TY - JOUR AU - Sun, Liyan AU - Fan, Zhiwen AU - Huang, Yue AU - Ding, Xinghao AU - Paisley, John PY - 2018/04/26 Y2 - 2024/03/28 TI - Compressed Sensing MRI Using a Recursive Dilated Network JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 32 IS - 1 SE - Main Track: Machine Learning Applications DO - 10.1609/aaai.v32i1.11869 UR - https://ojs.aaai.org/index.php/AAAI/article/view/11869 SP - AB - <p> 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. </p> ER -