Keywords: MR Fingerprinting/Synthetic MR, Image ReconstructionWe propose a novel, deep learning-based method, “SuperMRF”, for the reconstruction of MR Fingerprinting (MRF) parametric maps that enables rapid image reconstruction. Built upon a convolutional neural network, SuperMRF uses three loss functions to incorporate additional information from the Bloch equations, estimated maps, de-aliasing, and data consistency losses. We investigate the use of SuperMRF for further acceleration of data acquisition by reducing the number of MRF time frames. Our results demonstrate that proposed SuperMRF is robust to noise and can achieve a 20x reduction in acquired MRF time frames. Tissue property maps can be reconstructed in less than one second.
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