In this work, we propose a novel deep learning-based framework DEMO for fast and robust MR parametric mapping. Different from current deep learning-based methods, DEMO trains the network in an unsupervised way. Specifically, a CS-based loss function is used in DEMO to avoid the necessity of using fully sampled k-space data as the label, and thus make it an unsupervised learning approach. DEMO reconstructs the parametric weighted images and generates the parametric map simultaneously, which enables multi-tasking learning. Experimental results show the promising performance of the proposed DEMO framework in quantitative MR T1ρ mapping.
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