Keywords: Sparse & Low-Rank Models, Quantitative ImagingSubspace or low-rank models have been demonstrated useful in producing efficient representations and reconstructions for high dimensional imaging problems. In this study, we propose an unsupervised learning-based approach by generalizing the subspace model using a deep generative network. The generative subspace model can then be incorporated into the physics-based reconstruction formalism. The network parameters can be self-trained by minimizing the cost function with the flexibility to integrate with conventional constraints. We demonstrated the effectiveness of the proposed method over standard linear subspace and deep image prior models using in vivo T2 mapping dataset.
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