We introduce a novel deep learning network which combines elements of model and data driven approaches for fast MR imaging, termed modified Learned PD. The network is inspired by the first-order primal dual algorithm, where the convolutional neural network blocks are used to learn the proximal operators. Learned PD network works directly from undersampled k-space data and reconstructs MR images by updating in k-space and image domain alternatively. This approach has been evaluated by in vivo MR datasets and achieves accurate MR reconstructions, outperforming other comparing methods across various quantitative metrics.
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