Automated assessment of neonatal internal capsule maturation on T2-weighted MRI across 7T and 3T
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Motivation
Quantitative assessment of neonatal internal capsule (IC) maturation remains largely reliant on qualitative visual evaluation, limiting objectivity and scalability.
Approach
We developed a fully automated 3D deep learning framework for anatomically detailed segmentation of IC subregions and PLIC myelin-related signal from structural T2-weighted MRI, trained on both high-resolution 7T and conventional 3T neonatal datasets. Volumetric and intensity-based metrics were derived, and developmental trajectories were modelled using postmenstrual age (PMA) and postnatal age (PNA), with normative modelling used to quantify individual deviations.
Results
The pipeline achieved high segmentation accuracy across field strengths (Dice > 0.95, relative volume difference < 5%). IC metrics showed robust age-related changes, with volumetric measures increasing and intensity-based measures decreasing with PMA. PNA effects indicated prematurity-related modulation at equivalent maturational age. These patterns generalized to 3T, where normative modelling revealed significant deviations in preterm infants, particularly for myelin-related intensity measures.
Conclusion
Structural T2-weighted MRI, combined with anatomically informed segmentation, enables quantitative and biologically meaningful assessment of neonatal IC maturation. This provides a scalable framework for studying early white matter development and supports potential clinical translation.
