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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