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Abstract #0641

Compressed sensing and Parallel MRI using deep residual learning

Dongwook Lee1, Jaejun Yoo1, and Jong Chul Ye1

1Korea Advanced Institute of Science and Technology, Daejeon, Korea, Republic of

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.

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