Real-Time Cardiac Cine MRI with Residual Convolutional Recurrent Neural Network
This work addresses the need for real-time cardiac imaging in patients who cannot hold their breath or have abnormal heart rhythms, representing an incremental advance by applying deep learning to a specific domain.
The paper tackled the challenge of reconstructing high-quality images from highly undersampled data in real-time cardiac cine MRI, proposing a residual convolutional RNN that achieved superior performance over compressed sensing based on radiologist evaluations.
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.