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Abstract #4952

Eye motion artifacts in T2-weighted images, identified by deep neural networks, correlate with concussion

Andrew S. Nencka1, John D. Bukowy2, Robin A. Karr1, Andrew P. Klein1, Kevin M. Koch1, Peter LaViolette1, Sean D. McGarry1, Timothy B. Meier3, Brad J. Swearingen3, and Michael McCrea3

1Radiology, Medical College of Wisconsin, Milwaukee, WI, United States, 2Physiology, Medical College of Wisconsin, Milwaukee, WI, United States, 3Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States

Oculomotor deficits occur with traumatic brain injury, and eye motion yields artifacts in the phase encoding directions of common MRI acquisitions. Here we quantify motion artifacts in regions of interest of T2-weighted MRI head images in concussed and healthy high school and collegiate athletes. Regions of interest over eyes, and inner ear structures as a control, were automatically generated using a convolutional neural network. Acute and sub-acute injury was found to yield significantly increased motion artifact compared to controls in ROIs covering eyes. No differences in motion artifacts covering inner ear structures were found. These results indicate that anatomical MRI following traumatic brain injury may offer increased diagnostic or prognostic information through artifact resulting from eye motion associated with injury.

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