Compressed Sensing and Parallel MRI using Deep Residual Learning

作者：Dongwook Lee, Jaejun Yoo, Jong Chul Ye · 发表于：Scholarworks@UNIST (Ulsan National Institute of Science and Technology) · 年份：2017 · 被引用次数：6 · 研究领域：Sparse and Compressive Sensing Techniques、Image Processing Techniques and Applications、Machine Learning and ELM

A deep residual learning algorithm is proposed to reconstruct MR images from highly down-sampled k-space data. After formulating a compressed sensing problem as a residual regression problem, a deep convolutional neural network (CNN) was designed to learn the aliasing artifacts. The residual learning algorithm took only 30-40ms with significantly better reconstruction performance compared to GRAPPA and the state-of-the-art compressed sensing algorithm, ALOHA.