论文标题
带有残留卷积复发性神经网络的实时心脏Cine MRI
Real-Time Cardiac Cine MRI with Residual Convolutional Recurrent Neural Network
论文作者
论文摘要
实时心脏Cine MRI不需要数据获取中的ECG门控，对于无法屏住呼吸或患有异常心律的患者更有用。但是，为了获得快速的图像获取，实时Cine通常会获取高度不足的数据，这对MRI图像重建构成了重大挑战。我们提出了一个残留的卷积RNN，用于实时心脏电影重建。据我们所知，这是将深度学习方法应用于笛卡尔实时心脏电影重建的第一项工作。基于放射科医生的评估，我们的深度学习模型比压缩感应表现出优越的性能。
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.
