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Abstract #3591

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

Salman Ul Hassan Dar1,2, Mahmut Yurt1,2, Muzaffer Özbey1,2, and Tolga Çukur1,2,3
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.

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