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. 2019 Sep 13;14(9):e0222406. doi: 10.1371/journal.pone.0222406

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© 2019 Gomi et al

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Fig 6 — (a) Metal artifacts obtained employing the artifact index (AI) of the selected characteristics. The in-focus plane image displays the background areas and metal artifact of the AI measurements. The AIs resulting from differences in the mini-batch size (b), epochs (c), and weighting coefficient (w) in the denoising convolutional neural network metal artifact reduction hybrid reconstruction (DnCNN-MARHR) images are shown (d). The settings of mini-batch size of 544, epochs of 60, and weighting coefficient (w) of 0.7 generated the maximum artifact decreasing effect. The error bar represents the standard error.