In this study, we exploited a sparsifying deep learning method and an inverse filtering reconstruction to obtain high quality complex MR images for under-sampled MRI data. This study allows much more flexible data representations for complex MRI data training, leading to significantly higher complex reconstruction quality for practical MRI applications.
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