We propose a deep learning approach for MR fingerprinting that jointly learns a low-dimensional representation of the fingerprints and estimates biophysical parameters from this subspace. In contrast to SVD-based projections, which are agnostic to the estimation task, the learned subspace is optimized to maximize information content about the parameters of interest. Incorporating the learned basis functions in the forward imaging operator suppresses undersampling artifacts and increases computational efficiency.
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