Keywords: Image Reconstruction, Brain, Compressed sensing MRIGenerative adversarial network (GAN) has emerged as one of the most prominent approaches for fast CS-MRI reconstruction. However, most deep-learning models achieve performance by increasing the depth and width of the networks, leading to prolonged reconstruction time and difficulty to train. We have developed an improved GAN-based model to achieve quality performance without increasing complexity by implementing the following: 1) dilated-residual structure with different dilation rates at different depth of the networks; 2) CAM to adjust the allocation of network resources; 3) multi-scale information fusion module to achieve feature fusion. Experiment data have confirmed the validity for the modules.
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