A new approach to auto-calibrating, coil-by-coil parallel imaging reconstruction is presented. It is a generalized reconstruction framework based on deep learning. A neural network consisting of three Dense layer (Fully connected layer) units, an RNN layer and an output Dense unit is designed and trained to identify the mapping relationship between the zero-filled and fully-sampled k-space data. The training process could be separated into two steps: pre-training and fine-tuning. Results show our proposed model could be robust to arbitrary undersampling patterns in k-space and shows a higher structural similarity index compared with traiditional k-space based methods.
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