Long scan times and susceptibility to respiratory motion are major challenges in free-breathing 3D cardiac MRI. Respiratory-resolved 4D approaches deal with motion by assigning data to different respiratory bins and exploiting motion redundancies during reconstruction. However, for accelerated acquisitions this leads to highly undersampled respiratory bins, affecting image quality. Here we propose a novel unrolled VNN that reconstructs undersampled 4D cardiac MRI by exploiting motion redundancies and by using conjugate gradient to enforce data-consistency within every stage of the VNN, providing generalization of the network to the unpredictable sampling of each bin due to subject-specific respiratory motion.
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