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KMID : 1144720200240040196
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Investigative Magnetic Resonance Imaging
2020 Volume.24 No. 4 p.196 ~ p.206
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Deep Learning in MR Motion Correction: a Brief Review and a New Motion Simulation Tool (view2Dmotion)
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Lee Seul
Jung Soo-Zy
Jung Kyu-Jin
Kim Dong-Hyun
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Abstract
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With the development of deep-learning techniques, the application of deep learning in MR imaging processing seems to be growing. Accordingly, deep learning has also been introduced in motion correction and seemed to work as well as do conventional motion-compensation methods. In this article, we review the motion-correction methods based on deep learning, focusing especially on the motion-simulation methods adopted. We then propose a new motion-simulation tool, which we call view2Dmotion.
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KEYWORD
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Deep learning, Machine learning, Motion artifact, Motion correction, Motion simulation
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