Evolution of magnetic resonance diffusion tensor imaging metrics in the normal fetal brain
Bibliographic record
Abstract
OBJECTIVE: Diffusion tensor imaging (DTI) of the fetal brain can generate unique quantitative data that reflect both tissue integrity and the level of myelination in the developing brain. The objective of this study was to quantify normal fetal brain metrics from 21 to 36 weeks' gestation using DTI in a cohort of healthy fetuses, using the latest techniques designed to minimize artifacts from movement and those inherent to magnetic resonance imaging (MRI) acquisition. METHODS: We conducted a prospective study between June 2021 and June 2022 of pregnant volunteers with no known fetal anomalies, between 21 and 36 weeks' gestation. MRI scans were performed using a 1.5-T 450W General Electric Signa MRI system, including 15 non-collinear diffusion-weighted axial images of the fetal brain. Preprocessing included denoising, correction of Gibb's ringing artifact, eddy current correction, bias removal, registration to a reference template, slice-to-volume reconstruction and constrained spherical convolution to obtain maps of fractional anisotropy (FA), apparent diffusion coefficient (ADC), axial diffusivity (AD) and radial diffusivity (RD). A total of 51 white matter and gray matter regions from both hemispheres were analyzed. Regression models were used to describe the evolution of the DTI metrics during gestation. RESULTS: DTI was successful in 94/111 (84.7%) fetuses and was performed at a median of 30 (range, 21-36) weeks' gestation. In the different white matter tracts, FA showed five distinct patterns: (1) initial decrease until 32-34 weeks, followed by an increase until 36 weeks; (2) initial decrease until 25-26 weeks, followed by an increase until 36 weeks; (3) linear increase; (4) linear decrease; or (5) no gestational-age-related change. In the different gray matter regions, FA showed three distinct patterns: (1) linear decrease; (2) initial decrease until 31-34 weeks, followed by an increase; or (3) no gestational-age-related change. In the majority of white matter and gray matter regions, ADC, AD and RD showed a linear decrease between 21 and 36 weeks. CONCLUSIONS: The variations in DTI metrics may indirectly reflect the microstructural changes that occur during brain development, particularly during myelination, and may help characterize the development of fetal brain connectivity in utero. © 2025 International Society of Ultrasound in Obstetrics and Gynecology.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".