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. 2019 Apr 26;19(1):64–76. doi: 10.2463/mrms.mp.2018-0156

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© 2019 Japanese Society for Magnetic Resonance in Medicine

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/

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Fig. 3 — Process diagram of the network training. The datasets for the input and output were generated from the GT images. Images with simulated artifact were calculated using the respiratory motion simulation with the GT images. The residual images were derived from the subtraction of the images with simulated artifact from the GT images. Finally, patches for the input and output were generated by cropping these images. The training was performed using the pair of patches. GT, ground truth; MARC, Motion artifact reduction method based on convolutional neural network.