While slice-to-volume registration and super-resolution reconstruction laid the foundation for motion-corrected 3D T2-weighted fetal brain magnetic resonance imaging (MRI) more than two decades ago, advances in deep learning are now enabling automation across acquisition planning, segmentation, biometry, and image quality control. In this narrative review, we highlight these emerging techniques and analyse their strengths and limitations in the context of clinical translation. We examine the major barriers to widespread clinical adoption of artificial intelligence tools and outline future directions at the clinical interface that may further transform the diagnostic role of fetal MRI. Together, these developments underscore a shifting landscape towards more comprehensive, quantitative in-utero assessment, with the potential to enhance diagnostic accuracy and workflow efficiency, and broaden the clinical applications of fetal MRI in prenatal care.
A. Luis, A. Uus, L. Story et al.· Developmental Medicine & Chi...· 0 citations
Purpose: To develop and evaluate an anatomy-aware deep learning framework for enhancement of neonatal 64mT T2-weighted MRI that improves anatomical visibility while preserving native ultra-low-field contrast and enabling quantitative structural analysis. Methods: A multitask network, jointly performing image enhancement and tissue segmentation, was trained on 75 and evaluated on 20 paired neonatal 64mT/3T MRI datasets spanning a broad range of gestational ages and pathologies. To preserve native 64mT contrast, 3T images were locally harmonized before training. The framework also generated quality-control maps and regional volumetric measurements. Volumetric agreement was further assessed in 40 paired term-born control datasets. Results: Enhanced 64mT images showed improved image quality metrics and better delineation of cortical, deep gray matter, ventricular, white matter, and posterior fossa structures while maintaining native contrast characteristics. Tissue segmentations demonstrated good agreement with reference 3T labels. Volumetric measurements showed excellent correspondence with 3T across major tissue compartments, with only small systematic regional biases. Conclusions: Anatomy-aware enhancement enables automated tissue segmentation and volumetric analysis directly from neonatal 64mT MRI while preserving native image contrast. These findings support the feasibility of quantitative neonatal neuroimaging at ultra-low field.
P. Cawley, A. Uus, K. Colford et al.· medRxiv· 0 citations
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