Detection and classification of head motion may be required for optimal application of prospective motion correction techniques for brain imaging using external tracking systems. Supervised neural networks using various motion metrics were designed to classify head motion inside MR scanner into rigid-body motion and skin motion using single-marker 6-DOF information. The neural networks were trained using volunteer data and then applied to head motion data from 6 clinical in-patients. Neural networks could consistently achieve overall accuracy of 75% or greater.
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