Cardiac CINE MR imaging allows for accurate and reproducible measurement of cardiac function but requires long scanning times. Parallel imaging and compressed sensing (CS) have reduced the acquisition time, but the possible acceleration remained limited. Deep learning-based MR image reconstructions can further increase the acceleration rates with improved image quality. In this work, we propose a novel network for retrospectively undersampled 2D cardiac CINE that a) operates in image and k-space domain with interleaved architectures and b) utilizes spatio-temporal filters for multi-coil dynamic data. The proposed network outperforms CS and some other neural networks in both qualitative and quantitative evaluations.
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