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Abstract #3343

Evaluation of motion correction capability in retrospective motion correction with MoCo MedGAN

Thomas Küstner1,2,3, Friederike Gänzle3, Tobias Hepp2, Martin Schwartz3,4, Konstantin Nikolaou5, Bin Yang3, Karim Armanious2,3, and Sergios Gatidis2,5
1Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 2Medical Image and Data Analysis (MIDAS), University Hospital Tübingen, Tübingen, Germany, 3Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany, 4Section on Experimental Radiology, University Hospital Tübingen, Tübingen, Germany, 5Department of Radiology, University Hospital Tübingen, Tübingen, Germany

Motion is the main extrinsic source for imaging artifacts which can strongly deteriorate image quality and thus impair diagnostic accuracy. Numerous motion correction strategies have been proposed to mitigate or capture the artifacts. These methods require some a-priori knowledge about the expected motion type and appearance. We have recently proposed a deep neural network (MoCo MedGAN) to perform retrospective motion correction in a reference-free setting, i.e. not requiring any a-priori motion information. In this work, we propose a confidence-check and evaluate the correction capability of MoCo MedGAN with respect to different motion patterns in healthy subjects and patients.

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