An Unsupervised Deep Learning Method for Parallel Cardiac MRI via Time-Interleaved Sampling

20 Dec 2019Yanjie ZhuZiwen KeJing ChengSen JiaYuanyuan LiuHaifeng WangLeslie YingXin LiuHairong ZhengDong Liang

Deep learning has achieved good success in cardiac magnetic resonance imaging (MRI) reconstruction, in which convolutional neural networks (CNNs) learn the mapping from undersampled k-space to fully sampled images. Although these deep learning methods can improve reconstruction quality without complex parameter selection or a lengthy reconstruction time compared with iterative methods, the following issues still need to be addressed: 1) all of these methods are based on big data and require a large amount of fully sampled MRI data, which is always difficult for cardiac MRI; 2) All of these methods are only applicable for single-channel images without exploring coil correlation... (read more)

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