Abstract #4868

Towards Contrast-Independent Automated Motion Detection Using 2D Adversarial DenseNets

Silvia Arroyo-Camejo^1,2, Benjamin Odry¹, Xiao Chen¹, Kambiz Nael³, Luoluo Liu^1,4, David Grodzki^1,2, and Mariappan S. Nadar¹

¹Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, United States, ²Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany, ³Radiology, Icahn School of Medicine at Mount Sinai, New York City, NY, United States, ⁴Electrical Engineering, Johns Hopkins University, Baltimore, MD, United States

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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