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Predicting primary diffusion directions in white matter from T₁-weighted magnetic resonance images Connolly, Sam

Abstract

Orientation dependence studies are an ongoing research area in Magnetic Resonance Imaging (MRI), often relying on diffusion MRI to gain nerve fiber direction, a time-consuming scan not always included in research and clinical protocols. Using paired 3D T₁-weighted and diffusion images from the Human Connectome Project (HCP), a U-Net was trained to find voxel-wise principal diffusion directions from 3D T₁-weighted images. Different loss functions and data augmentations were used in model training. The models trained with different data augmentations and loss functions were evaluated using cosine distance during training. The trained model with the best performance had a group average cosine distance of 0.349 ± 0.007 and a group average difference in absolute polar angle of 8.762° ± 0.293° on the independent test set.

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