Evaluating reliability of automated quantitative brain morphometry from fetal T2-weighted MRI.

Overview

1. Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
2. Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
3. Department of Pediatrics, Harvard Medical School, Boston, MA, United States
4. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
5. Department of Radiology, Harvard Medical School, Boston, MA, United States

Abstract

Introduction: Three-dimensional assessment of fetal cortical morphology from MRI is essential for understanding early brain neurodevelopment. However, measurement can be affected by fetal imaging quality, number and selection of available stacks, and reconstruction methods.

Methods: We evaluated the within-session reliability of an automated cortical morphometry pipeline in 30 typically developing fetuses [22–36 weeks gestational age (GA)]. For each subject, two disjoint subsets of 2D T2-weighted stacks (no shared stacks) were independently reconstructed into 3D volumes using the Neural Slice-to-Volume Reconstruction (NeSVoR) and the Slice-to-Volume Reconstruction Toolkit (SVRTK). Cortical plate volume, surface area, mean sulcal depth, and absolute mean curvature were extracted, and measurement reliability was assessed using absolute percent difference (APD) and intraclass correlation coefficients (ICC). Multiple linear regression evaluated the effects of mean stack quality, quality difference between subsets, stack count, and GA on measurement reliability.

Results: NeSVoR-derived metrics showed high reliability for all measures (mean APD < 3%, ICC > 0.99). SVRTK-derived metrics were also robust (mean APD < 5%, ICC > 0.97). Reliability increased with greater stack count and older GA in NeSVoR, and with higher mean stack quality in SVRTK.

Discussion: These results demonstrate that automated cortical morphometry from fetal MRI yields highly consistent measurements of volumetric and surface metrics within the proposed within-session design, once minimum levels of image quality and stack count are met. This study proposes a within-session benchmark for automated fetal cortical measurements and underscores that systematic reliability assessment is essential for confident application of automated pipelines in fetal neuroimaging.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data availability statement

Image-processing and analysis scripts are available via the lab’s centralized GitHub (HYPERLINK https://github.com/FNNDSC). Anonymized MRI data, associated metadata, and derivative files used in this study may be obtained from the corresponding author () upon reasonable request and execution of a data-use agreement, as required by institutional policies.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Recorded: type, language, journal, volume, pages, dates, 8 authors, 6 keywords, 2 funders, 37 references.

Cite

This paper

Jeong, S., Gondova, A., Yun, H. J., You, S., Lagunas, M., Golland, P., Grant, P. E., & Im, K. (2026). Evaluating reliability of automated quantitative brain morphometry from fetal T2-weighted MRI. Frontiers in neuroscience, 20, 1817732. https://doi.org/10.3389/fnins.2026.1817732

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