxel‐wise coefficient of variation was calculated across the whole brain. The variability due to age and brain size was studied separately in males and females, along with variability due to nonideal transmit calibration. Results The voxel‐based mean coefficient of variation was 4.0% across all subjects, and the difference in between central (left thalamus) and outer regions (left frontal gray matter) was 24.2% ± 2.3%. The least intersubject variability occurred in central regions, whereas regions toward brain edges increased markedly in variation. The variability with age was mostly attributed to lifespan changes in CSF volume (which alters brain conductivity) and head orientation. Larger brain size correlated with more inhomogeneity (p < .001). Varying head position and anatomy resulted in an inaccurate transmit calibration. Conclusion In standard atlas space, intersubject variability at 3 T was relatively small in a large population aged 5–90 years. The varied with age‐related changes of CSF volume and head orientation, as well as differences in brain size and transmit calibration.
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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 | 0.000 |
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".