Keywords: Heart, Machine Learning/Artificial Intelligence, ReconstructionThe main focus of this work is to introduce a deep generative model for simultaneous free-breathing cardiac $$$T_1$$$ mapping and CINE MRI. The data is acquired by a gradient echo inversion recovery sequence with intermittent delays for magnetization recovery. The joint reconstruction of the image time-series is performed using a patient-specific deep manifold reconstruction algorithm which learns a CNN generative model and its latent vectors from the measured k-t space data in an unsupervised fashion. Following learning, the model can be used to generate synthetic images at specific motion and contrast states.
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