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Abstract #4855

k-space deep learning for MR herringbone artifact correction

Juyoung Lee1 and Jong Chul Ye1

1KAIST, Daejeon, Korea, Republic of

Herringbone artifact is caused by power fluctuation of MR equipment or unstable shielding. Herringbone artifact image is difficult to analyze because it scatters on whole image region of single or multiple slices. There is a study for MR artifact correction which can be represented as sparse outliers on k-space. This method exploits the duality between the low-rankness of Hankel matrix in k-space and the sparsity in the image domain. However, this method has high computational complexity, and consumes much time. In this research, we suggest the new effective and fast MR artifact correction method using deep learning.

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