The conventional multi-shot diffusion weighted imaging (DWI) techniques, such as MUSE, have not been widely adopted clinically due to long scan time. In this study, an accelerated multi-shot DWI method based on deep learning is proposed. By learning a fully convolutional neural network to enhance DWI images, more structural details and less noise can be achieved, especially when with fewer shots or NSA (Number of Signal Average), in the meantime the reconstruction time can be reduced by over 200 times. It means the proposed approach reduces the scan and reconstruction time dramatically while keeping high quality of the images, which makes it a potential technique for high resolution multi-shot DWI in routine clinical study.
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