Diffusion weighted imaging has become a key imaging modality for the assessment of brain connectivity as well as the structural integrity, but the method is limited by low SNR and long scan times. In this study we demonstrate that AI models trained on a moderate number of publicly available 7T datasets (n=12) are able to enhance image resolutions in diffusion MRI up to 25 times. The applied NoGAN model performs well for smaller resolution enhancements (4- and 9-fold), but generates "hyper" realistic images for higher enhancements (16- and 25-fold). Models trained using perceptual loss seem to avoid this limitation.
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