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Abstract #4426

Deep Learning based motion artifact correction improves the quality of cortical reconstructions

Ben A Duffy1, Lu Zhao1, Arthur Toga1, and Hosung Kim1

1Institute of Neuroimaging and Informatics, University of Southern California, los angeles, CA, United States

Cortical reconstruction is prone to failure without high quality structural imaging data. Here, motion simulation was performed on good quality structural MRI images and used to train a regression convolutional neural network to predict the motion-free images as the output. We show that performing retrospective motion correction using a convolutional neural network is able to significantly reduce the number of cortical surface reconstruction quality control failures.

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