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

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.