Abstract #3591

Hybrid Deep Neural Network Architectures for Multi-Coil MR Image Reconstruction

Salman Ul Hassan Dar^1,2, Mahmut Yurt^1,2, Muzaffer Özbey^1,2, and Tolga Çukur^1,2,3

¹Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey, ²National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara, Turkey, ³Neuroscience Program, Aysel Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey

Two main frameworks for reconstruction of undersampled MR acquisitions are compressive sensing/parallel imaging methods (CS-PI) and deep neural networks (DNNs). CS-PI relies on sparsity in fixed transform domains and requires careful hyperparameter tuning. On the other hand, DNNs for multi-coil reconstructions can be difficult to train due to increased model complexity. To address these limitations, we propose a DNN-PI hybrid in which DNNs that learn population-driven priors are combined with PI that learns subject-specific priors. Evaluations on T1/T2-weighted brain images demonstrate the improved immunity of DNN-PI to scarce training data and suboptimal hyperparameter selection.

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.

Click here for more information on becoming a member.