Reconstruction of cine Cardiac MRI (CMRI) is an active research area with room for improvement in motion detection (particularly irregular cardiac motion) and modeling in order to significantly enhance the quality of reconstructed images. Moreover, the reduction of scan time and image reconstruction time of cine CMRI is also a key aspect of today’s clinical requirement. We propose a dual domain cascade of neural networks intercalated with multi-coil data consistency layers for the reconstruction of cardiac MR images from Variable Density under-sampled data. The results show successful reconstruction results of our proposed method when compared with conventional compressed sensing reconstruction.
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