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Abstract #3981

Classification of Head Movements Inside an MRI Scanner using a Single Marker and Neural Networks

Aditya Singh1, Brian Keating1, Sara Hayama1, Michael Herbst1, and Thomas Ernst1

1JABSOM, University of Hawaii, Honolulu, HI, United States

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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