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 qual...
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 enhancemen...
P. Cawley, A. Uus, K. Colford et al.· medRxiv· 0 citations
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