We developed a deep-learning-based approach for motion correction in quantitative cardiac MRI, including perfusion, T1 mapping, and T2 mapping. The proposed approach consists of a segmentation network and a registration network. The segmentation network was trained using 2D short-axis images for each of the three sequences, while the same registration network was shared between all three sequences. The proposed approach was faster and more accurate than a popular traditional registration method. Our work is beneficial for building a faster and more robust automated processing pipeline to obtain CMR parametric maps.
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.
Keywords