Numerous motion correction methods have been developed to reduce motion artifacts and improve image quality in MRI. Conventional techniques utilizing motion measurement required a prolonged scan time or intensive computational costs. Deep learning methods have opened up a new way for motion correction without motion information. A proposed method using a multi-input neural network with the structural similarity loss takes an advantage of a common clinical setting of multi-contrast acquisition to clearly correct motion artifacts in brain imaging. Motion artifacts can be fully retrospectively and greatly reduced without any motion measurement by the proposed method.
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