Keywords: Parallel Imaging, Machine Learning/Artificial Intelligence, Noise-robust methodWe propose a novel loss function that increases noise-robustness in accelerated parallel MR image reconstruction. The loss function is based on the variance of the background area in the noisy undersampled image and that of the difference image between noisy undersampled image and the synthesized undersampled image. The proposed loss function provides stronger regularization and robustness when applied along with other constraints. We show that the application of the proposed loss function boosts performance of the network, yielding improved quality of reconstructed image.
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