Keywords: Machine Learning/Artificial Intelligence, Image ReconstructionRecently, iterative algorithm driven deep neural network unfolding methods have been successfully applied to MRI. However, the network replaces the original algorithm structure and mathematical properties such as interpretability and convergence of the original algorithm are not guaranteed. Fortunately, the k-space-filled Hankel low rank can naturally be associated with convolutional networks. Given this, we propose an unfolding method for k-space-filling, which guarantees convergence to the unique real MR image. Furthermore, we train this network in a self-supervised manner to cope with scenarios where fully sampled data are difficult to obtain. Finally, numerical experiments validate the effectiveness of the proposed method.
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