  Real-time cardiac cine MRI does not require ECG gating in the data
acquisition and is more useful for patients who can not hold their breaths or
have abnormal heart rhythms. However, to achieve fast image acquisition,
real-time cine commonly acquires highly undersampled data, which imposes a
significant challenge for MRI image reconstruction. We propose a residual
convolutional RNN for real-time cardiac cine reconstruction. To the best of our
knowledge, this is the first work applying deep learning approach to Cartesian
real-time cardiac cine reconstruction. Based on the evaluation from
radiologists, our deep learning model shows superior performance than
compressed sensing.
