Cardiac MR imaging plays an important role in clinical diagnosis. But the long scan time limits its wide applications. To accelerate data acquisition, deep learning based methods have been applied to effectively reconstruct the undersampled images. However, current deep convolutional neural network (CNN) based methods do not make full use of the hierarchical features from different convolutional layers, which impedes their performances. In this work, we propose a cascaded residual dense network (C-RDN) for dynamic MR image reconstruction with both local features and global features being fully explored. Our proposed C-RDN achieves the best performance on in vivo datasets compared to the iterative optimization methods and the state-of-the-art CNN method.