While the application of deep learning in MR image analysis has gained significant popularity, using raw MR k-space data as part of deep learning analysis is an underexplored area. Here we develop a completely complex U-Net deep learning architecture, CU-Net, where we apply deep learning components and operations in the complex space. CU-Net leverages k-space MR signals while training a U-Net with Attention and Residual components, as opposed to using processed spatial (real) data, typically seen with MRI deep learning applications. As part of a proof-of-concept study, the complex networks demonstrated their utility and potential superiority over their spatial counterparts.
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