Compressed sensing (CS) magnetic resonance (MR) imaging acquisitions reduce MR exam times by decreasing the amount of data acquired during acquisition, while still reconstructing high quality images. Deep learning methods have the advantage of reconstructing images in a single step as opposed to iterative (and slower) CS methods. Our proposal aims to leverage information from both k-space and image domains, in contrast to most other deep-learning methods that only use image domain information. We compare our W-net model against four recently published deep-learning-based methods. We achieved second best results in the quantitative analysis, but with more visually pleasing reconstructions.
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