The state-of-the-art methods in accelerating dynamic Magnetic Resonance (dMR) Imaging rely on sparse and/or low-rank priors. We propose a novel manifold driven framework that exploits the manifold smoothness priors to highly accelerate data acquisition in dMR. We postulate that images in dMR lie on or close to a smooth manifold and learn the manifold geometry from the navigator signals. Capitalizing on the learned manifold, we develop two regularization loss functions and subsequently build a framework to reconstruct dMR images from highly undersampled k-space data. The proposed method is shown to be superior than competitive methods in different data sets.
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