DEEP RESIDUAL LEARNING FOR COMPRESSED SENSING MRI

被引:0
|
作者
Lee, Dongwook [1 ]
Yoo, Jaejun [1 ]
Ye, Jong Chul [1 ]
机构
[1] Korea Adv Inst Sci & Technol, Dept Bio & Brain Engn, Bio Imaging & Signal Proc Lab, Daejeon, South Korea
关键词
Compressed sensing MRI; deep learning; residual learning; CNN;
D O I
暂无
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Compressed sensing (CS) enables significant reduction of MR acquisition time with performance guarantee. However, computational complexity of CS is usually expensive. To address this, here we propose a novel deep residual learning algorithm to reconstruct MR images from sparsely sampled k-space data. In particular, based on the observation that coherent aliasing artifacts from downsampled data has topologically simpler structure than the original image data, we formulate a CS problem as a residual regression problem and propose a deep convolutional neural network (CNN) to learn the aliasing artifacts. Experimental results using single channel and multi channel MR data demonstrate that the proposed deep residual learning outperforms the existing CS and parallel imaging algorithms. Moreover, the computational time is faster in several orders of magnitude.
引用
收藏
页码:15 / 18
页数:4
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