Compressed Sensing MRI Using a Recursive Dilated Network


  • Liyan Sun Xiamen University
  • Zhiwen Fan Xiamen University
  • Yue Huang Xiamen University
  • Xinghao Ding Xiamen University
  • John Paisley Columbia University


compressed sensing, magnetic resonance imaging, deep neural networks


Compressed sensing magnetic resonance imaging (CS-MRI) is an active research topic in the field of inverse problems. Conventional CS-MRI algorithms usually exploit the sparse nature of MRI in an iterative manner. These optimization-based CS-MRI methods are often time-consuming at test time, and are based on fixed transform bases or shallow dictionaries, which limits modeling capacity. Recently, deep models have been introduced to the CS-MRI problem. One main challenge for CS-MRI methods based on deep learning is the trade off between model performance and network size. We propose a recursive dilated network (RDN) for CS-MRI that achieves good performance while reducing the number of network parameters. We adopt dilated convolutions in each recursive block to aggregate multi-scale information within the MRI. We also adopt a modified shortcut strategy to help features flow into deeper layers. Experimental results show that the proposed RDN model achieves state-of-the-art performance in CS-MRI while using far fewer parameters than previously required.




How to Cite

Sun, L., Fan, Z., Huang, Y., Ding, X., & Paisley, J. (2018). Compressed Sensing MRI Using a Recursive Dilated Network. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from



Main Track: Machine Learning Applications