Keywords: Machine Learning/Artificial Intelligence, Artifacts, Quality ControlA three-dimensional convolutional neural network was trained to detect motion artifacts on a volume level for two pediatric diffusion MRI datasets acquired between 1 month and 3 years of age. Accuracies of 95% and 98% were achieved between the two datasets. Additionally, the effects of motion-corrupted volumes on quantitative parameter estimation was examined. Data was processed without quality control and with quality control performed by the neural network. DTI and NODDI metrics were calculated and compared between methods. Significant differences were found for both individual and group results.
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