Meeting Banner
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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords