A physics-based deep learning reconstruction is proposed for cardiac MRF that is based on the deep image prior framework, which employs self-supervised training and does not require additional in vivo training data. Calculation of the forward model is accelerated using a neural network to rapidly output MRF signal timecourses (in place of a Bloch simulation) and low-rank modeling to reduce the number of NUFFT operations. The proposed reconstruction enables cardiac T1/T2 mapping during a shortened 5-heartbeat breathhold and 150ms acquisition window.
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