Keywords: Machine Learning/Artificial Intelligence, Image ReconstructionExisting deep learning-based methods for MR reconstruction mainly use MSE as loss function to train the network under the assumption that MR images follow the sub-Gaussian distribution, without considering the real distribution of the images. In this work, we propose a new DL-based method that models the image distribution with equilibrium Langevin dynamic to converge the distribution, and trains the network with Wasserstein distance to approach the real distribution. Experimental results on highly undersampled MR data demonstrate the superior performance 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