A deep residual learning algorithm is proposed to reconstruct MR images from highly down-sampled k-space data. After formulating a compressed sensing problem as a residual regression problem, a deep convolutional neural network (CNN) was designed to learn the aliasing artifacts. The residual learning algorithm took only 30-40ms with significantly better reconstruction performance compared to GRAPPA and the state-of-the-art compressed sensing algorithm, ALOHA.
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