In clinical scans, the acquired DWI images usually has limited resolution. Super resolution method has the potential to improve the image resolution without adding scan time. Here we propose a deep-learning based multi-contrast super resolution network with gradient-map guidance and a novel FA loss function to reconstruct high-resolution DWI images from low-resolution DWI images and high–resolution anatomical images. In-vivo DWI data are used to test the proposed method. The results show that the image quality can be improved.
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