Dual Domain Deep Learning Framework for Cardiac MR Image Reconstruction

Most deep learning methods apply U-Net either in image or k-space domain. Nevertheless, these methods have limitations: (1) Directly applying U-Net in k-space domain is not optimal for extracting features; (2) conventional image-domain oriented U-Net does not fully utilize the information of encoder part of the network for extracting features in the decoder part. In this paper, a dual-domain deep learning-based approach is presented, incorporating multi-coil data consistency layers for the reconstruction of cardiac MR images from 1-D Variable Density (VD) under-sampled data. Experiments show superior reconstruction results of the proposed method than conventional Compressed Sensing (CS) method.