Patient motion is a challenging and common source of artifacts in MRI. Two recent studies investigating motion detection with convolutional neural networks showed promising results, but did not generalize to varying MRI contrasts. We present a unified, domain adapted deep learning routine to provide automated image motion assessment in MR brain scans with T1 and T2 contrast. We aim to limit the influence of varying image contrasts, scanner models, and scan parameters in the motion detection routine by using adversarial training.
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