We propose an enhanced recursive residual network (ERRN) that improves the basic recursive residual network with both a high-frequency feature guidance and dense connections. The feature guidance is designed to predict the underlying anatomy based on image a priori learning from the label data, playing a complementary role to the residual learning. The ERRN is adapted to include super resolution MRI and compressed sensing MRI, while an application-specific error-correction unit is added into the framework, i.e. back projection for SR-MRI and data consistency for CS-MRI due to their different sampling schemes.
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