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Abstract #3354

Motion Correction for a Multi-Contrast Brain MRI using a Multi-Input Neural Network

Jongyeon Lee1, Byungjai Kim1, Namho Jeong1, and Hyunwook Park1
1Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea

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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