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PT  - JOURNAL ARTICLE
AU  - Sommer, K.
AU  - Saalbach, A.
AU  - Brosch, T.
AU  - Hall, C.
AU  - Cross, N.M.
AU  - Andre, J.B.
TI  - Correction of Motion Artifacts Using a Multiscale Fully Convolutional Neural Network
AID  - 10.3174/ajnr.A6436
DP  - 2020 Feb 13
TA  - American Journal of Neuroradiology
4099  - http://www.ajnr.org/content/early/2020/02/13/ajnr.A6436.short
4100  - http://www.ajnr.org/content/early/2020/02/13/ajnr.A6436.full
AB  - BACKGROUND AND PURPOSE: Motion artifacts are a frequent source of image degradation in the clinical application of MR imaging (MRI). Here we implement and validate an MRI motion-artifact correction method using a multiscale fully convolutional neural network.MATERIALS AND METHODS: The network was trained to identify motion artifacts in axial T2-weighted spin-echo images of the brain. Using an extensive data augmentation scheme and a motion artifact simulation pipeline, we created a synthetic training dataset of 93,600 images based on only 16 artifact-free clinical MRI cases. A blinded reader study using a unique test dataset of 28 additional clinical MRI cases with real patient motion was conducted to evaluate the performance of the network.RESULTS: Application of the network resulted in notably improved image quality without the loss of morphologic information. For synthetic test data, the average reduction in mean squared error was 41.84%. The blinded reader study on the real-world test data resulted in significant reduction in mean artifact scores across all cases (P &amp;lt; .03).CONCLUSIONS: Retrospective correction of motion artifacts using a multiscale fully convolutional network is promising and may mitigate the substantial motion-related problems in the clinical MRI workflow.FCNfully convolutional neural networkMSEmean squared errorSSIMstructural similarity index