Liver magnetic resonance imaging (MRI) is limited by several technical challenges, including relatively long acquisition time and respiratory motion artifacts. Recently, deep learning methods have been proposed to reconstruct undersampled k-space data by training deep neural networks. In this study, we raised a U-net convolutional neural network architecture to improve the reconstruction speed and image quality of liver T2-weighted MRI. This technique was able to cover the whole liver during one breath hold and showed promising performance in image quality and lesion detectability.
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