Most of the unrolling-based deep learning fast MR imaging methods learn the parameters and regularization functions with the network architecture structured by the corresponding optimization algorithm. In this work, we introduce an effective strategy, VIOLIN and use the primal dual hybrid gradient (PDHG) algorithm as an example to demonstrate improved performance of the unrolled networks via breaking the variable combinations in the algorithm. Experiments on in vivo MR data demonstrate that the proposed strategy achieves superior reconstructions from highly undersampled k-space data.
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